Efficient energy smart sensor for fall detection based on accelerometer data and CNN model
Brahim Achour, Idir Filali, Malika Belkadi, Mourad Laghrouche · 2022
Fall detection helps to provide medical assistance quickly and to avoid the aggravation of injuries. In this paper, we propose a new noninvasive and energy-efficient smart sensor for fall detection. The sensor is based on accelerometer data and is attached to 20 building workers. To reduce power consumption, a new method of data selection is proposed. This method is based on the use of sensor timers, which allows for the reduction of 91% of the acquired data and 94% of the transmitted data. Regarding the classification, a new classification approach is proposed. Indeed, each data segment is displayed as a graph. Then, a convolution neural network is trained to detect the presence or absence of falls in each graph. An accuracy of 98% was obtained. This result exceeds that obtained in several studies and shows the effectiveness of the proposed approach.