A Hybrid Temporal Reasoning Framework for Fall Monitoring

Abstract

This paper presents a real-time method for detecting a fall at different phases using a wireless tri-axial accelerometer and reports the classification performance when the sensor is placed on different body parts. The proposed hybrid framework combines a rule-based knowledge representation scheme with a time control mechanism and machine-learning-based activity classification. Real-time temporal reasoning is performed using a standard rule-based inference engine. The framework is validated for fall detection performance, false alarm evaluation, and comparison with a highly cited baseline method. Based on a data set with 14 fall types (280 falls) collected from 16 subjects, the highest accuracy values of 86.54%, 87.31%, and 91.15% are obtained for fall detection at pre-impacts, impacts, and post-impacts, respectively. Without post-impact activity information, the side of the waist and chest are the best sensor positions, followed by the head, front of the waist, wrist, ankle, thigh, and upper arm. With post-impact activity information, the best sensor position is the side of the waist, followed by the head, wrist, front of the waist, thigh, chest, ankle, and upper arm. Most false alarms occur during transitions of lying postures. The proposed method is more robust to a variety of fall and activity types and yields better classification performance and false alarm rates compared with the baseline method. The results provide guidelines for sensor placement when developing a fall monitoring system.