# OESense: Employing Occlusion Effect for In-ear Human Sensing

arXiv: https://arxiv.org/abs/2106.08607 · HTML: https://mlsanity.com/paper/2106.08607/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2021.

## Built on by (most cited first, 5 of 5)

- [Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables](https://mlsanity.com/paper/2311.00320/) (2023)
- [UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers](https://mlsanity.com/paper/2402.09264/) (2024)
- [Earables for Detection of Bruxism: a Feasibility Study](https://mlsanity.com/paper/2108.04144/) (2021)
- [OmniBuds: A Sensory Earable Platform for Advanced Bio-Sensing and On-Device Machine Learning](https://mlsanity.com/paper/2410.04775/) (2024)
- [AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers](https://mlsanity.com/paper/2605.11901/) (2026)

## Abstract (arXiv)

Smart earbuds are recognized as a new wearable platform for personal-scale human motion sensing. However, due to the interference from head movement or background noise, commonly-used modalities (e.g. accelerometer and microphone) fail to reliably detect both intense and light motions. To obviate this, we propose OESense, an acoustic-based in-ear system for general human motion sensing. The core idea behind OESense is the joint use of the occlusion effect (i.e., the enhancement of low-frequency components of bone-conducted sounds in an occluded ear canal) and inward-facing microphone, which naturally boosts the sensing signal and suppresses external interference. We prototype OESense as an earbud and evaluate its performance on three representative applications, i.e., step counting, activity recognition, and hand-to-face gesture interaction. With data collected from 31 subjects, we show that OESense achieves 99.3% step counting recall, 98.3% recognition recall for 5 activities, and 97.0% recall for five tapping gestures on human face, respectively. We also demonstrate that OESense is compatible with earbuds' fundamental functionalities (e.g. music playback and phone calls). In terms of energy, OESense consumes 746 mW during data recording and recognition and it has a response latency of 40.85 ms for gesture recognition. Our analysis indicates such overhead is acceptable and OESense is potential to be integrated into future earbuds.