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Edge-Based ADL Recognition Using Room-Specialized Mixture-of-Experts
Addepalli Vr, Rao P, Lee K.. medRxiv, 2026-07-27
Topic: Health services, long-term care and technology
Activities of daily living (ADLs) provide important indicators of functional decline in people living with dementia, motivating the need for continuous in-home monitoring. However, deploying transformer-based activity recognition models on resource-constrained edge devices remains challenging because of limited computational resources and the need to preserve participant privacy by avoiding cloud-based processing. In this work, we propose a room-specialized Mixture-of-Experts (MoE) architecture for edge-based ADL recognition using ambient smart home sensors. Household activities are decomposed into room-specific transformer experts through deterministic routing, while temporal subsampling bounds the computational cost of each activity segment, enabling efficient on-device learning and inference. We evaluated the proposed framework using data collected from five dementia households, achieving Macro-F1 scores ranging from 0.437 to 0.911 despite substantial differences in activity distributions across homes. End-to-end training was successfully performed on a Raspberry Pi, demonstrating the feasibility of transformer-based ADL recognition on low-cost edge hardware. These findings suggest that room-specialized MoE provides a practical, privacy-preserving framework for continuous smart home monitoring and establishes a foundation for future edgenative healthcare applications, including continual and federated learning.
Key findings
- These findings suggest that room-specialized MoE provides a practical, privacy-preserving framework for continuous smart home monitoring and establishes a foundation for future edgenative healthcare applications, including continual and federated learning.
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Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 13 of the 62 points we could judge it on, which is 21 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 13 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 13 of the 62 points we could judge it on is 21 out of 100. |
| Total | 21 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Vr, A., Rao, P., & Lee, K. (2026). Edge-Based ADL Recognition Using Room-Specialized Mixture-of-Experts. medRxiv. https://doi.org/10.64898/2026.07.24.26357042
Vancouver
Vr A, Rao P, Lee K. Edge-Based ADL Recognition Using Room-Specialized Mixture-of-Experts. medRxiv. 2026. doi:10.64898/2026.07.24.26357042.
BibTeX
@article{vr2026-aaf3e626,
author = {Vr, Addepalli and Rao, P. and Lee, K.},
title = {{Edge-Based ADL Recognition Using Room-Specialized Mixture-of-Experts}},
journal = {medRxiv},
year = {2026},
doi = {10.64898/2026.07.24.26357042},
url = {https://doi.org/10.64898/2026.07.24.26357042}
}
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