SKS-Transformer: Multi-scale and Direction-aware Attention for Inertial Sensor-based Activity Recognition
Date
Authors
Feng, Chengwei
Bačić, Boris
Li, Weihua
Xu, Hongqi
Supervisor
Item type
Journal Article
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Journal Title
Journal ISSN
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Publisher
Frontiers Media S.A.
Abstract
Introduction: Human Activity Recognition (HAR) has emerged as an enabling research field, with applications ranging from healthcare and sports analytics to smart environments. However, achieving scalable and accurate HAR systems that generalize across diverse activity scenarios remains a challenging problem. Methods: In this paper, we propose a scalable HAR system, which integrates a new model named SKS-Transformer with a custom-designed wearable Inertial Measurement Unit (IMU). The IMU combines an ESP8266 microcontroller and a JY61 sensor, enabling wireless acquisition of motion data. The proposed SKS-Transformer model incorporates Selective Kernel Networks and squeeze-enhanced axial attention modules to capture multiscale temporal dynamics and directional dependencies, respectively. The motion data preprocessing pipeline includes denoising, segmentation, and normalization. The preprocessed data are fused through a learnable gating mechanism, enabling the model to adaptively balance local and global motion patterns. Results: We evaluate the system scalability and performance on two public datasets (UCI-HAR and PAMAP2) and two captured datasets that feature both daily activities and fine-grained golf swing errors. Experimental results demonstrate that the SKS-Transformer model consistently surpasses the state of the art on both public datasets (by 0.3% and 0.09% compared to the best of 11 other published models) and by 2.86% and 0.46%, achieving the accuracy of up to 98.10% on collected HAR data, as well as 100% accuracy in golf swing error detection. Discussion: Ablation studies of SKS-Transformer confirm the contribution of each architectural model component to overall model performance and provide further insights for future optimizations. Future work will investigate the applications of the SKS-Transformer-based system in extended real-world scenarios, including intelligent healthcare, sports performance monitoring, and wearable computing. The source code for our proposed method has been released publicly and is available on GitHub at: URL: https://github.com/cw-feng/SKS-Transformer-Multi-scale-and-direction-aware-attention-for-activity-recognitionDescription
Keywords
Human Activity Recognition (HAR), IoT, biomechanics, inertial data classification, open source (OS), privacy & security, transformer, wearable IMU sensors, 42 Health Sciences, 4207 Sports Science and Exercise, Networking and Information Technology R&D (NITRD), Bioengineering, 4207 Sports science and exercise
Source
Frontiers in Sports and Active Living, ISSN: 2624-9367 (Print); 2624-9367 (Online), Frontiers Media S.A., 8, 1754717-. doi: 10.3389/fspor.2026.1754717
Rights statement
© 2026 Feng, Bačić, Li and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.