Adaptive learning based human activity and fall detection using fuzzy frequent pattern mining

Human activity recognition (HAR) has gained a lot of significance in monitoring the health of people, especially to detect fall among elderly people who live independently. This project proposes a novel method for recognizing activities and detecting fall of a person using body-worn sensors. Traditional algorithms like Naïve Bayes classifier and Support Vector Machine are mainly used for activity classification. However, these systems fail to capture significant association that exists between interesting patterns. Existing accelerometer based wearable systems are not sufficient to determine the fall of a person. Hence, a Fuzzy Associative Classification based Human Activity Recognition (FAC-HAR) system is proposed to overcome the aforementioned drawbacks in detecting abnormal status of a person. The proposed (FAC-HAR) system uses three different sensors namely heartbeat, breathing rate and accelerometer and employs fuzzy clustering and associative classification for abnormality detection. The proposed system introduces a novel learning mechanism is to improve classification accuracy. A classification accuracy of 92% is achieved with the proposed fuzzy frequent pattern mining based human activity recognition.