Human activity recognition with inertial sensors using a deep learning approach

Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. The influence of various important hyper-parameters such as number of convolutional layers and kernel size on the performance of CNN was monitored. Experimental results indicate that CNNs achieved significant speed-up in computing and deciding the final class and marginal improvement in overall classification accuracy compared to the baseline models such as Support Vector Machines and Multi-layer perceptron networks

Movement and gesture recognition using deep learning and wearable-sensor technology

Pattern recognition of time-series signals for movement and gesture analysis plays an important role in many fields as diverse as healthcare, astronomy, industry and entertainment. As a new technique in recent years, Deep Learning (DL) has made tremendous progress in computer vision and Natural Language Processing (NLP), but largely unexplored on its performance for movement and gesture recognition from noisy multi-channel sensor signals. To tackle this problem, this study was undertaken to classify diverse movements and gestures using four developed DL models: a 1-D Convolutional neural network (1-D CNN), a Recurrent neural network model with Long Short Term Memory (LSTM), a basic hybrid model containing one convolutional layer and one recurrent layer (C-RNN), and an advanced hybrid model containing three convolutional layers and three recurrent layers (3+3 C-RNN). The models will be applied on three different databases (DB) where the performances of models were compared. DB1 is the HCL dataset which includes 6 human daily activities of 30 subjects based on accelerometer and gyroscope signals. DB2 and DB3 are both based on the surface electromyography (sEMG) signal for 17 diverse movements. The evaluation and discussion for the improvements and limitations of the models were made according to the result