Abstract
Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.
Highlights
Human activity recognition (HAR) is very important for human–computer interaction and is an indispensable part of many current real-world applications
hand-crafted features (HC) comprises simple metrics computed on data and uses simple statistical values or frequency domain correlation features based on the signal Fourier transform to analyze the time series of human activity recognition data
For the UniMib-SHAR dataset, the multi-layer perceptron (MLP) method achieves the best performance among all the learning-based methods
Summary
Human activity recognition (HAR) is very important for human–computer interaction and is an indispensable part of many current real-world applications. The key technology in HAR includes a sliding time window of time-series data captured with on-body sensors, manually designed feature extraction procedures, and a wide variety of supervised learning methods. More and more researchers are using variants of CNNs to learn sensor-based data representations for human activity recognition and have achieved remarkable performances. In this paper, inspired by the facts above, we propose a model to handle a set of activity data, synchronized by an asymmetric net, using a short time window to capture spatial features and a long time window to capture fine temporal features, corresponding to the P-cells and the M-cells, respectively. We design a network that consists of an asymmetric residual net that can effectively manage information flow, but will automatically learn effective activity feature representation, while capturing the fine feature distribution in different activities from wearable sensor data.
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