MMTSA: Multi-Modal Temporal Segment Attention Network for Efficient Human Activity Recognition

Ziqi Gao, Jianguo Chen, Xin Liu, Yuntao Wang, Yuanchun Shi

Keywords: Multimodal Learning, Human Activity Recognition

Abstract: Multimodal sensors (e.g., visual, non-visual, and wearable) provide complementary information to develop robust perception systems for recognizing activities. However, most existing algorithms use dense sampling and heterogeneous sub-network to extract unimodal features and fuse them at the end of their framework, which causes data redundancy, lack of multimodal complementary information and high computational cost. In this paper, we propose a new novel multi-modal neural architecture based on RGB and IMU wearable sensors (e.g., accelerometer, gyroscope) for human activity recognition called Multimodal Temporal Segment Attention Network (MMTSA). MMTSA first employs a multimodal data isomorphism mechanism based on Gramian Angular Field (GAF) and then applies a novel multimodal sparse sampling method to reduce redundancy. Moreover, we propose an inter-segment attention module in MMTSA to fuse multimodal features effectively and efficiently. We demonstrate the importance of imu data imaging and attention mechanism in human activity recognition by rigours evaluation on three public datasets, and achieved superior improvements ($11.13\%$ on the MMAct dataset) than the previous state-of-the-art methods.

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