ABSTRACT
Abstract
A wearable device including a sensor system for Manual Wheelchair Users (MWCUs) to provide an alert to the MWCUs when there is an increased risk of injury is provided. The wearable device includes sensors measuring the physical activity of the MWCU and transmitting it as user data to a user motion dynamics analysis system on a user device. When the user motion dynamics analysis system determines that there is a risk of injury, it transmits an alert to the wearable device for display to the user.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims the benefit of U.S. Provisional Application No. 62/303,057 filed Mar. 3, 2016, entitled âCloud Based Preventative Healthcare Wearable Device and System for Repetitive Strain Injuriesâ, which is hereby incorporated by reference in its entirety.
BACKGROUND OF THE INVENTION
The present invention generally relates to healthcare monitoring devices. More particularly, the present invention relates to a wearable, network connected healthcare monitoring system for monitoring injury or activity.
Repetitive tasks that are performed in many aspects of daily life may lead to strain and/or stress injuries. Such injuries frequently occur in a population such as individuals using assistive devices such as walkers, wheelchairs for example, and/or individuals engaged in occupational situations such as repeated load carriage, repeated gripping tasks, etc.
One example of an assistive device in which requires repeated physical effort on the part of a user is a wheelchair that is manually operated. Such wheelchair users must effectively learn to walk with their hands. Over 3 million manual wheelchair users (MWCUs) in the U.S. face this reality every day. Research shows that about 70% of MWCUs (approximately 2.1 million in U.S. alone) experience debilitating shoulder injuries within the first 12 months of using a manual wheelchair due to repetitive propulsion. Shoulder pain in MWCUs negatively impacts their quality and independence of living. Moreover, this pain increases their disability and health care costs.
Studies have shown that clinical training of MWCUs on proper propulsion technique and providing continuous feedback on their propulsion technique reduces the risk of shoulder injury. However such training is not widely accessible to MWCU and clinicians.
FIG. 1 illustrates the current status of such propulsion technique training. As shown in FIG. 1 , today the MWCU must leave their home and travel to a clinical and/or research facility. At the facility, the MWCU demonstrates their usage of the wheelchair for a clinician and the clinician evaluates their usage of the wheelchair. This evaluation typically required the MWCU is use a specialized device or equipment with force sensing instrumented wheels. Such equipment typically costs in excess of U.S. $40,000. Additionally, MWCU access to the facility may be constrained because there are only a limited number of available professionals with the capacity to provide such analysis and training. Also, MWCUs face various barriers such as arranging transportation and the cost of transportation that may reduce their ability to visit specialized clinics to receive training. Consequently, many MWCUs do not receive this analysis and training, or may not receive it in a timely manner. This in turn exposes the MWCUs to higher injury risk and higher health care costs, especially due to the cost of having to treat additional stress and/or strain injuries that might arise.
Additionally, such clinical training may not capture the daily reality of the MWCU's physical activity because the clinical evaluation is an artificial situation wherein the MWCU knows that their physical activity is being evaluated and consequently is seeking to emulate the most proper technique for the clinician. Consequently, the clinician's ability to analyze and give feedback with regard to the MWCU's typically technique may be limited. Additionally, the clinical evaluation typically lasts for only a finite amount of time and consequently may not allow the clinician an opportunity to accurately evaluate the physical motion of the MWCU when the MWCU is fatigued and may be more susceptible to injury.
BRIEF SUMMARY OF THE INVENTION
One or more of the embodiments of the present invention provide a wearable device including a sensor system for Manual Wheelchair Users (MWCUs) and a feedback system wherein said sensor system senses user sensor data and transmits it to a user motion dynamics analysis system on a user mobile device. The user sensor data is used along with stored information including customized desired propulsion metrics by the user motion dynamics analysis system to determine a risk prediction for an injury. When the user motion dynamics analysis system determines that there is a risk of injury, an alert instruction is transmitted from the user device to the wearable device and cause the actuation of feedback system in the wearable device that is observable by the user. Although in one embodiment, the invention may be used for a MWCUs, other embodiments may be employed for other applications such as training and tracking for assistive devices such as walkers, crutches, etc and training and tracking of any gripping task.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates the current status of Manual Wheelchair User (MWCU) propulsion technique training.
FIG. 2 illustrates a simplified version of a wearable user motion dynamics analysis system according to an embodiment of the present invention.
FIG. 3 illustrates a first embodiment of a wearable device as discussed in FIG. 2 .
FIG. 4 illustrates a second embodiment of a wearable device as discussed in FIG. 2 .
FIG. 5 illustrates an embodiment of the wearable device embodied as a glove.
FIG. 6 illustrates an alternate embodiment of the wearable device embodied as a wristband having a connection to receive readings from an external Force Sensing Resistor (FSR).
FIG. 7 illustrates an embodiment of the user mobile device.
FIG. 8 illustrates the data process flow of the wearable user motion dynamics analysis system of FIG. 2 .
FIG. 9 illustrates a flowchart of a low power usage method for the wearable device.
FIG. 10 illustrates the data flow of the calculation of the injury risk score and biofeedback according to an embodiment of the present invention.
FIGS. 11A-11E illustrate various embodiments of the report display interfaces.
FIG. 12 illustrates a cloud-integrated version according to an embodiment of the present invention.
FIG. 13 illustrates the data flow of a cloud-integrated version of the wearable user motion analysis.
FIGS. 14A and 14B illustrate an example of the calculation of the user demographics specific risk exposure score.
FIG. 15 illustrates an example of the calculation of the user perceived pain exposure score.
FIG. 16 illustrates an example of the calculation of the user perceived quality of life score.
FIGS. 17A to 17D illustrate an example of the calculation of the wheelchair propulsion score.
FIGS. 18A and 18B illustrate an example of the calculation of the transfer score.
DETAILED DESCRIPTION OF THE INVENTION
Various remote monitoring healthcare systems exist today. Integration with remote patient information, historical data, and detailed individual bioinformatic data may increase awareness in the user, as well as provide a desirable component to the healthcare system provided by clinicians. Analysis of patient data may be supplemented with remotely acquired data. The facilitation of providing training materials recommended by the healthcare provider may lead to a reduced risk of patient injury through early warning detection. Patients benefit from remote systems through the reduction in required transportation, time spent at clinics, and money spent on insurance copays.
To address the problems mentioned above, which are of significant public health importance, one or more embodiments of the present invention provide a local and/or a cloud-integrated wearable technology that improves the quality of life and/or efficacy of MWCUs by providing easier access (home-based access as opposed to clinical access) to wheelchair propulsion training and performance feedback, including an injury-risk information derived from learning sensor data. All these are provided using a mobile device. Using the cloud-integrated functionality, individuals may share their performance data with their healthcare providers, family, trainers, and/or coach. The healthcare providers, family members, trainers, and/or coach may monitor their client's performance from a remote location and provide customized feedback, training, and/or alert recommendations. The system may continuously track the arm usage and may acts as a decision support system to for preventive healthcare as further discussed below.
Currently, for home based rehabilitation training and/or monitoring such as for wheelchair users, there exists no way to remotely send and receive training information gathered from wearable devices along with healthcare-provided customized training feedback based on extracted information from sensor data. No current glove embedded wearable technologies for manual wheelchair users use sensor data from wearable device information in predicting injury risk by providing a risk-predicting assessment to help prevent and delay the onset of upper appendage repetitive stress and/or strain injuries.
As further described below, one or more embodiments of the present invention provide a local and/or cloud-based wearable technology that reduces the risk of shoulder injury in MWCUs by providing access to affordable home-based propulsion/rehabilitation training and feedback. One embodiment of the present invention provides a pair of gloves with embedded sensors, including, but not limited to, force sensors and acceleration sensors and gyroscopes, capable of accurately measuring and securely transmitting real-time sensor data to a mobile phone application, pc application, or tablet application. While a user is propelling his/her wheelchair wearing the gloves, sensor data from the gloves is collected and automatically sent wirelessly to the user motion and force analysis system on the user's mobile device, computer, or tablet. Based on the received sensor data, the user motion and force analysis system also estimates customized injury-risk information, and provides bio-feedback to the user on their day-to-day assistive device use/push pattern in order to help maintain proper propulsion technique. The bio-feedback includes, but is not limited to, display of summary information on the users mobile device screen, sensory feedback (auditory/vibratory), text/voice/video data exchange and email notification. The bio-feedback may be a closed loop, wherein the user may receive, share and/or send the information using the cloud for efficient healthcare. The wheelchair propulsion and injury risk metrics are computed using measurements from the sensor data, including the number of pushes, accelerometry (movement), inertial measurements (movement and orientation), push time, activity rate, and peak force during different activities of daily living in manual wheelchair users.
FIG. 2 illustrates a simplified version of a wearable user motion dynamics analysis system 200 according to an embodiment of the present invention. As shown in FIG. 2 , the user motion dynamics analysis system 200 includes a wearable device 210 having sensor system 230 and a feedback system 250 and a user mobile device 220 including a user motion dynamics analysis system 240 .
In operation, the wearable device 210 may be positioned on a user and the sensor system 230 may detect the motion and grip force of the user during wheelchair use and relay it as user motion and force data to the user motion dynamics analysis system 240 at the user mobile device 220 . The user mobile device 220 may for example be any of a smartphone or mobile phone, computer, smart watch, or tablet device.
As further described below, the user motion dynamics analysis system 240 uses a complex computerized technique to analyze the received user motion and force data and detect when the user motion and force data departs from a predetermined and/or dynamically determined desired set of parameters. If such a deviation is detected, the user motion dynamics analysis system 240 transmits alert data to the feedback system 250 . In response, the feedback system 250 may display an alert and/or alarm to the user to that the user will be aware that their motion and force deviates from desired parameters. Preferably, the user will then adjust their motion and force to conform with desired parameters in order to reduce the risk of injury.
FIG. 3 illustrates a first embodiment of a wearable device 310 as discussed in FIG. 2 . As shown in FIG. 3 , the wearable device 310 includes a power source 315 , a voltage regulator 320 , a microcontroller</figure-c
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims the benefit of U.S. Provisional Application No. 62/303,057 filed Mar. 3, 2016, entitled âCloud Based Preventative Healthcare Wearable Device and System for Repetitive Strain Injuriesâ, which is hereby incorporated by reference in its entirety.
BACKGROUND OF THE INVENTION
The present invention generally relates to healthcare monitoring devices. More particularly, the present invention relates to a wearable, network connected healthcare monitoring system for monitoring injury or activity.
Repetitive tasks that are performed in many aspects of daily life may lead to strain and/or stress injuries. Such injuries frequently occur in a population such as individuals using assistive devices such as walkers, wheelchairs for example, and/or individuals engaged in occupational situations such as repeated load carriage, repeated gripping tasks, etc.
One example of an assistive device in which requires repeated physical effort on the part of a user is a wheelchair that is manually operated. Such wheelchair users must effectively learn to walk with their hands. Over 3 million manual wheelchair users (MWCUs) in the U.S. face this reality every day. Research shows that about 70% of MWCUs (approximately 2.1 million in U.S. alone) experience debilitating shoulder injuries within the first 12 months of using a manual wheelchair due to repetitive propulsion. Shoulder pain in MWCUs negatively impacts their quality and independence of living. Moreover, this pain increases their disability and health care costs.
Studies have shown that clinical training of MWCUs on proper propulsion technique and providing continuous feedback on their propulsion technique reduces the risk of shoulder injury. However such training is not widely accessible to MWCU and clinicians.
FIG. 1 illustrates the current status of such propulsion technique training. As shown in FIG. 1 , today the MWCU must leave their home and travel to a clinical and/or research facility. At the facility, the MWCU demonstrates their usage of the wheelchair for a clinician and the clinician evaluates their usage of the wheelchair. This evaluation typically required the MWCU is use a specialized device or equipment with force sensing instrumented wheels. Such equipment typically costs in excess of U.S. $40,000. Additionally, MWCU access to the facility may be constrained because there are only a limited number of available professionals with the capacity to provide such analysis and training. Also, MWCUs face various barriers such as arranging transportation and the cost of transportation that may reduce their ability to visit specialized clinics to receive training. Consequently, many MWCUs do not receive this analysis and training, or may not receive it in a timely manner. This in turn exposes the MWCUs to higher injury risk and higher health care costs, especially due to the cost of having to treat additional stress and/or strain injuries that might arise.
Additionally, such clinical training may not capture the daily reality of the MWCU's physical activity because the clinical evaluation is an artificial situation wherein the MWCU knows that their physical activity is being evaluated and consequently is seeking to emulate the most proper technique for the clinician. Consequently, the clinician's ability to analyze and give feedback with regard to the MWCU's typically technique may be limited. Additionally, the clinical evaluation typically lasts for only a finite amount of time and consequently may not allow the clinician an opportunity to accurately evaluate the physical motion of the MWCU when the MWCU is fatigued and may be more susceptible to injury.
BRIEF SUMMARY OF THE INVENTION
One or more of the embodiments of the present invention provide a wearable device including a sensor system for Manual Wheelchair Users (MWCUs) and a feedback system wherein said sensor system senses user sensor data and transmits it to a user motion dynamics analysis system on a user mobile device. The user sensor data is used along with stored information including customized desired propulsion metrics by the user motion dynamics analysis system to determine a risk prediction for an injury. When the user motion dynamics analysis system determines that there is a risk of injury, an alert instruction is transmitted from the user device to the wearable device and cause the actuation of feedback system in the wearable device that is observable by the user. Although in one embodiment, the invention may be used for a MWCUs, other embodiments may be employed for other applications such as training and tracking for assistive devices such as walkers, crutches, etc and training and tracking of any gripping task.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates the current status of Manual Wheelchair User (MWCU) propulsion technique training.
FIG. 2 illustrates a simplified version of a wearable user motion dynamics analysis system according to an embodiment of the present invention.
FIG. 3 illustrates a first embodiment of a wearable device as discussed in FIG. 2 .
FIG. 4 illustrates a second embodiment of a wearable device as discussed in FIG. 2 .
FIG. 5 illustrates an embodiment of the wearable device embodied as a glove.
FIG. 6 illustrates an alternate embodiment of the wearable device embodied as a wristband having a connection to receive readings from an external Force Sensing Resistor (FSR).
FIG. 7 illustrates an embodiment of the user mobile device.
FIG. 8 illustrates the data process flow of the wearable user motion dynamics analysis system of FIG. 2 .
FIG. 9 illustrates a flowchart of a low power usage method for the wearable device.
FIG. 10 illustrates the data flow of the calculation of the injury risk score and biofeedback according to an embodiment of the present invention.
FIGS. 11A-11E illustrate various embodiments of the report display interfaces.
FIG. 12 illustrates a cloud-integrated version according to an embodiment of the present invention.
FIG. 13 illustrates the data flow of a cloud-integrated version of the wearable user motion analysis.
FIGS. 14A and 14B illustrate an example of the calculation of the user demographics specific risk exposure score.
FIG. 15 illustrates an example of the calculation of the user perceived pain exposure score.
FIG. 16 illustrates an example of the calculation of the user perceived quality of life score.
FIGS. 17A to 17D illustrate an example of the calculation of the wheelchair propulsion score.
FIGS. 18A and 18B illustrate an example of the calculation of the transfer score.
DETAILED DESCRIPTION OF THE INVENTION
Various remote monitoring healthcare systems exist today. Integration with remote patient information, historical data, and detailed individual bioinformatic data may increase awareness in the user, as well as provide a desirable component to the healthcare system provided by clinicians. Analysis of patient data may be supplemented with remotely acquired data. The facilitation of providing training materials recommended by the healthcare provider may lead to a reduced risk of patient injury through early warning detection. Patients benefit from remote systems through the reduction in required transportation, time spent at clinics, and money spent on insurance copays.
To address the problems mentioned above, which are of significant public health importance, one or more embodiments of the present invention provide a local and/or a cloud-integrated wearable technology that improves the quality of life and/or efficacy of MWCUs by providing easier access (home-based access as opposed to clinical access) to wheelchair propulsion training and performance feedback, including an injury-risk information derived from learning sensor data. All these are provided using a mobile device. Using the cloud-integrated functionality, individuals may share their performance data with their healthcare providers, family, trainers, and/or coach. The healthcare providers, family members, trainers, and/or coach may monitor their client's performance from a remote location and provide customized feedback, training, and/or alert recommendations. The system may continuously track the arm usage and may acts as a decision support system to for preventive healthcare as further discussed below.
Currently, for home based rehabilitation training and/or monitoring such as for wheelchair users, there exists no way to remotely send and receive training information gathered from wearable devices along with healthcare-provided customized training feedback based on extracted information from sensor data. No current glove embedded wearable technologies for manual wheelchair users use sensor data from wearable device information in predicting injury risk by providing a risk-predicting assessment to help prevent and delay the onset of upper appendage repetitive stress and/or strain injuries.
As further described below, one or more embodiments of the present invention provide a local and/or cloud-based wearable technology that reduces the risk of shoulder injury in MWCUs by providing access to affordable home-based propulsion/rehabilitation training and feedback. One embodiment of the present invention provides a pair of gloves with embedded sensors, including, but not limited to, force sensors and acceleration sensors and gyroscopes, capable of accurately measuring and securely transmitting real-time sensor data to a mobile phone application, pc application, or tablet application. While a user is propelling his/her wheelchair wearing the gloves, sensor data from the gloves is collected and automatically sent wirelessly to the user motion and force analysis system on the user's mobile device, computer, or tablet. Based on the received sensor data, the user motion and force analysis system also estimates customized injury-risk information, and provides bio-feedback to the user on their day-to-day assistive device use/push pattern in order to help maintain proper propulsion technique. The bio-feedback includes, but is not limited to, display of summary information on the users mobile device screen, sensory feedback (auditory/vibratory), text/voice/video data exchange and email notification. The bio-feedback may be a closed loop, wherein the user may receive, share and/or send the information using the cloud for efficient healthcare. The wheelchair propulsion and injury risk metrics are computed using measurements from the sensor data, including the number of pushes, accelerometry (movement), inertial measurements (movement and orientation), push time, activity rate, and peak force during different activities of daily living in manual wheelchair users.
FIG. 2 illustrates a simplified version of a wearable user motion dynamics analysis system 200 according to an embodiment of the present invention. As shown in FIG. 2 , the user motion dynamics analysis system 200 includes a wearable device 210 having sensor system 230 and a feedback system 250 and a user mobile device 220 including a user motion dynamics analysis system 240 .
In operation, the wearable device 210 may be positioned on a user and the sensor system 230 may detect the motion and grip force of the user during wheelchair use and relay it as user motion and force data to the user motion dynamics analysis system 240 at the user mobile device 220 . The user mobile device 220 may for example be any of a smartphone or mobile phone, computer, smart watch, or tablet device.
As further described below, the user motion dynamics analysis system 240 uses a complex computerized technique to analyze the received user motion and force data and detect when the user motion and force data departs from a predetermined and/or dynamically determined desired set of parameters. If such a deviation is detected, the user motion dynamics analysis system 240 transmits alert data to the feedback system 250 . In response, the feedback system 250 may display an alert and/or alarm to the user to that the user will be aware that their motion and force deviates from desired parameters. Preferably, the user will then adjust their motion and force to conform with desired parameters in order to reduce the risk of injury.
FIG. 3 illustrates a first embodiment of a wearable device 310 as discussed in FIG. 2 . As shown in FIG. 3 , the wearable device 310 includes a power source 315 , a voltage regulator 320 , a microcontroller 325 , a user sensor system 330 , a memory buffer 340 , an memory system 345 such as a microSD card and associated connector, a feedback system 350 such as a biofeedback buzzer and/or vibration element, and a Bluetooth module 360 , such as a Bluetooth Low Energy module. The user sensor system 330 includes one or more force sensing resistors 332 and Microelectromechanical systems (MEMS) 335 that include a gyroscope 337 and accelerometer 338 .
In operation, power is supplied to the wearable device 310 by the power source 315 and regulated by the voltage regulator 320 . The user sensor system 330 detects user data from the force sensing resistors 332 , gyroscope 337 , and accelerometer 338 . The user data is then passed to the microcontroller 325 . The microcontroller 325 directs the user data into the memory buffer 340 where it is buffered while being written to the memory system 345 . Additionally, the microcontroller 325 may cause the user data to be passed to the Bluetooth module 360 for communication to the user motion dynamics analysis system 240 of the user mobile device 220 as shown in FIG. 2 .
As mentioned in FIG. 2 , if the user motion dynamics analysis system 240 determines that the user data departs from the predetermined and/or dynamically determined desired set of parameters, the user motion dynamics analysis system 240 transmits alert data. The alert data is received by the Bluetooth module 360 and relayed to the microcontroller 325 . The microcontroller 325 received the alert data and activates the feedback system 350 in response to the alert data.
As further discussed below, the feedback system 350 may provide several alert options. For example, the feedback system 350 may include a vibration element and the vibration element may be optionally controlled to any of several available levels of vibration. In this embodiment, the alert data may specify the level of vibration to be delivered by the feedback system 350 . Similarly, when the feedback system 350 includes an audible component such as a buzzer, the alert data may specify the volume of the audible component. Additionally, when the feedback system 350 includes multiple display elements such as a buzzer and a vibration element, the alert data may specify which of the display elements is to be activated by the microcontroller 325 , including optional activation of one or the other of the display elements and/or simultaneous display of more than one of the display elements.
In one embodiment, all of the components of the wearable device other than the power source may be located on a single Printed Circuit Board (PCB).
In one embodiment, an ARM microcontroller is used. Communication with a micro SD card module, in the form of a component of the PCB, is managed by the microcontroller using an SPI bus. Sensor data is received and converted using an on-chip analog to digital converter. The sensor data is saved directly onto the microSD card, as well as transmitted wirelessly, using a separate low power Bluetooth module that communicates using the SPI protocol. One specific embodiment of this design utilizes an ARM ATMEGA328p-au microcontroller, an EEPROM memory buffer, an nRF8001 low-power Bluetooth IC module capable of wirelessly sending and receiving data, and JST-XH connectors to attach and detach force-sensitive resistors and power sources.
Recharging of the power source 315 such as a battery is accomplished using a mini USB connector and a standard battery management charge controller such as a Microchip MCP73831T. This provides a system for charging the device at any time, including during operation by the user. In various embodiments where an on-board storage device is employed, a real time clock is included on the PCB along with a backup coin cell battery in order to maintain accurate timestamp readings for data logging purposes.
FIG. 4 illustrates a second embodiment of a wearable device 410 as discussed in FIG. 2 . Similar to the embodiment shown in FIG. 3 , as shown in FIG. 4 , the wearable device 410 includes a power source 415 , a voltage regulator 420 , a System On a Chip (SoC) 425 , a user sensor system 430 , a memory buffer 440 , an memory system 445 such as a microSD card and associated connector, and a feedback system 450 such as a biofeedback buzzer and/or vibration element. The user sensor system 430 includes one or more force sensing resistors 432 and Microelectromechanical systems (MEMS) 435 that include a gyroscope 437 and accelerometer 438 .
In operation, the wearable device 410 of FIG. 4 operates similarly to the wearable device 310 of FIG. 3 , but employs the SoC 425 instead of the microcontroller 325 and Bluetooth module 360 of FIG. 3 . The SoC 425 includes a processor that operates similarly to the microcontroller 325 of FIG. 3 and a wireless communication system such as a Bluetooth 4.0 transceiver that operates similarly to the Bluetooth module 360 of FIG. 3 as described above.
In one embodiment, a low-power SoC microcontroller unit is used for the main processing module in the PCB. A built-in analog to digital converter of the SoC processes the sensor data from the device. Wireless communication with a mobile device, tablet, computer or other such device is achieved using the Bluetooth 4.0 transceiver, which is built into the SoC. In one embodiment, the SoC is a Nordic nRF51422. In a separate embodiment, the SoC is a Dialog Semiconductor NRF51422. In yet another embodiment, the SoC is a NXP Semiconductors QN902X. In one or more of the embodiments described above, I2C and SPI busses are used to communicate with on-board peripherals, including the micro SD card module.
FIG. 5 illustrates an embodiment of the wearable device 500 embodied as a glove 505 . As shown in FIG. 5 , the wearable device 500 includes a glove 505 , having an attached battery 515 , and a Printed Circuit Board (PCB) 520 including all of the remaining components shown in the embodiment of the wearable device of FIG. 3 other than the Force Sensitive Resistors (FSRs). The FSRs are positioned in the palm of the glove and connected to the PCB 520 with two wires 533 .
In one embodiment, the wearable device is embedded with a PCB, including a microcontroller, wireless transmission module, memory buffer, on-board MEMS sensors, a microSD connector with a microSD card, biofeedback mechanisms, connectors for external sensors (including Force Sensitive Resistors or load sensors), a voltage regulator, and a power source. In one specific embodiment, a pair of gloves are embedded with a PCB attached to the back of the glove using an adhesive material, such that the PCB is located on the back of the user's palm, below the fingers, centered on the back of the palm as shown. Wires running around the side of the hand connect to Force Sensitive Resistor elements attached to the palm of the glove. Also attached to the back of the glove is a housing with the PCB and the rechargeable battery.
The Printed Circuit Board (PCB) is capable of sensor data collection and wireless data transmission, and includes sensors including force, acceleration, gyroscope and GPS sensors, capable of accurately measuring and securely transmitting real-time wheelchair propulsion technique data to a user mobile device such as a mobile phone, computer, smart watch, or tablet device.
Sensors embedded in the wearable device provides access to measuring various wheelchair propulsion parameters without impairing the hand mobility or comfort of the user. The wearable device automatically collects, processes, locally stores the data in an on-board storage unit (such as a microSD card), and transmits the locally stored data wirelessly to a separate device. Reports of propulsion parameter summaries obtained by the wearable device may be displayed in a separate medium (pc, tablet or phone) and may be used to continuously analyze the deviation from the user's preferred propulsion technique, increasing the ability to detect the onset of potential injuries.
In one embodiment, the battery may be a rechargeable 110 mAh 3.3 volt Polymer Lithium Ion Battery and it may be located on the back of the glove along with the PCB. It may be held inside an ABS plastic enclosure, which is attached to the glove with adhesive material. The battery is attached to the PCB using a JST-XH connector. Ultra low power consumption is achieved through the use of low-power peripherals combined with a power-saving system as further described below.
FIG. 6 illustrates an alternate embodiment of the wearable device 600 embodied as a wristband 605 having a connection to receive readings from an external Force Sensing Resistor (FSR). As shown in FIG. 6 , the wearable device 600 includes a wristband 605 , having an attached battery 615 , and a Printed Circuit Board (PCB) 620 including all of the remaining components shown in the embodiment of the wearable device of FIG. 3 other than the Force Sensitive Resistors (FSRs). The FSRs 632 are positioned in the palm of the user and connected to the PCB 620 using a data connection such as a JST- XH connector 633 to attach and detach the FSR 632 . The FSR 632 may also be positioned attached to or embedded in a glove or may be held in or attached to the hand of the user.
With regard to the positioning of the FSRs in one or more of the embodiments herein, FSRs may be attached to the palm of the glove with an adhesive material or may be tailored or stitched on to desired location in order to measure and detect the force applied by the user's hand onto the wheel of the wheelchair. In one embodiment, the sensors may be stitched on to the inner surface of the glove that sits on the user's palm region. In this embodiment, the sensor may not be visible from the exterior of the glove. The FSRs are easily attached and detached from the PCB using a JST-XH series of connectors, allowing the PCB to be located at various locations.
In one embodiment, the PCB is located on the back of the glove as seen in FIG. 5 . In a separate embodiment, the PCB is located on a wristband, as seen in FIG. 6 . The leads of the FSR may be directly soldered onto the PCB.
The sensor placement on the glove is customizable in multiple ways. Below are various embodiments for the positioning of the FSR. One embodiment of the includes a pair of gloves embedded with one force-sensitive resistor with a sensing area of 1.5â³Ã1.5â³. This force-sensitive resistor is located on the palm, beneath the fingers such that it is positioned directly above the volar palm of the user when the glove is worn. In a separate embodiment, five circular diameter FSR's (whose diameter may be between 0.2â³ to 1.5â³ diameter) are attached to the glove directly beneath the fingers such that each FSR is located directly above ametacarpal head located on the user's hand. In yet another embodiment, up to fourteen small 0.6â³ circular FSRs are mounted on the front of the glove (inner surface of the glove which is in contact with the palm when the glove is worn), each located in a different location, corresponding to the following regions of the hand:
1. Palm below the second, third, fourth and fifth fingers
2. Palm below the thumb
3. Volar palm
4. Heel of the hand
5. First metacarpal head
6. Second metacarpal head
7. Third metacarpal head
8. Fourth metacarpal head
9. Fifth metacarpal head
10. First distal phalanx
11. Second distal phalanx
12. Third distal phalanx
13. Fourth distal phalanx
14. Fifth distal phalanx
In one embodiment, the motion of the hand is measured by a MEMS accelerometer and gyroscope designed for low power consumption. In one embodiment, an Analog Devices ADXL377 triple axis accelerometer is integrated into the PCB in order to measure hand motion acceleration parameters, which, in one embodiment, is embedded onto the rear of the glove, located above the center of the opisthenar area of the user's hand. In a separate embodiment, the MEMS accelerometer and gyroscope is mounted onto a PCB, which is located on a wristband, worn on the wrist by the user. In this embodiment, the force sensors (attached to the glove) connect to the PCB using JST-XH connectors. Both embodiments mentioned above may additionally support multiple types and combinations of accelerometers and gyroscopes. For example, an exemplary embodiment of the present invention utilizes a Bosch BNO055 Intelligent 9-Axis sensor, in replacement of the ADXL377 device. In an additional embodiment, an InvenSense MPU-6000 motion-processing unit is used to measure gyroscopic and accelerometer data. In yet another embodiment, a MPU-6050 unit is used in both methods described above, located on either the opisthenar area of the hand or on the wrist.
A separate embodiment utilizes a MEMS gyroscope and accelerometer that are mounted on the user's shoulder using an elastic band worn directly on the shoulder. The sensors are coupled with JST-XH connectors, which have wires running down the length of the arm are used to connect the MEMS sensors to the PCB.
In one embodiment, third party Bluetooth devices are integrated into this system using a Bluetooth 4.0 communication protocol. For example, one embodiment of the invention uses a GoSafe TPMS 500BT Bluetooth air pressure-monitoring device to measure the tire pressure of the wheelchair. The air pressure data may then be relayed through the Bluetooth system to the User mobile device and used by the user motion dynamics analysis system.
FIG. 7 illustrates an embodiment of the user mobile device 720 . As shown in FIG. 7 the user mobile device 710 includes a Bluetooth transceiver 710 , a clock 730 , a user motion dynamics analysis system 740 including a processor 742 and a memory 744 , a Graphical User Interface (GUI) 750 , and a cloud communication system 760 .
In operation, sensor data is received from the wearable device at the Bluetooth transceiver 710 and alert data is transmitted to the wearable device using to Bluetooth transceiver 710 . The Bluetooth transceiver 710 communicates with the processor 742 of the user motion dynamics analysis system 740 which performs the actual analysis of the user/sensor data as described herein. Also, as the processor 742 receives the sensor data it retrieves a timestamp from the clock 730 , associates the timestamp with the sensor data, and saves the sensor data and associated timestamp in the memory 744 .
The GUI 750 may be used to receive input commands or data from the user, for example entering additional user data to be received by the processor 742 and stored in the memory 744 or for receiving a command to retrieve specific sensor readings from the memory 744 and display them on the GUI 750 .
Additionally, as further described below, the cloud communication system 760 may be used to communicated between the user motion dynamics analysis system 740 and a remote cloud server.
In embodiments where data transfer from the wearable device to the user's mobile phone, tablet, smart-watch, computer, or digital display is desired, device and sensor readings along with biometric analytic data are accessible to through the user motion dynamics analysis system which may include a standalone software application. This application may be written in a programming language conforming to the operating system of the peripheral display device or generically through, such as for display through a browser.
The user motion dynamics analysis system receives the sensor data, saves the data with timestamps in order to track data for continuous monitoring and viewing of past history, as well as displays various parameters. This includes summary data such as the average peak force as measured by the wearable device over a specified period of time.
All sensor data is saved to local memory on the user's mobile phone, tablet, or computer in order to allow for long-term analysis of propulsion technique trends. Additionally, all sensor data is saved directly to a microSD card, located on the PCB in a SMD microSD card connector. In one embodiment, the microSD card connector located on the PCB is a Molex 503182 Memory Card Connector. By saving all data over an extended period of time, the user motion dynamics analysis system has the ability to continuously monitor how much the propulsion parameters are changing per unit of time, as well as calculate the magnitude of the changes. The user motion dynamics analysis system additionally allows for the option to upload the gathered data onto the cloud database engine, providing a system for sharing data with a third party. Visualization of desired propulsion parameters is available through the GUI in the form of charts, lists, and graphs. At any point, the user has the option to permanently delete all personal data from the memory and/or cloud server using the user motion dynamics analysis system.
While a user is propelling his/her wheelchair, sensor data is collected and automatically sent wirelessly to the user motion dynamics analysis system on the user's mobile device, tablet, or home computer. This transmission may be achieved using various transmission protocols, including but not limited to Bluetooth, Wifi, NFC, RFID, text message, IEEE 802.11, IrDA, GSM, GPRS, LTE, EDGE, and PCI. Data transmission from the wearable device is achieved using a wireless data transmission protocol. In one embodiment, this is achieved using an nRF8001 low-power Bluetooth IC. Other embodiments utilize an on-chip Bluetooth 4.0 transmitter implemented in various SoC processors. One embodiment uses the on-chip Bluetooth 4.0 transmitter of the Nordic nRF51422 SoC. Other embodiments use the Nordic nRF51822 SoC. Yet another embodiment uses the Nordic nFR52832SoC. Wifi transmission may be used in a specific embodiment instead of Bluetooth transmission. An embodiment of this type uses an ESP8266 WiFi Module SoC as the main processor and wireless transmission module.
FIG. 8 illustrates the data process flow 800 of the wearable user motion dynamics analysis system 200 of FIG. 2 . As shown in FIG. 8 and described herein, the wearable device 810 detects, records, and transmits sensor data to the user mobile device 820 for analysis by the user motion dynamics analysis system. The user motion dynamics analysis system receives the sensor data metrics from the wearable device and retrieves from memory wheelchair parameters that were previously input and/or stored by the user and/or a care provider as well as user specific information for risk evaluation that was previously input and/or stored by the user and/or a care provider, both of which are discussed further below.
The user motion dynamics analysis system then computes a shoulder usage summary and injury risk either at the user device or transmits the data to a cloud server for analysis there. The resulting metrics and/or shoulder injury risk information calculated by the user motion dynamics analysis system may then be displayed for the user, for example at the GUI. Additionally, the user motion dynamics analysis system may send alert data to the wearable device over the bio-feedback alert system.
FIG. 9 illustrates a flowchart 900 of a low power usage method for the wearable device. First, at step 900 , power is supplied to the wearable device. Next, at step 905 , the sensor system on the wearable device boots up. Then, at step 910 , the accelerometer is powered up. Next, at step 915 , the activity monitor is started. At step 920 , a start wakeup interrupt is triggered and the process proceeds to step 925 . At step 925 , if no activity is detected at the accelerometer, then the process proceeds to step 930 and the wearable device enters a low power sleep mode. At step 935 , the wakeup interrupt is completed and the process proceeds back to step 925 to await the detection of future activity at the accelerometer.
At some point in the future, activity is detected at the accelerometer, so the flowchart proceeds to step 940 and the wakeup interrupt is stopped. The process then proceeds to step 945 and the wearable device's peripherals and sensors are powered up. At step 950 , the sensors are calibrated. Next, at step 955 , the sensors are places into a low-power sleep mode. At step 960 , wireless communication is initiated. Then, at step 965 , a wireless connection monitor interrupt is started. The process then proceeds to step 970 where it is determined whether a wireless connection has been established. If the wireless connection has not been established, then the process proceeds to step 975 and the wearable device enters a low power sleep mode and the wireless connection interrupt completes at step 980 . The process then proceeds to step 970 and awaits the establishment of a wireless connection. Conversely, if a wireless connection is established at step 970 , then the process proceeds to step 985 and the wearable device waits for user instruction.
FIG. 10 illustrates the data flow 1000 of the calculation of the injury risk score and biofeedback according to an embodiment of the present invention. As shown in FIG. 10 , at step 1005 , the sensor data is collected from both hands of the wearable device and sent to the mobile device with the corresponding clock time (time stamps). At <figure-callout id="1010" label="st
CLAIMS
Claims ( 12 )
The invention claimed is:
1. A wheelchair user strain injury alert system, said system including:
a wearable device, wherein said device is worn on the hand of a wheelchair user, wherein said wearable device includes:
one or more force sensing resistors positioned proximal to the palm of the user's hand when said wearable device is worn, wherein said one or more force sensing resistors determine grip force data representing the force of said user gripping said wheelchair; and
a user feedback system including an alert display; and
a user motion dynamics analysis system, wherein said user motion dynamics analysis system includes a memory storing a user-specific grip force risk table,
wherein said user motion dynamics analysis system receives said grip force data and compares said grip force data to said grip force risk table to determine a grip force risk score,
wherein said user motion dynamics analysis system compares said grip force risk score to a predetermined overall average risk score table and generates alert data when said overall average risk score exceeds a predetermined threshold,
wherein said user feedback system receives said alert data and displays an alert using said alert display in response to the receipt of said alert data.
2. The system of claim 1 wherein said memory also stores user physical characteristics data representing one or more physical characteristics of said user,
wherein said user motion dynamics analysis system compares said user physical characteristics data to a predetermined user physical characteristics risk table to determine a user physical characteristic risk score,
wherein said overall average risk score is determined by averaging said physical characteristic risk score and said grip force risk score.
3. The system of claim 1 further including:
a gyroscope determining wrist angular movement data representing wrist angular movement of said user,
wherein said user motion dynamics analysis system receives said wrist angular movement data and compares said wrist angular movement data to a predetermined wrist angular movement risk table to determine a wrist angular movement risk score,
wherein said overall average risk score is determined by averaging said wrist angular movement risk score and said grip force risk score.
4. The system of claim 1 further including:
an accelerometer determining wrist acceleration data representing the acceleration pattern of said user's wrist during wheelchair use,
wherein said user motion dynamics analysis system receives said wrist acceleration data and compares said wrist acceleration data to a predetermined wrist acceleration risk table to determine a wrist acceleration risk score,
wherein said overall average risk score is determined by averaging said wrist acceleration risk score and said grip force risk score.
5. The system of claim 1 wherein said user motion dynamics analysis system identifies a plurality of peaks in said grip force data, determines a number of grip force peaks occurring in a predetermined time frame, and compares said number of grip force peaks to a predetermined threshold to determine a number of peaks risk score,
wherein said overall average risk score is determined by averaging said number of peaks risk score and said grip force risk score.
6. The system of claim 1 wherein said alert provides sensory feedback to said user.
7. A method for alerting a wheelchair user of an increased risk of a strain injury, said method including:
positioning a wearable device on a hand of a user of a wheelchair wherein said wearable device includes:
one or more force sensing resistors positioned proximal to the palm of the user's hand when said wearable device is worn, wherein said one or more force sensing resistors determine grip force data representing the force of said user gripping said wheelchair; and
a user feedback system including an alert display; and
transmitting said grip force data to a user motion dynamics analysis system, wherein said user motion dynamics analysis system includes a memory storing a user-specific grip force risk table;
determining a grip force risk score, at said user motion dynamics analysis system, by comparing said grip force data with said grip force risk table;
comparing said grip force risk score to a predetermined overall average risk score table;
generating alert data when said overall average risk score exceeds a predetermined threshold; and
receiving said alert data at said user feedback system and displaying an alert using said alert display in response to the receipt of said alert data.
8. The method of claim 7 wherein said memory also stores user physical characteristics data representing one or more physical characteristics of said user,
further including:
determining a user physical characteristic risk score, at said user motion dynamics analysis system, by comparing said user physical characteristics data with a predetermined user physical characteristics risk table;
averaging said user physical characteristic risk score and said grip force risk score and comparing said average to said predetermined overall average risk score table;
generating said alert data when said overall average risk score exceeds a predetermined threshold.
9. The method of claim 7 further including:
positioning a gyroscope on said wearable device;
determining wrist angular movement data representing wrist angular movement of said user using said gyroscope,
wherein said user motion dynamics analysis system receives said wrist angular movement data and compares said wrist angular movement data to a predetermined wrist angular movement risk table to determine a wrist angular movement risk score,
averaging said wrist angular movement risk score and said grip force risk score and comparing said average to said predetermined overall average risk score table; and
generating said alert data when said overall average risk score exceeds a predetermined threshold.
10. The method of claim 7 further including:
positioning an accelerometer on said wearable device;
determining wrist acceleration data representing the acceleration pattern of said user's wrist during wheelchair use using said accelerometer
wherein said user motion dynamics analysis system receives said wrist acceleration data and compares said wrist acceleration data to a predetermined wrist acceleration risk table to determine a wrist acceleration risk score,
averaging said wrist acceleration risk score and said grip force risk score and comparing said average to said predetermined overall average risk score table; and
generating said alert data when said overall average risk score exceeds a predetermined threshold.
11. The method of claim 7 further including:
identifying, at said user motion dynamics analysis system, a plurality of peaks in said grip force data;
determining a number of grip force peaks occurring in a predetermined time frame;
comparing said number of grip force peaks to a predetermined threshold to determine a number of peaks risk score,
averaging said number of peaks risk score and said grip force risk score and comparing said average to said predetermined overall average risk score table; and
generating said alert data when said overall average risk score exceeds a predetermined threshold.
12. The method of claim 7 wherein said alert provides sensory feedback to said user.
US15/449,103
2016-03-03
2017-03-03
Wearable device and system for preventative health care for repetitive strain injuries
Expired - Fee Related
US10456078B2
( en )
Priority Applications (1)
Application Number
Priority Date
Filing Date
Title
US15/449,103
US10456078B2
( en )
2016-03-03
2017-03-03
Wearable device and system for preventative health care for repetitive strain injuries
Applications Claiming Priority (2)
Application Number
Priority Date
Filing Date
Title
US201662303057P
2016-03-03
2016-03-03
US15/449,103
US10456078B2
( en )
2016-03-03
2017-03-03
Wearable device and system for preventative health care for repetitive strain injuries
Publications (2)
Publication Number
Publication Date
US20170251972A1
US20170251972A1 ( en )
2017-09-07
US10456078B2
true
US10456078B2 ( en )
2019-10-29
Family
ID=59723109
Family Applications (1)
Application Number
Title
Priority Date
Filing Date
US15/449,103
Expired - Fee Related
US10456078B2
( en )
2016-03-03
2017-03-03
Wearable device and system for preventative health care for repetitive strain injuries
Country Status (1)
Country
Link
US
( 1 )
US10456078B2
( en )
Families Citing this family (21)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US20160058372A1
( en )
2014-09-02
2016-03-03
Apple Inc.
Terrain type inference from wearable with motion sensing
US10699594B2
( en )
2015-09-16
2020-06-30
Apple Inc.
Calculating an estimate of wind resistance experienced by a cyclist
US10620232B2
( en )
2015-09-22
2020-04-14
Apple Inc.
Detecting controllers in vehicles using wearable devices
US10456078B2
( en )
*
2016-03-03
2019-10-29
Chandrasekaran Jayaraman
Wearable device and system for preventative health care for repetitive strain injuries
US10694994B2
( en )
2016-03-22
2020-06-30
Apple Inc.
Techniques for jointly calibrating load and aerobic capacity
US10687707B2
( en )
*
2016-06-07
2020-06-23
Apple Inc.
Detecting activity by a wheelchair user
US10687752B2
( en )
2016-08-29
2020-06-23
Apple Inc.
Detecting unmeasurable loads using heart rate and work rate
JP6745980B2
( en )
2016-08-31
2020-08-26
ã¢ããã« ã¤ã³ã³ã¼ãã¬ã¤ãããＡï½ï½ï½ï½ Ｉï½ï½ï¼
System and method for swimming analysis
US10617912B2
( en )
2016-08-31
2020-04-14
Apple Inc.
Systems and methods of swimming calorimetry
US11896368B2
( en )
2016-08-31
2024-02-13
Apple Inc.
Systems and methods for determining swimming metrics
US10512406B2
( en )
2016-09-01
2019-12-24
Apple Inc.
Systems and methods for determining an intensity level of an exercise using photoplethysmogram (PPG)
US11051720B2
( en )
2017-06-01
2021-07-06
Apple Inc.
Fitness tracking for constrained-arm usage
CN108074633B
( en )
*
2017-12-27
2021-12-21
广ä¸å°å¤©æç§ææéå ¬å¸
Exercise coordination monitoring method based on daily activities and wearable device
DE202018101048U1
( en )
*
2018-02-26
2019-06-06
Meyra Gmbh
wheelchair
US11227507B2
( en )
*
2018-10-11
2022-01-18
International Business Machines Corporation
Wearable technology employed in injury detection, prevention and skills training
US11937904B2
( en )
2019-09-09
2024-03-26
Apple Inc.
Detecting the end of cardio machine activities on a wearable device
US12109453B2
( en )
2019-09-27
2024-10-08
Apple Inc.
Detecting outdoor walking workouts on a wearable device
US12478835B2
( en )
2019-09-27
2025-11-25
Apple Inc.
Detecting the end of hiking activities on a wearable device
US11439322B2
( en )
*
2019-12-05
2022-09-13
Peter Garay
Method and apparatus for sports and muscle memory training and tracking
US11224359B2
( en )
*
2019-12-17
2022-01-18
Robert Bosch Gmbh
Repetitive human activities abnormal motion detection
US12382135B2
( en )
*
2024-01-08
2025-08-05
Logistics and Supply Chain MultiTech R&D Centre Limited
Television use assessment system
Citations (19)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US6760009B2
( en )
*
1998-06-09
2004-07-06
Ricoh Company, Ltd.
Coordinate position inputting/detecting device, a method for inputting/detecting the coordinate position, and a display board system
US20060136285A1
( en )
*
2004-12-17
2006-06-22
International Business Machines Corporation
Tiered on-demand location-based tracking service and infrastructure
US20060233632A1
( en )
*
2005-02-28
2006-10-19
The Braun Corporation
Wheelchair lift with a rotary sensor used to determine lift position
US20060281980A1
( en )
*
2003-10-13
2006-12-14
Novo Nordisk A/S
Apparatus and method for determining a physiological condition
US20060282021A1
( en )
*
2005-05-03
2006-12-14
Devaul Richard W
Method and system for fall detection and motion analysis
US20080004550A1
( en )
*
2004-02-05
2008-01-03
Motorika, Inc.
Methods and Apparatus for Rehabilitation and Training
US20100157291A1
( en )
*
2008-12-18
2010-06-24
Palo Alto Research Center Incorporated
Causing Relative Motion
US20110270135A1
( en )
*
2009-11-30
2011-11-03
Christopher John Dooley
Augmented reality for testing and training of human performance
US20130006718A1
( en )
*
2011-07-01
2013-01-03
Certusview Technologies, Llc
Methods, apparatus and systems for chronicling the activities of field technicians
US20130046438A1
( en )
*
2011-08-17
2013-02-21
Harris Corporation
Haptic manipulation system for wheelchairs
US20130218456A1
( en )
*
2006-02-16
2013-08-22
John S. Zelek
Wearable tactile navigation system
US8944977B2
( en )
*
2006-04-12
2015-02-03
Daniel N. Foster
Combination ergonomic task chair and exercise device
US8961185B2
( en )
*
2011-08-19
2015-02-24
Pulson, Inc.
System and method for reliably coordinating musculoskeletal and cardiovascular hemodynamics
US20160143593A1
( en )
*
2013-10-16
2016-05-26
University of Central Oklahoma
Intelligent apparatus for patient guidance and data capture during physical therapy and wheelchair usage
US20170095382A1
( en )
*
2014-03-21
2017-04-06
Rensselaer Polytechnic Institute
Mobile human-friendly assistive robot
US20170251972A1
( en )
*
2016-03-03
2017-09-07
Chandrasekaran Jayaraman
Wearable device and system for preventative health care for repetitive strain injuries
US20180040258A1
( en )
*
2013-10-29
2018-02-08
Nordin Kouache
Method and system for transmitting tactile instructions to a human body
US9943268B1
( en )
*
2015-02-06
2018-04-17
Brain Trust Innovations I, Llc
System, medical item including RFID chip, data collection engine, server and method for capturing medical data
US20180225421A1
( en )
*
2017-02-08
2018-08-09
International Business Machines Corporation
Personalized health tracker and method for destination selection based on tracked personalized health information
2017
2017-03-03
US
US15/449,103
patent/US10456078B2/en
not_active
Expired - Fee Related
Patent Citations (19)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US6760009B2
( en )
*
1998-06-09
2004-07-06
Ricoh Company, Ltd.
Coordinate position inputting/detecting device, a method for inputting/detecting the coordinate position, and a display board system
US20060281980A1
( en )
*
2003-10-13
2006-12-14
Novo Nordisk A/S
Apparatus and method for determining a physiological condition
US20080004550A1
( en )
*
2004-02-05
2008-01-03
Motorika, Inc.
Methods and Apparatus for Rehabilitation and Training
US20060136285A1
( en )
*
2004-12-17
2006-06-22
International Business Machines Corporation
Tiered on-demand location-based tracking service and infrastructure
US20060233632A1
( en )
*
2005-02-28
2006-10-19
The Braun Corporation
Wheelchair lift with a rotary sensor used to determine lift position
US20060282021A1
( en )
*
2005-05-03
2006-12-14
Devaul Richard W
Method and system for fall detection and motion analysis
US20130218456A1
( en )
*
2006-02-16
2013-08-22
John S. Zelek
Wearable tactile navigation system
US8944977B2
( en )
*
2006-04-12
2015-02-03
Daniel N. Foster
Combination ergonomic task chair and exercise device
US20100157291A1
( en )
*
2008-12-18
2010-06-24
Palo Alto Research Center Incorporated
Causing Relative Motion
US20110270135A1
( en )
*
2009-11-30
2011-11-03
Christopher John Dooley
Augmented reality for testing and training of human performance
US20130006718A1
( en )
*
2011-07-01
2013-01-03
Certusview Technologies, Llc
Methods, apparatus and systems for chronicling the activities of field technicians
US20130046438A1
( en )
*
2011-08-17
2013-02-21
Harris Corporation
Haptic manipulation system for wheelchairs
US8961185B2
( en )
*
2011-08-19
2015-02-24
Pulson, Inc.
System and method for reliably coordinating musculoskeletal and cardiovascular hemodynamics
US20160143593A1
( en )
*
2013-10-16
2016-05-26
University of Central Oklahoma
Intelligent apparatus for patient guidance and data capture during physical therapy and wheelchair usage
US20180040258A1
( en )
*
2013-10-29
2018-02-08
Nordin Kouache
Method and system for transmitting tactile instructions to a human body
US20170095382A1
( en )
*
2014-03-21
2017-04-06
Rensselaer Polytechnic Institute
Mobile human-friendly assistive robot
US9943268B1
( en )
*
2015-02-06
2018-04-17
Brain Trust Innovations I, Llc
System, medical item including RFID chip, data collection engine, server and method for capturing medical data
US20170251972A1
( en )
*
2016-03-03
2017-09-07
Chandrasekaran Jayaraman
Wearable device and system for preventative health care for repetitive strain injuries
US20180225421A1
( en )
*
2017-02-08
2018-08-09
International Business Machines Corporation
Personalized health tracker and method for destination selection based on tracked personalized health information
Also Published As
Publication number
Publication date
US20170251972A1
( en )
2017-09-07
Similar Documents
Publication
Publication Date
Title
US20170251972A1
( en )
2017-09-07
Wearable device and system for preventative health care for repetitive strain injuries
US11826165B2
( en )
2023-11-28
Devices, systems, and methods for adaptive health monitoring using behavioral, psychological, and physiological changes of a body portion
US12357244B2
( en )
2025-07-15
System and method for diagnosing and notification regarding the onset of a stroke
US11123562B1
( en )
2021-09-21
Pain quantification and management system and device, and method of using
US11278238B2
( en )
2022-03-22
Wearable sensor device and analysis platform for objective outcome assessment in spinal diseases
CA2914240C
( en )
2023-04-04
Fall detection system and method
US10264971B1
( en )
2019-04-23
System and methods for integrating feedback from multiple wearable sensors
US20190282131A1
( en )
2019-09-19
Management of biomechanical achievements
US20070063850A1
( en )
2007-03-22
Method and system for proactive telemonitor with real-time activity and physiology classification and diary feature
US11699524B2
( en )
2023-07-11
System for continuous detection and monitoring of symptoms of Parkinson&#39;s disease
US20060252999A1
( en )
2006-11-09
Method and system for wearable vital signs and physiology, activity, and environmental monitoring
EP4537748A1
( en )
2025-04-16
Methods and systems for arrhythmia tracking and scoring
KR101584090B1
( en )
2016-01-12
Mirror device, wearable device and exercise management system
Fotiadis et al.
2006
Wearable medical devices
Geman et al.
2018
Ubiquitous healthcare system based on the sensors network and android internet of things gateway
Aakesh et al.
2023
Review on Healthcare Monitoring and Tracking Wristband for Elderly People using ESP-32
US20250339105A1
( en )
2025-11-06
System and method for diagnosis and notification regarding the onset of a stroke
TW201941153A
( en )
2019-10-16
Method and device for intelligent sleep detecting system for human physiological message providing an analysis result to a communication information unit according to identify recognition data
Avella-RodrÃguez et al.
2022
Multimodal Wearable Technology Approaches to Human Falls
Bächlin et al.
2011
Experiences in developing a wearable gait assistant for parkinsonâs disease patients
AU2014200437A1
( en )
2014-09-04
Systems and Methods for Musculoskeletal Health Care Using Wearable Sensors
Legal Events
Date
Code
Title
Description
2019-03-16
STPP
Information on status: patent application and granting procedure in general
Free format text : RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER
2019-06-17
STPP
Information on status: patent application and granting procedure in general
Free format text : NOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONS
2019-09-21
STPP
Information on status: patent application and granting procedure in general
Free format text : PUBLICATIONS -- ISSUE FEE PAYMENT VERIFIED
2019-10-09
STCF
Information on status: patent grant
Free format text : PATENTED CASE
2023-06-19
FEPP
Fee payment procedure
Free format text : MAINTENANCE FEE REMINDER MAILED (ORIGINAL EVENT CODE: REM.); ENTITY STATUS OF PATENT OWNER: MICROENTITY
2023-12-04
LAPS
Lapse for failure to pay maintenance fees
Free format text : PATENT EXPIRED FOR FAILURE TO PAY MAINTENANCE FEES (ORIGINAL EVENT CODE: EXP.); ENTITY STATUS OF PATENT OWNER: MICROENTITY
2023-12-04
STCH
Information on status: patent discontinuation
Free format text : PATENT EXPIRED DUE TO NONPAYMENT OF MAINTENANCE FEES UNDER 37 CFR 1.362
2023-12-26
FP
Lapsed due to failure to pay maintenance fee
Effective date : 20231029