TinyHAR: A Lightweight Deep Learning Model Designed for Human Activity Recognition
Open accessAbstract
Deep learning models have shown excellent performance in human activity recognition tasks. However, these models typically require large amounts of computational resources, which makes them inefficient to deploy on edge devices. Furthermore, the superior performance of deep learning models relies heavily on the availability of large datasets to avoid over-fitting. However, the expensive efforts for labeling limits the amount of datasets. We address both challenges by designing a more lightweight model, called TinyHAR. TinyHAR is designed specifically for human activity recognition employing different saliency of multi modalities, multimodal collaboration, and temporal information extraction. Initial experimental results show that TinyHAR is several times smaller and often meets or even surpasses the performance of DeepConvLSTM, a state-of-the-art human activity recognition model.
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When resources are less constrained, efficient Deep Learning based classifiers can be employed, e.g., TinyHAR and MLP-HAR.
“When resources are less constrained, efficient Deep Learning based classifiers can be employed, e.g., TinyHAR [222] and MLP-HAR [220].”
From Past, Present, and Future of Sensor-based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task (2025) · cites this paper as [222]
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Cites
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“Similarly, the WISDM dataset was collected from 36 subjects through controlled, laboratory conditions, which reduces the effect of generalizability.”
MethodInfluentialDeep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 3× · see citing works
“DeepConvLSTM [21], a popular benchmark HAR model, first used a CNN subnet to extract local features from different sensor modalities and feed the extracted features into a long-short-term-memory (LSTM).”
BackgroundMethodThe Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
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“Compared to other datasets, dataset Opportunity has much more sensor channels.”
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Cited by
Sorted by citations
Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Plötz, T. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3729467 · Cited by 33 · see citing works
Continual Egocentric Activity Recognition With Foreseeable-Generalized Visual–IMU Representations
He, C., Cheng, S., Qiu, Z., Xu, L., Meng, F., Wu, Q., & Li, H. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3371975 · Cited by 11 · see citing works
Towards Customizable Foundation Models for Human Activity Recognition with Wearable Devices
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Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Ploetz, T. (2024) · arXiv · Cited by 31 · see citing works
Calatrava-Nicolás, F. M., Stoyanov, T., & Martinez Mozos, O. (2026) · Scientific Reports · doi:10.1038/s41598-026-64846-5
- Authors
- Zhou, Y., Zhao, H., Huang, Y., Riedel, T., Hefenbrock, M., & Beigl, M.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Venue
- Proceedings of the 2022 ACM International Symposium on Wearable Computers
- Publisher
- ACM
- Published
- 2022-09-11
- Pages
- 89-93
- OpenAlex
- W4312889818
- Cited by
- 78
- References
- 4
- Citations by year
- 2026: 26 · 2025: 26 · 2024: 19 · 2023: 7
- FWCI
- 4.71
- Type
- conference paper
- Language
- en
- Open access
- Locations
- ACM International Symposium on Wearable Computers (ISWC) (publishedVersion)
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion)
- Funders
- German Ministry of Research and Education (SDIL (01IS19030A))
- Ministry of Economic Affairs, Labour and Tourism Baden-Wuerttemberg (CC-KING)
- Carl-Zeiss-Foundation (Jubot)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- human activity recognition, lightweight deep learning, edge deployment, multimodal fusion, temporal information extraction, model compression, small labeled datasets
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