# RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online

arXiv: https://arxiv.org/abs/2508.21167 · HTML: https://mlsanity.com/paper/2508.21167/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [A Survey of mmWave-based Human Sensing: Technology, Platform and Applications](https://mlsanity.com/paper/2308.03149/) (2023)
- [Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos](https://mlsanity.com/paper/1810.08691/) (2018)
- [HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders](https://mlsanity.com/paper/2103.06375/) (2021)
- [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025)
- [On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study](https://mlsanity.com/paper/2404.02461/) (2024)
- [hear-your-action: human action recognition by ultrasound active sensing](https://mlsanity.com/paper/2309.08087/) (2023)

## Abstract (arXiv)

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.