Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model

Abstract

Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences.

A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

“…Human falls are typically connected to one or several brusque peaks of the acceleration originated when the body hits the ground [104]. This acceleration peak (or peaks) is preceded by a period of "free fall" during which the acceleration module rapidly decays.…”

A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets

“…Falls are normally associated with one or several sudden upsurges of the acceleration magnitude caused by the impacts of the body against the ground [63]. Hence, our analysis will be concentrated on a time interval of fixed duration around the instant in which the maximum value of the acceleration magnitude is detected, implicitly assuming that in the case of the sequence with a fall event, the fall has occurred during this interval.…”

A Survey on Recent Advances in Wearable Fall Detection Systems

“…Recent research has also focused on the application of ensembles to fall detection. Hsieh et al [46] use a combination of threshold-based and knowledge-based approach based on SVM, on data from a triaxial accelerometer, to BioMed Research International detect a fall event. Absolute falls and ADLs are detected using thresholds on acceleration.…”