Design of a Wearable Wireless Multi-Sensor Monitoring System and Application for Activity Recognition Using Deep Learning

Abstract

Human activity monitoring and recognition systems assist experts in evaluating various health problems including obesity, cardiac diseases and, sports injury detection. However, these systems have two challenging points; monitoring activities for outdoor applications and extracting relevant features using hand-crafted techniques from multi-dimensional and large datasets. To address these challenges, we have focused on new dataset generation for activity recognition, a novel design of a sensor-based wireless activity monitoring system, and its application to deep learning neural networks. The designed monitoring system consists of one master and four slave devices, and can collect and record acceleration and gyroscope information. The slave devices were attached on arm, chest, thigh, and shank areas of the human body. Activity data were collected and recorded from sixty healthy people for thirteen activity types including drink from cup and cleaning table. These activities were divided into three activity categories as basic, complex, and all, which is the combination of basic and complex activities. Obtained datasets were fed into deep learning neural networks namely convolutional neural network (CNN), long-short term memory (LSTM) neural networks, and convolutional LSTM (ConvLSTM) neural networks. The performance of each neural network for each category type was separately examined. The results show that ConvLSTM outperforms CNN and LSTM as far as activity recognition is concerned.