Signal Processing and Feature Extraction in Markerless Telerehabilitation

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 33; pp. 911 - 924
• Main Authors: Dellepiane, Silvana G., Ferraro, Federica, Baffigo, Camilla, Simonini, Marina
• Format: Journal Article
• Language: English
• Published: United States IEEE 2025
• Subjects: Accuracy; Adult; Algorithms; Data mining; Exercise Therapy - methods; Feature extraction; Female; Healthy Volunteers; Humans; Male; markerless motion capture (MMC) sensor; Middle Aged; Motion capture; Movement; Noise; nonlinear signal processing; Older adults; Posture; Protocols; Reproducibility of Results; Signal Processing, Computer-Assisted; skeletal joint detection; Smoothing methods; Telerehabilitation; Three-dimensional displays; Video games; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2025.3541153

Telerehabilitation solutions are a concrete answer to many needs in the healthcare framework since they enable remote support for patients and foster continuity of care. This paper explores telerehabilitation using the ReMoVES system, a markerless approach that facilitates remote exercise guidance. Focusing on the sit-to-stand (STS) task, which is crucial for daily activities, this study employs the Microsoft Kinect sensor for human movement monitoring. Emphasizing preprocessing and analysis, the research extracts reliable parameters, enabling remote observation and evaluation of patient performance. This study highlights the importance of noise reduction and automatic segmentation for feature extraction, which are essential for assessing task execution and identifying compensatory movements. By utilizing a diverse healthy subject group, a reference model is established, providing optimal features for accurate exercise execution. Statistical analyses involving both healthy subjects and patients revealed key features for remote exercise observation. Automatic feature extraction related to poses and body movements, together with homogeneity within control group sessions, forms the basis for a quantitative parametric model. This model describes and compares accurate exercise execution, offering a method to remotely evaluate and adapt individual rehabilitation plans on the basis of robust and reliable parameters.