Machine Learning for Classification of Physical Therapy Exercises from Inertial and Magnetic Sensor Data
DOI:
https://doi.org/10.48047/7d2n8z19Keywords:
Keywords: Patient rehabilitation, Physical therapy, Machine Learning, Support vector classifier, AdaBoost classifier, Inertial and magnetic sensor dataAbstract
Physical therapy exercises play a crucial role in patient rehabilitation, yet accurate classification remains a challenge. Studies show that sensor-based recognition of human activities can achieve up to 90% accuracy using machine learning, while traditional manual assessments are prone to inconsistencies, resulting in a 15–20% variation in evaluation outcomes. Currently, physical therapists rely heavily on subjective observations, which are time-consuming and susceptible to human error, limiting the precision of patient progress tracking. To address these challenges, we propose a machine learning based approach f
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