Faliagka, E.; Skarmintzos, V.; Panagiotou, C.; Syrimpeis, V.; Antonopoulos, C.P.; Voros, N. Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring. Electronics2023, 12, 3375.
Faliagka, E.; Skarmintzos, V.; Panagiotou, C.; Syrimpeis, V.; Antonopoulos, C.P.; Voros, N. Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring. Electronics 2023, 12, 3375.
Faliagka, E.; Skarmintzos, V.; Panagiotou, C.; Syrimpeis, V.; Antonopoulos, C.P.; Voros, N. Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring. Electronics2023, 12, 3375.
Faliagka, E.; Skarmintzos, V.; Panagiotou, C.; Syrimpeis, V.; Antonopoulos, C.P.; Voros, N. Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring. Electronics 2023, 12, 3375.
Abstract
In this work, an IoT system with edge computing capability is proposed facilitating postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises are executed correctly, which is critical since, it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages Internet of Things (IoT) paradigm, in combination with Deep Learning and Edge Computing to classify the extend-flex movement of one’s knee via embedded Machine Learning (ML) classification algorithms. The contribution of the proposed work is multilayered. Furthermore as an outcome of this work a dataset of labeled knee movements is freely available on https://www.kaggle.com/datasets/billskarm/knee-range-of-motion to the research community. It also provides real time movement detection with an accuracy reaching 100%, which is achieved with a ML model trained to fit a low cost, off the shelf, Bluetooth Low Energy platform. The proposed Edge Computing approach allows predictions to be performed on-device rather than solely relying on a cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy, while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99,9% wireless data transfer reduction, a critical 39% prediction delay reduction and a valuable 17% increase of prediction event. Finally, enhanced privacy comprises another significant benefit from the implemented Edge Computing ML model as sensitive data can be processed on-site and only events or predictions are shared with the medical personnel.
Keywords
Edge computing; Internet of Things system; Knee rehabilitation; Machine Learning rehabilitation; Movement detection
Subject
Public Health and Healthcare, Physical Therapy, Sports Therapy and Rehabilitation
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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