Light Residual Network for Human Activity Recognition using Wearable Sensor Data
Open accessAbstract
This paper addresses the problem of Human Activity Recognition (HAR) of people wearing inertial sensors using data from the UCI-HAR dataset. We propose a light residual network which obtains an F1-Score of 97.6% that outperforms previous works, while drastically reducing the number of parameters by a factor of 15, and thus the training complexity. In addition, we propose a new benchmark based on leave-one(person)-out cross-validation to standardize and unify future classifications on the same dataset, and to increase reliability and fairness in the comparisons. In addition, we propose a common bechmark for the UCI-HAR dataset based on Leaving On (Participant) Out Cross-Validation (LOOCV) to justify the robustness of the architecture proposed and providing a fairer approach for comparisons.
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HAR suffers from poor generalization to unseen users, largely driven by data heterogeneity such as inter-subject variability.
“However, HAR often suffers from poor generalization to unseen users due to data heterogeneity across different individuals and conditions.”
From Siamese masked reconstruction with temporal rotation consistency for inertial-based human activity recognition (2026) · cites this paper as [16]
Supervised models trained only on activity labeled data still struggle to generalise to unseen users.
“Deep learning has greatly advanced HAR in recent years, yet supervised models trained only on activity labeled data still struggle to generalise to unseen users.”
From Siamese masked reconstruction with temporal rotation consistency for inertial-based human activity recognition (2026) · cites this paper as [16]
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- Authors
- Calatrava-Nicolás, F. M., & Mozos, O. M.
- Affiliations
- Örebro University (SE)
- AI for Life, Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden
- Venue
- IEEE Sensors Letters
- Published
- 2023-10
- Volume
- 7
- Issue
- 10
- Pages
- 1-4
- ISSN
- 2475-1472 (ISSN-L)
- OpenAlex
- W4386432007
- Cited by
- 22
- References
- 32
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 7 · 2023: 1
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Sensors Letters (publishedVersion, cc-by)
- Funders
- Wallenberg AI
- Autonomous Systems
- Software Program
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- human activity recognition, wearable sensors, inertial sensors, parameter reduction, UCI HAR dataset, benchmarking, training complexity
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