Multi-sensor Fusion Framework for HAR: Integrating Time-Frequency Features and Self-supervised Learning

doi:10.1007/978-981-95-3055-7_6

Multi-sensor Fusion Framework for HAR: Integrating Time-Frequency Features and Self-supervised Learning

2026-01-01

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DOI：10.1007/978-981-95-3055-7_6

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The increasing ubiquity of the Internet of Things (IoT) and smart devices equipped with embedded human body sensors has intensified the focus on Human Activity Recognition (HAR). However, HAR, which relies on sensor data, faces challenges related to feature extraction and data correlation. To address these issues, our paper proposes a Multi-Sensor Fusion Network (MSFNet). This model leverages accelerometer and gyroscope time-domain data, transforms it into the frequency domain, and extracts features from both domains to enhance feature interaction through Self-Supervised Learning (SSL). MSFNet fuses time-frequency data and employs four encoders for feature extraction, along with two SSL tasks to improve classifier accuracy. Our experimental results demonstrate the models effectiveness and its competitive performance compared to existing benchmarks. The code can be accessed via the Github link https://github.com/bx12138/MSFNet

Keywords:

Human Activity Recognition

Self-supervised learning

Time-frequency domain transformation

Multi-Sensor Fusion

Multi-sensor Fusion Framework for HAR: Integrating Time-Frequency Features and Self-supervised Learning

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