The prototype of wearable sensors system for supervision of patient rehabilitation using artificial intelligence methods
In this paper, we investigate a wearable multi-sensor system for telemedical supervision of patient rehabilitation in home conditions. Our approach is based on a set of wearable sensors connected with a master digital acquisition module. Proposed platform is capable of recording a single-lead ECG and acceleration signals which are used for determining the patient’s activity form. During research we examined the following activities of daily living: sitting (idle), walking and squatting. A set of machine learning methods were used for classification of mentioned activities as rehabilitation exercises. Our methodology showed high success rates (above 92%) for detecting selected activities and it showed great potential to be used for patient’s activity recognition.
Keywordswearable system body sensor network biomedical sensors telemedicine patient rehabilitation activity classification
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