Feasibility of Immersive Virtual Reality and Customized Robotics with Wearable Sensors for Upper Extremity Training

Behdokht Kiafar, Pinar Kullu, Rakshith Lokesh, Amit Chaudhari, Qile Wang, Shayla Sharmin, Sagar M. Doshi, Elham Bakhshipour, Erik Thostenson, Joshua Cashaback, Roghayeh Leila Barmaki
Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:543-556, 2025.

Abstract

Upper limb impairment significantly impacts daily activities and quality of life. Traditional robotic systems have been widely used in neurological rehabilitation applications. However, its adoption has been limited to laboratory and clinical settings due to cost constraints. Our study aimed to assess the feasibility and usability of a cost-effective virtual reality (VR) for home-based upper limb training. We used a customized wearable sleeve sensor to assess the hand and elbow joint movements objectively. A pilot user study (n = 16) with healthy participants involved evaluating system usability, task load, and presence within two conditions of VR alone and VR combined with a customized inverse kinematics robot arm (KinArm). Results of statistical analysis using a two-way repeated measure (ANOVA) revealed no significant difference between conditions in task completion time. However, significant differences were observed in the normalized number of mistakes and recorded elbow joint angles between tasks. Our findings highlight the potential advantages of an immersive and multi-sensory approach towards performance assessment. This study explores avenues for the development of potentially cost-effective, tailored, and engaging environments for home-based therapy applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v287-kiafar25a, title = {Feasibility of Immersive Virtual Reality and Customized Robotics with Wearable Sensors for Upper Extremity Training}, author = {Kiafar, Behdokht and Kullu, Pinar and Lokesh, Rakshith and Chaudhari, Amit and Wang, Qile and Sharmin, Shayla and Doshi, Sagar M. and Bakhshipour, Elham and Thostenson, Erik and Cashaback, Joshua and Barmaki, Roghayeh Leila}, booktitle = {Proceedings of the sixth Conference on Health, Inference, and Learning}, pages = {543--556}, year = {2025}, editor = {Xu, Xuhai Orson and Choi, Edward and Singhal, Pankhuri and Gerych, Walter and Tang, Shengpu and Agrawal, Monica and Subbaswamy, Adarsh and Sizikova, Elena and Dunn, Jessilyn and Daneshjou, Roxana and Sarker, Tasmie and McDermott, Matthew and Chen, Irene}, volume = {287}, series = {Proceedings of Machine Learning Research}, month = {25--27 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v287/main/assets/kiafar25a/kiafar25a.pdf}, url = {https://proceedings.mlr.press/v287/kiafar25a.html}, abstract = {Upper limb impairment significantly impacts daily activities and quality of life. Traditional robotic systems have been widely used in neurological rehabilitation applications. However, its adoption has been limited to laboratory and clinical settings due to cost constraints. Our study aimed to assess the feasibility and usability of a cost-effective virtual reality (VR) for home-based upper limb training. We used a customized wearable sleeve sensor to assess the hand and elbow joint movements objectively. A pilot user study (n = 16) with healthy participants involved evaluating system usability, task load, and presence within two conditions of VR alone and VR combined with a customized inverse kinematics robot arm (KinArm). Results of statistical analysis using a two-way repeated measure (ANOVA) revealed no significant difference between conditions in task completion time. However, significant differences were observed in the normalized number of mistakes and recorded elbow joint angles between tasks. Our findings highlight the potential advantages of an immersive and multi-sensory approach towards performance assessment. This study explores avenues for the development of potentially cost-effective, tailored, and engaging environments for home-based therapy applications.} }
Endnote
%0 Conference Paper %T Feasibility of Immersive Virtual Reality and Customized Robotics with Wearable Sensors for Upper Extremity Training %A Behdokht Kiafar %A Pinar Kullu %A Rakshith Lokesh %A Amit Chaudhari %A Qile Wang %A Shayla Sharmin %A Sagar M. Doshi %A Elham Bakhshipour %A Erik Thostenson %A Joshua Cashaback %A Roghayeh Leila Barmaki %B Proceedings of the sixth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2025 %E Xuhai Orson Xu %E Edward Choi %E Pankhuri Singhal %E Walter Gerych %E Shengpu Tang %E Monica Agrawal %E Adarsh Subbaswamy %E Elena Sizikova %E Jessilyn Dunn %E Roxana Daneshjou %E Tasmie Sarker %E Matthew McDermott %E Irene Chen %F pmlr-v287-kiafar25a %I PMLR %P 543--556 %U https://proceedings.mlr.press/v287/kiafar25a.html %V 287 %X Upper limb impairment significantly impacts daily activities and quality of life. Traditional robotic systems have been widely used in neurological rehabilitation applications. However, its adoption has been limited to laboratory and clinical settings due to cost constraints. Our study aimed to assess the feasibility and usability of a cost-effective virtual reality (VR) for home-based upper limb training. We used a customized wearable sleeve sensor to assess the hand and elbow joint movements objectively. A pilot user study (n = 16) with healthy participants involved evaluating system usability, task load, and presence within two conditions of VR alone and VR combined with a customized inverse kinematics robot arm (KinArm). Results of statistical analysis using a two-way repeated measure (ANOVA) revealed no significant difference between conditions in task completion time. However, significant differences were observed in the normalized number of mistakes and recorded elbow joint angles between tasks. Our findings highlight the potential advantages of an immersive and multi-sensory approach towards performance assessment. This study explores avenues for the development of potentially cost-effective, tailored, and engaging environments for home-based therapy applications.
APA
Kiafar, B., Kullu, P., Lokesh, R., Chaudhari, A., Wang, Q., Sharmin, S., Doshi, S.M., Bakhshipour, E., Thostenson, E., Cashaback, J. & Barmaki, R.L.. (2025). Feasibility of Immersive Virtual Reality and Customized Robotics with Wearable Sensors for Upper Extremity Training. Proceedings of the sixth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 287:543-556 Available from https://proceedings.mlr.press/v287/kiafar25a.html.

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