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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.21167 |
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| _version_ | 1866911129424363520 |
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| author | Lee, Dong Yoon Weakley, Alyssa Wei, Hui Brown, Blake Carrion, Keyana Pan, Shijia |
| author_facet | Lee, Dong Yoon Weakley, Alyssa Wei, Hui Brown, Blake Carrion, Keyana Pan, Shijia |
| contents | One in four people dementia live alone, leading family members to take on caregiving roles from a distance. Many researchers have developed remote monitoring solutions to lessen caregiving needs; however, limitations remain including privacy preserving solutions, activity recognition, and model generalizability to new users and environments. Structural vibration sensor systems are unobtrusive solutions that have been proven to accurately monitor human information, such as identification and activity recognition, in controlled settings by sensing surface vibrations generated by activities. However, when deploying in an end user's home, current solutions require a substantial amount of labeled data for accurate activity recognition. Our scalable solution adapts synthesized data from near-surface acoustic audio to pretrain a model and allows fine tuning with very limited data in order to create a robust framework for daily routine tracking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online Lee, Dong Yoon Weakley, Alyssa Wei, Hui Brown, Blake Carrion, Keyana Pan, Shijia Sound Machine Learning I.5.4 One in four people dementia live alone, leading family members to take on caregiving roles from a distance. Many researchers have developed remote monitoring solutions to lessen caregiving needs; however, limitations remain including privacy preserving solutions, activity recognition, and model generalizability to new users and environments. Structural vibration sensor systems are unobtrusive solutions that have been proven to accurately monitor human information, such as identification and activity recognition, in controlled settings by sensing surface vibrations generated by activities. However, when deploying in an end user's home, current solutions require a substantial amount of labeled data for accurate activity recognition. Our scalable solution adapts synthesized data from near-surface acoustic audio to pretrain a model and allows fine tuning with very limited data in order to create a robust framework for daily routine tracking. |
| title | RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online |
| topic | Sound Machine Learning I.5.4 |
| url | https://arxiv.org/abs/2508.21167 |