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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2508.05175 |
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| _version_ | 1866916885109407744 |
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| author | Russo, Stefania Klimas, Rafał Płonka, Marta Gall, Hugo Le Holm, Sven Stanev, Dimitar Lipsmeier, Florian Zanon, Mattia Kriara, Lito |
| author_facet | Russo, Stefania Klimas, Rafał Płonka, Marta Gall, Hugo Le Holm, Sven Stanev, Dimitar Lipsmeier, Florian Zanon, Mattia Kriara, Lito |
| contents | We developed a ResNet-based human activity recognition (HAR) model with minimal overhead to detect gait versus non-gait activities and everyday activities (walking, running, stairs, standing, sitting, lying, sit-to-stand transitions). The model was trained and evaluated using smartphone sensor data from adult healthy controls (HC) and people with multiple sclerosis (PwMS) with Expanded Disability Status Scale (EDSS) scores between 0.0-6.5. Datasets included the GaitLab study (ISRCTN15993728), an internal Roche dataset, and publicly available data sources (training only). Data from 34 HC and 68 PwMS (mean [SD] EDSS: 4.7 [1.5]) were included in the evaluation. The HAR model showed 98.4% and 99.6% accuracy in detecting gait versus non-gait activities in the GaitLab and Roche datasets, respectively, similar to a comparative state-of-the-art ResNet model (99.3% and 99.4%). For everyday activities, the proposed model not only demonstrated higher accuracy than the state-of-the-art model (96.2% vs 91.9%; internal Roche dataset) but also maintained high performance across 9 smartphone wear locations (handbag, shopping bag, crossbody bag, backpack, hoodie pocket, coat/jacket pocket, hand, neck, belt), outperforming the state-of-the-art model by 2.8% - 9.0%. In conclusion, the proposed HAR model accurately detects everyday activities and shows high robustness to various smartphone wear locations, demonstrating its practical applicability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05175 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Human Activity Recognition from Smartphone Sensor Data for Clinical Trials Russo, Stefania Klimas, Rafał Płonka, Marta Gall, Hugo Le Holm, Sven Stanev, Dimitar Lipsmeier, Florian Zanon, Mattia Kriara, Lito Machine Learning 68T10 (Primary), 62P10 (Secondary) I.2.1 We developed a ResNet-based human activity recognition (HAR) model with minimal overhead to detect gait versus non-gait activities and everyday activities (walking, running, stairs, standing, sitting, lying, sit-to-stand transitions). The model was trained and evaluated using smartphone sensor data from adult healthy controls (HC) and people with multiple sclerosis (PwMS) with Expanded Disability Status Scale (EDSS) scores between 0.0-6.5. Datasets included the GaitLab study (ISRCTN15993728), an internal Roche dataset, and publicly available data sources (training only). Data from 34 HC and 68 PwMS (mean [SD] EDSS: 4.7 [1.5]) were included in the evaluation. The HAR model showed 98.4% and 99.6% accuracy in detecting gait versus non-gait activities in the GaitLab and Roche datasets, respectively, similar to a comparative state-of-the-art ResNet model (99.3% and 99.4%). For everyday activities, the proposed model not only demonstrated higher accuracy than the state-of-the-art model (96.2% vs 91.9%; internal Roche dataset) but also maintained high performance across 9 smartphone wear locations (handbag, shopping bag, crossbody bag, backpack, hoodie pocket, coat/jacket pocket, hand, neck, belt), outperforming the state-of-the-art model by 2.8% - 9.0%. In conclusion, the proposed HAR model accurately detects everyday activities and shows high robustness to various smartphone wear locations, demonstrating its practical applicability. |
| title | Human Activity Recognition from Smartphone Sensor Data for Clinical Trials |
| topic | Machine Learning 68T10 (Primary), 62P10 (Secondary) I.2.1 |
| url | https://arxiv.org/abs/2508.05175 |