An Exercise Action Recognition System Using RFID Technology in Reducing the Impact of Alzheimer's Disease on the Elderly

This paper presents a recognition system that uses passive RFID tags to identify and monitor various exercise actions accurately. This system is designed to assist doctors in evaluating the conditions of Alzheimer's patients, particularly the elderly. By providing precise data on physical activity, it aims to aid in the early detection and management of health issues, thereby mitigate the effects of Alzheimer's disease on the elderly. The proposed recognition system does not required wearable tags. By combining RSSI and Phase parameters as feature data, this method improves the diversity and richness of the collected data. A time-efficient data segmentation algorithm and an even-odd splitting technique are used for data processing. After that, various machine learning models are applied to evaluate the effectiveness of this methodology. The Random Forest model achieves the highest accuracy of 94%.