Performance evaluation and rectification of prosthetic sockets: a machine learning approach using wearable sensors

Ottikkutti, Suranjan Ram ORCID: https://orcid.org/0000-0002-0699-3889 , Mehryar, Pouyan ORCID: https://orcid.org/0000-0003-2396-6433 , Zeybek, Begum ORCID: https://orcid.org/0000-0001-6789-043X , Karamousadakis, Michalis, Ali, Zulfiqur ORCID: https://orcid.org/0000-0001-9884-8146 and Chen, Dejiu ORCID: https://orcid.org/0000-0001-7048-0108 (2025) Performance evaluation and rectification of prosthetic sockets: a machine learning approach using wearable sensors. IEEE Access .

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Abstract

This study demonstrates a data-driven decision support system to aid in rectification of prosthetic sockets aimed at improving overall comfort perceived by amputees. Prosthetic technology, particularly in the realm of socket design, plays a pivotal role in rehabilitation for individuals with limb amputations. Prosthetic sockets, which serve as the critical interface between the residual limb and the artificial limb, enable amputees to walk without the need for invasive implants that connect directly to the bone of the residual limb. This study focuses on the role of intra-socket pressure in socket performance and its impact on optimal socket rectifications for improving comfort in transfemoral amputees. Employing thin Force Sensing Resistor (FSR) sensors, the research measures dynamic pressure variations across individual gait cycles. To explore the effects of altered pressure distribution on socket performance, a clinical trial was conducted consisting of four different socket configurations across several participants, one of which was with no pad inserted and three of which incorporated a silicone pad to modify the dynamic pressure profiles. With data from multiple participants including specific dynamic pressure features extracted from FSR sensors, and subjective feedback of comfort, a Multi-Layer Perceptron (MLP) model is trained to establish predictive relationships between intra-socket pressure and appropriate rectification action. The findings suggest that the MLP agent is more accurate at suggesting rectification actions to prosthetists when compared to simpler classification algorithms such as Random Forest, XGBoost and Logistic regression, laying the foundation for future advancements in prosthetic design.

Item Type:	Article
Journal / Publication Title:	IEEE Access
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2169-3536
Departments:	Professional Services > Vice Chancellor's Office
Additional Information:	Professor Zulfiqur Ali, PhD, Pro-Vice Chancellor for Research and Knowledge Exchange, University of Cumbria, UK. This work is licensed under a Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/).
Depositing User:	Anna Lupton
Date Deposited:	19 Sep 2025 08:46
Last Modified:	24 Sep 2025 09:45
URI:	https://insight.cumbria.ac.uk/id/eprint/9051

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