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IF-ConvTransformer: A Framework for Human Activity Recognition Using IMU Fusion and ConvTransformer

Authors:

Longguang Wang,

Yulan GuoAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 6, Issue 2

Article No.: 88, Pages 1 - 26

https://doi.org/10.1145/3534584

Published: 07 July 2022 Publication History

Abstract

Recent advances in sensor based human activity recognition (HAR) have exploited deep hybrid networks to improve the performance. These hybrid models combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to leverage their complementary advantages, and achieve impressive results. However, the roles and associations of different sensors in HAR are not fully considered by these models, leading to insufficient multi-modal fusion. Besides, the commonly used RNNs in HAR suffer from the 'forgetting' defect, which raises difficulties in capturing long-term information. To tackle these problems, an HAR framework composed of an Inertial Measurement Unit (IMU) fusion block and an applied ConvTransformer subnet is proposed in this paper. Inspired by the complementary filter, our IMU fusion block performs multi-modal fusion of commonly used sensors according to their physical relationships. Consequently, the features of different modalities can be aggregated more effectively. Then, the extracted features are fed into the applied ConvTransformer subnet for classification. Thanks to its convolutional subnet and self-attention layers, ConvTransformer can better capture local features and construct long-term dependencies. Extensive experiments on eight benchmark datasets demonstrate the superior performance of our framework. The source code will be published soon.

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Index Terms

IF-ConvTransformer: A Framework for Human Activity Recognition Using IMU Fusion and ConvTransformer
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 6, Issue 2

July 2022

1551 pages

EISSN:2474-9567

DOI:10.1145/3547347

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Published: 07 July 2022

Published in IMWUT Volume 6, Issue 2

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Li WGao RXiong JZhou JWang LMao XYi EZhang D(2024)WiFi-CSI Difference ParadigmProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596088:2(1-29)Online publication date: 15-May-2024
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Jeong DHan K(2024)PRECYSE: Predicting Cybersickness using Transformer for Multimodal Time-Series Sensor DataProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595948:2(1-24)Online publication date: 15-May-2024
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Zhou YZhao HHuang YRöddiger TKurnaz MRiedel TBeigl M(2024)AutoAugHAR: Automated Data Augmentation for Sensor-based Human Activity RecognitionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595898:2(1-27)Online publication date: 15-May-2024
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