Abstract
Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.
Highlights
human activity recognition (HAR) has received a lot of attention in recent years for its applications in smart homes, fall detection for the elderly, sports training, medical rehabilitation, and misbehavior recognition [1,2]
In this study, we proposed a new deep learning (DL) model which cascades a residual network with bi-directional Long Short-Term Memory (LSTM) (BiLSTM)
We introduce the BiLSTM into residually connected convolutions (ResNet) to extract the forward and backward dependencies of feature sequence which is useful to improve the performance of the network
Summary
HAR has received a lot of attention in recent years for its applications in smart homes, fall detection for the elderly, sports training, medical rehabilitation, and misbehavior recognition [1,2]. Duc et al [12] designed a HAR system based on SVM by extracting features to recognize six activities Even though these methods achieve good results in some datasets, certain human experience is required for extracting hand-crafted features which results in a limited accuracy [6]. Nafea et al [25] proposed a model using CNN with varying kernel dimensions along with BiLSTM to obtain features at different resolutions It has a high accuracy on the WISDM and UCI datasets. A new model, combining the ResNet with BiLSTM, is proposed to capture the spatial and temporal feature of sensor data. The rationality of this model is explained from the perspective of human lower limb movement and the corresponding IMU signal.
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