Floor Plan-Agnostic Detection of Gait Speed Drifts Using Ambient Sensors
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911639660396544 |
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| author | Vicini, Marina Rudorfer, Martin Dai, Zhuangzhuang Beltagui, Ahmad Manso, Luis J. |
| author_facet | Vicini, Marina Rudorfer, Martin Dai, Zhuangzhuang Beltagui, Ahmad Manso, Luis J. |
| contents | Gait speed is a vital health indicator for older adults, as changes in gait speed can reflect physiological and functional decline. Ambient sensors offer a promising, privacy-preserving solution for continuous in-home monitoring of gait speed; although it is often limited by methods requiring a home floor plan, which is frequently unfeasible. This paper proposes a novel, floor plan-agnostic method to detect gait speed drifts using only sparse ambient sensors. Our approach identifies informative sensor-to-sensor transitions and analyses fluctuations in their duration. For each sequence a non-parametric statistical test detects changes between a recent period and an initial baseline; and daily test results are aggregated to provide a robust drift detection response. We evaluate our method on a simulated dataset across four different home layouts, showing performance comparable to, and in some cases exceeding, a state-of-the-art baseline that requires floor plan information. This work demonstrates a feasible approach for scalable, cost effective gait drift detection monitoring, providing a foundation for future validation in complex real-world environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00900 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Floor Plan-Agnostic Detection of Gait Speed Drifts Using Ambient Sensors Vicini, Marina Rudorfer, Martin Dai, Zhuangzhuang Beltagui, Ahmad Manso, Luis J. Signal Processing Gait speed is a vital health indicator for older adults, as changes in gait speed can reflect physiological and functional decline. Ambient sensors offer a promising, privacy-preserving solution for continuous in-home monitoring of gait speed; although it is often limited by methods requiring a home floor plan, which is frequently unfeasible. This paper proposes a novel, floor plan-agnostic method to detect gait speed drifts using only sparse ambient sensors. Our approach identifies informative sensor-to-sensor transitions and analyses fluctuations in their duration. For each sequence a non-parametric statistical test detects changes between a recent period and an initial baseline; and daily test results are aggregated to provide a robust drift detection response. We evaluate our method on a simulated dataset across four different home layouts, showing performance comparable to, and in some cases exceeding, a state-of-the-art baseline that requires floor plan information. This work demonstrates a feasible approach for scalable, cost effective gait drift detection monitoring, providing a foundation for future validation in complex real-world environments. |
| title | Floor Plan-Agnostic Detection of Gait Speed Drifts Using Ambient Sensors |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.00900 |