Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures
Personalizing wearable robots by incorporating user physiological feedback can improve energy efficiency and comfort. However, many current personalization methods are specific to a particular device and often require reprogramming, making them less accessible. In this study, we present an open-sour...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10196448/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233929533390848 |
|---|---|
| author | Prakyath Kantharaju Sai Siddarth Vakacherla Michael Jacobson Hyeongkeun Jeong Meet Nikunj Mevada Xingyuan Zhou Matthew J. Major Myunghee Kim |
| author_facet | Prakyath Kantharaju Sai Siddarth Vakacherla Michael Jacobson Hyeongkeun Jeong Meet Nikunj Mevada Xingyuan Zhou Matthew J. Major Myunghee Kim |
| author_sort | Prakyath Kantharaju |
| collection | DOAJ |
| description | Personalizing wearable robots by incorporating user physiological feedback can improve energy efficiency and comfort. However, many current personalization methods are specific to a particular device and often require reprogramming, making them less accessible. In this study, we present an open-source, device-independent personalization framework that allows for human-in-the-loop optimization. This modular framework includes cost functions and optimization algorithms that use a physiological response to optimize wearable robot parameters. We tested this framework in three case studies involving diverse subjects and wearable robots. The first case study focused on personalizing an ankle-foot prosthesis using indirect calorimetry feedback. This resulted in a 5.3% and 18.1% reduction in metabolic cost for walking for two individuals with transtibial amputation, compared to the weight-based assistance. The second case study personalized a robotic ankle exoskeleton for three different walking speeds using indirect calorimetry feedback for two subjects. The metabolic cost was reduced by 1%, 2%, and 5.8% for one subject and by 20.8%, 1.9%, and 19% for the other subject, compared to a generic assistance condition for increasing speeds. The third case study personalized gait parameters, specifically step frequency, using an electrocardiogram (ECG)-based cost function along with an optimization algorithm variant, resulting in a 43% reduction in optimization time for one non-disabled subject. These case studies suggest that our personalization framework can effectively personalize wearable robot parameters and potentially enhance assistance benefits. |
| format | Article |
| id | doaj-art-c9edcc2bd4b3491db5789eaebfb6c85f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c9edcc2bd4b3491db5789eaebfb6c85f2025-08-20T04:03:21ZengIEEEIEEE Access2169-35362023-01-0111813898140010.1109/ACCESS.2023.329987310196448Framework for Personalizing Wearable Devices Using Real-Time Physiological MeasuresPrakyath Kantharaju0https://orcid.org/0000-0003-3807-1683Sai Siddarth Vakacherla1Michael Jacobson2https://orcid.org/0000-0003-2220-8622Hyeongkeun Jeong3https://orcid.org/0000-0003-2592-7550Meet Nikunj Mevada4Xingyuan Zhou5Matthew J. Major6https://orcid.org/0000-0002-2330-4619Myunghee Kim7https://orcid.org/0000-0001-8965-6206Department of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL, USADepartment of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL, USADepartment of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL, USADepartment of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL, USADepartment of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL, USADepartment of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, USAResearch Health Scientist, Jesse Brown VA Medical Center, Chicago, IL, USADepartment of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL, USAPersonalizing wearable robots by incorporating user physiological feedback can improve energy efficiency and comfort. However, many current personalization methods are specific to a particular device and often require reprogramming, making them less accessible. In this study, we present an open-source, device-independent personalization framework that allows for human-in-the-loop optimization. This modular framework includes cost functions and optimization algorithms that use a physiological response to optimize wearable robot parameters. We tested this framework in three case studies involving diverse subjects and wearable robots. The first case study focused on personalizing an ankle-foot prosthesis using indirect calorimetry feedback. This resulted in a 5.3% and 18.1% reduction in metabolic cost for walking for two individuals with transtibial amputation, compared to the weight-based assistance. The second case study personalized a robotic ankle exoskeleton for three different walking speeds using indirect calorimetry feedback for two subjects. The metabolic cost was reduced by 1%, 2%, and 5.8% for one subject and by 20.8%, 1.9%, and 19% for the other subject, compared to a generic assistance condition for increasing speeds. The third case study personalized gait parameters, specifically step frequency, using an electrocardiogram (ECG)-based cost function along with an optimization algorithm variant, resulting in a 43% reduction in optimization time for one non-disabled subject. These case studies suggest that our personalization framework can effectively personalize wearable robot parameters and potentially enhance assistance benefits.https://ieeexplore.ieee.org/document/10196448/Wearable devicepersonalizationhuman-in-the-loop optimizationexoskeletonprosthesismetabolic cost |
| spellingShingle | Prakyath Kantharaju Sai Siddarth Vakacherla Michael Jacobson Hyeongkeun Jeong Meet Nikunj Mevada Xingyuan Zhou Matthew J. Major Myunghee Kim Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures IEEE Access Wearable device personalization human-in-the-loop optimization exoskeleton prosthesis metabolic cost |
| title | Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures |
| title_full | Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures |
| title_fullStr | Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures |
| title_full_unstemmed | Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures |
| title_short | Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures |
| title_sort | framework for personalizing wearable devices using real time physiological measures |
| topic | Wearable device personalization human-in-the-loop optimization exoskeleton prosthesis metabolic cost |
| url | https://ieeexplore.ieee.org/document/10196448/ |
| work_keys_str_mv | AT prakyathkantharaju frameworkforpersonalizingwearabledevicesusingrealtimephysiologicalmeasures AT saisiddarthvakacherla frameworkforpersonalizingwearabledevicesusingrealtimephysiologicalmeasures AT michaeljacobson frameworkforpersonalizingwearabledevicesusingrealtimephysiologicalmeasures AT hyeongkeunjeong frameworkforpersonalizingwearabledevicesusingrealtimephysiologicalmeasures AT meetnikunjmevada frameworkforpersonalizingwearabledevicesusingrealtimephysiologicalmeasures AT xingyuanzhou frameworkforpersonalizingwearabledevicesusingrealtimephysiologicalmeasures AT matthewjmajor frameworkforpersonalizingwearabledevicesusingrealtimephysiologicalmeasures AT myungheekim frameworkforpersonalizingwearabledevicesusingrealtimephysiologicalmeasures |