Attention induced multi-head convolutional neural network for human activity recognition

• Published in: Applied soft computing Vol. 110; p. 107671
• Main Authors: Khan, Zanobya N., Ahmad, Jamil
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2021
• Subjects: Attention mechanism; Convolutional neural network; Human activity recognition; Inertial sensors; Squeeze-and-excitation module; Attention mechanism; Inertial sensors; Human activity recognition; Squeeze-and-excitation module; Convolutional neural network
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.107671

Deep neural networks, including convolutional neural networks (CNNs), have been widely adopted for human activity recognition in recent years. They have attained significant performance improvement over traditional techniques due to their strong feature representation capabilities. Some of the challenges faced by the HAR community is the non-availability of a substantial amount of labeled training samples, and the higher computational cost and system resources requirements of deep learning architectures as opposed to shallow learning algorithms. To address these challenges, we propose an attention-based multi-head model for human activity recognition (HAR). This framework contains three lightweight convolutional heads, with each head designed using one-dimensional CNN to extract features from sensory data. The lightweight multi-head model is induced with attention to strengthen the representation ability of CNN, allowing for automatic selection of salient features and suppress unimportant ones. We conducted ablation studies and experiments on two publicly available benchmark datasets: WISDM and UCI HAR, to evaluate our model. The experimental outcome demonstrates the effectiveness of the proposed framework in activity recognition and achieves better accuracy while ensuring computational efficiency. •Lightweight multi-head CNN proposed for sensors based HAR.•Attention mechanism is exploited to enhance feature extraction capability of CNN.•Competitive experimental outcome achieved on benchmark datasets