Abstract
Systems of sensor human activity recognition are becoming increasingly popular in diverse fields such as healthcare and security. Yet, developing such systems poses inherent challenges due to the variations and complexity of human behaviors during the performance of physical activities. Recurrent neural networks, particularly long short-term memory have achieved promising results on numerous sequential learning problems, including sensor human activity recognition. However, parallelization is inhibited in recurrent networks due to sequential operation and computation that lead to slow training, occupying more memory and hard convergence. One-dimensional convolutional neural network processes input temporal sequential batches independently that lead to effectively executed operations in parallel. Despite that, a one-dimensional Convolutional Neural Network is not sensitive to the order of the time steps which is crucial for accurate and robust systems of sensor human activity recognition. To address this problem, we propose a network architecture based on dilated causal convolution and multi-head self-attention mechanisms that entirely dispense recurrent architectures to make efficient computation and maintain the ordering of the time steps. The proposed method is evaluated for human activities using smart home binary sensors data and wearable sensor data. Results of conducted extensive experiments on eight public and benchmark HAR data sets show that the proposed network outperforms the state-of-the-art models based on recurrent settings and temporal models.
Highlights
Human activity recognition (HAR) is a significant research field in ubiquitous computing for monitoring behaviors of people which plays an important role in various applications such as healthcare monitoring [1], security surveillance system [2] and resident situation assessment [3]
The proposed method improved the results of HAR by 5% up to 7% compared with long short-term memory (LSTM), 1D CNN, hybrid 1D CNN ? LSTM, CudNNLSTM, and Bidirectional LSTM and reduced the training time
The results indicate that dilated causal convolution with self-attention can effectively improve the performance of HAR systems and reduce the training time
Summary
Human activity recognition (HAR) is a significant research field in ubiquitous computing for monitoring behaviors of people which plays an important role in various applications such as healthcare monitoring [1], security surveillance system [2] and resident situation assessment [3]. The aim of HAR is to identify and recognize simple and complex human daily activities using smart home and wearable sensors data. LSTM processes temporal data using forget gate, input gate, and output gate to append or delete information to the cell state throughout the processing of the sequence data. Each LSTM cell works as a memory to remove, read, and write information that is controlled by the forget, output, and input gates, respectively. Forget gate process both inputs the previous output htÀ1 and new time step Xt using sigmoid activation function to indicate relevant or irrelevant information. The sigmoid activation function processes both the previous hidden state htÀ1 and the current input timestep xt to produce the output gate
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