Wearable Biofeedback for Knee Joint Health
2023
Miscellaneous
hi
The human body has the tremendous capacity to learn a new way of walking that reduces its risk of musculoskeletal disease progression. Wearable haptic biofeedback has been used to guide gait retraining in patients with knee osteoarthritis, enabling reductions in pain and improvement in function. However, this promising therapy is not yet a part of standard clinical practice. Here, I propose a two-pronged approach to improving the design and deployment of biofeedback for gait retraining. The first section concerns prescription, with the aim of providing clinicians with an interpretable model of gait retraining outcome in order to best guide their treatment decisions. The second section concerns learning, by examining how internal physiological state and external environmental factors influence the process of learning a therapeutic gait. This work aims to address the challenges keeping a highly promising intervention from being widely used to maintain pain-free mobility throughout the lifespan.
Author(s): | Nataliya Rokhmanova |
Year: | 2023 |
Month: | April |
Department(s): | Haptische Intelligenz |
Research Project(s): |
Gait Rehabilitation Through Haptic Feedback
|
Bibtex Type: | Miscellaneous (misc) |
Paper Type: | Abstract |
Address: | Hamburg, Germany |
DOI: | 10.1145/3544549.3577063 |
How Published: | Extended abstract (5 pages) presented at the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) Doctoral Consortium |
State: | Published |
BibTex @misc{Rokhmanova23-CHIEA-Biofeedback, title = {Wearable Biofeedback for Knee Joint Health}, author = {Rokhmanova, Nataliya}, howpublished = {Extended abstract (5 pages) presented at the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) Doctoral Consortium}, address = {Hamburg, Germany}, month = apr, year = {2023}, doi = {10.1145/3544549.3577063}, month_numeric = {4} } |