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Advisor(s)
Abstract(s)
Shoulder rehabilitation is a process that requires physical therapy sessions to recover the
mobility of the affected limbs. However, these sessions are often limited by the availability and
cost of specialized technicians, as well as the patient’s travel to the session locations. This paper
presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the
quality of the movements and provide feedback, allowing patients to perform autonomous recovery
sessions. This paper reviews the state of the art in wearable devices and camera-based systems for
human body detection and rehabilitation support and describes the system developed, which uses
MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with
reference videos made by professional physiotherapists using cosine similarity and dynamic time
warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion
capture, to validate the methods used by the smartphone application. The results show that there are
statistically significant differences between the three methods for different exercises, highlighting
the importance of selecting an appropriate method for specific exercises. This paper discusses the
implications and limitations of the findings and suggests directions for future research.
Description
Keywords
pose estimation; exercise evaluation; mobile health; remote monitoring; rehabilitation
Citation
1. Pereira B, Cunha B, Viana P, Lopes M, Melo ASC, Sousa ASP (2023). A Machine Learning App for Monitoring Physical Therapy at Home. Sensors. 2024; 24(1):158. https://doi.org/10.3390/s24010158