Killer heuristic optimized convolution neural network-based fall detection with wearable IoT sensor devices

• Published in: Measurement : journal of the International Measurement Confederation Vol. 167; p. 108258
• Main Authors: Alarifi, Abdulaziz, Alwadain, Ayed
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.01.2021; Elsevier Science Ltd
• Subjects: Accelerometer; Accelerometers; Alex Net convolution neural network; Artificial neural networks; Convolution; Data collection; Electronic devices; Fall detection; Falls; Feature extraction; Gyroscope; Internet of Things; Magnetometer; Motion sensors; Neural networks; Older people; Public health; Sensors; Wearable computers; Wearable sensor; Wearable technology; Accelerometer; Magnetometer; Gyroscope; Alex Net convolution neural network; Wearable sensor; Fall detection
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2020.108258

•It introduces the falls detection system by killer heuristics optimized AlexNet CNN.•The wearable sensor devices are placed on the subject’s body on 6 different positions.•Data is collected from the wearable device for feature extraction & analysis of data.•The features are analyzed by multi-linear principle component analysis. Current multi-disciplinary research views falls in the elderly as a significant worldwide public health risk. Several fall detection systems have been created that use wearable motion sensors, but these systems fail to provide an accurate assessment of the exact nature of human falls. This paper introduces an effective and optimized fall detection system that uses an approach based on a killer heuristics optimized AlexNet convolution neural network. Wearable sensor devices, which consist of a magnetometer, gyroscope, and accelerometer tri-axial device, are placed on the subject’s body in 6 different positions. During the data collection process, 16 activities of daily living and information on 20 voluntary falls are collected in 2520 trials. Information is collected from the IoT-assisted wearable device for feature extraction and analysis of sensor data. The derived features are analyzed by multilinear principle component analysis, which reduces the dimensionality of the features. Fall detection is then performed by applying the intelligent AlexNet convolution network. The fall detection based on the created wearable sensor device is evaluated using simulation results, and the system recognizes a fall with maximum accuracy and minimum complexity.