Practically Adopting Human Activity Recognition
Open accessAbstract
Existing inertial measurement unit (IMU) based human activity recognition (HAR) approaches still face a major challenge when adopted across users in practice. The severe heterogeneity in IMU data significantly undermines model generalizability in wild adoption. This paper presents UniHAR, a universal HAR framework for mobile devices. To address the challenge of data heterogeneity, we thoroughly study augmenting data with the physics of the IMU sensing process and present a novel adoption of data augmentations for exploiting both unlabeled and labeled data. We consider two application scenarios of UniHAR, which can further integrate federated learning and adversarial training for improved generalization. UniHAR is fully prototyped on the mobile platform and introduces low overhead to mobile devices. Extensive experiments demonstrate its superior performance in adapting HAR models across four open datasets.
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LIMU-BERT is a foundation model training framework that proposes to make use of unlabeled data with masked language modeling solution.
“LIMU-BERT is a foundation model training framework that proposes to make use of the ever common unlabeled data with masked language modeling solution.”
From Towards Customizable Foundation Models for Human Activity Recognition with Wearable Devices (2025) · cites this paper as [57]
Section 8 (Related Work) describes LIMU-BERT as an early foundation model for extracting features from unlabeled IMU data. Section 5.1.1 says the authors embed LIMU-BERT in their design, which reconstructs randomly masked IMU readings. The paper does not call this a masked language modeling solution or clearly attribute that specific training method to LIMU-BERT itself. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA 3-dimensional rotation deterministically maps accelerometer and gyroscope signals across axes via a rotation matrix.
“Physically, a 3-dimensional rotation deterministically maps accelerometer and gyroscope signals across axes via a rotation matrix29.”
From Siamese masked reconstruction with temporal rotation consistency for inertial-based human activity recognition (2026) · cites this paper as [29]
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Sorted by citations
Hong, Z., Li, Z., Zhong, S., Lyu, W., Wang, H., Ding, Y., He, T., & Zhang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3659597 · Cited by 67 · see citing works
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UniMTS: Unified Pre-training for Motion Time Series
Zhang, X., Teng, D., Chowdhury, R. R., Li, S., Hong, D., Gupta, R. K., & Shang, J. (2024) · arXiv · Cited by 9 · see citing works
“The physics engine generates sufficient motion time series data, which are subsequently encoded by graph networks and aligned with their corresponding text embeddings through contrastive learning.”
Calatrava-Nicolás, F. M., Stoyanov, T., & Martinez Mozos, O. (2026) · Scientific Reports · doi:10.1038/s41598-026-64846-5
- Authors
- Xu, H., Zhou, P., Tan, R., & Li, M.
- Affiliations
- Nanyang Technological University (SG)
- University of Pittsburgh (US)
- Venue
- Proceedings of the 29th Annual International Conference on Mobile Computing and Networking
- Publisher
- ACM
- Published
- 2023-10-02
- Pages
- 1-15
- ISSN
- 1543-5679 (ISSN-L)
- OpenAlex
- W4387227531
- Cited by
- 55
- References
- 56
- Citations by year
- 2026: 14 · 2025: 26 · 2024: 14 · 2023: 1
- FWCI
- 5.89
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Annual International Conference on Mobile Computing and Networking (MobiCom) (publishedVersion)
- DR-NTU (Nanyang Technological University) (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Research Foundation, Singapore under its Industry Alignment Fund ? Pre-positioning (IAF-PP) Funding Initiative
- National Research Foundation, Singapore under its NRF Investigatorship (NRFI) NRF-NRFI08-2022-0010
- Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RG88/22)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, inertial measurement unit, cross-user generalization, data heterogeneity, unlabeled data, federated learning, adversarial training, mobile devices
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