This paper discusses the design and implementation of a comprehensive sensor based framework for medical activity recognition of elderly patients. Tri-axial accelerometers worn on the wrists monitor hand movements to recognize activities. A comparison of different methods of feature extraction from tri-axial accelerometer signals is performed. These are based on well known Time and Frequency domain analyses, Wavelet Transform and Hilbert-Huang Transform (HHT) using Empirical Mode Decomposition (EMD). The dataset of activities is collected from 30 subjects and comprises of the following activities: opening a pill box, popping a pill in the mouth, drinking water, using a syringe, falling down, sleeping, sugar test and BP test. Acitivites are classified using Naïve Bayes classifier, k-Nearest Neighbor algorithm and SVM algorithm. The activity recognition system is integrated with a GSM module that is used to automatically prompt appropriate alerts to a medical examiner in case of any abnormalities in the patient’s physical activities. This will have huge potential in reducing the cost of providing health care services to the elderly.
MEDICAL ACTIVITY MONITORING FOR ELDERLY PEOPLE USING WEARABLE WRIST DEVICE