arXiv Machine Learning

One Residual with Three Reuses: A Wristband Front End for Gesture Sensing

arXiv:2608. 16542v1 Announce Type: new Abstract: Continuous wrist-worn hand sensing for gesture interfaces and motor symptom monitoring needs an always-on front end that fits inside a coin-cell power budget while pairing a micro-electro-mechanical-systems (MEMS) inertial measurement unit (IMU) with a 60 GHz frequency-modulated continuous-wave (FMCW) radar to stay robust under occlusion and on-body drift.

arXiv AI
Sep 25

When Temporal Perturbations Act Like Sensor Biases: Label-Free Auditing of Wearable Activity Recognizers

The paper introduces SpectrumAudit, a label‑sealed auditing method for wearable human‑activity recognition models that uses phase‑randomized full‑window stimuli to probe sensor biases. By replaying the DC component and a zero‑mean residual on held‑out subjects, the audit demonstrates significant accuracy drops across 27 victim models, with DC perturbations proving more harmful than AC in most cases. The study also shows that the audit can distinguish between persistent sensor offsets and zero‑mean variations under a fixed peak‑budget.

By Qingyu Wu, Yuan Wei, Renju Liu, Hua Cheng
arXiv Machine Learning
Aug 18

Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables

arXiv:2503. 07825v3 Announce Type: replace-cross Abstract: We present an advance in wearable technology: a mobile-optimized, real-time, ultra-low-power event camera system that enables natural hand gesture control for smart glasses, dramatically improving user experience.

By Prarthana Bhattacharyya, Joshua Mitton, Ryan Page, Owen Morgan, Oliver Powell, Benjamin Menzies, Gabriel Homewood, Kemi Jacobs, Paolo Baesso, Taru Muhonen, Richard Vigars, Louis Berridge
arXiv Machine Learning
Aug 27

Dynamic Influence-Weighted Distillation for Single-IMU Activity Recognition

The paper proposes Dynamic Influence-Weighted Distillation (DIW) to improve single-IMU activity recognition by leveraging a frozen four-IMU teacher during training. DIW assigns sample-wise gates to logit and feature losses, outperforming both supervised learning and fixed-weight knowledge distillation on the WEAR dataset with a macro‑F1 of 0.638451. The method enhances a right‑arm IMU model without altering the deployed sensor setup or the student network architecture.

By Bingxuan Xie
arXiv Machine Learning
Aug 21

A comparison between ceiling-mounted FMCW, IR-UWB and Wi-Fi radar for in-bedroom human activity monitoring and sleep interruption detection

arXiv:2608. 20322v1 Announce Type: new Abstract: Despite their growing importance for contact-free radio frequency (RF) based healthcare monitoring, different radio technologies such as frequency-modulated continuous wave (FMCW) radar, impulse radio ultra-wideband (IR-UWB), and Wi-Fi sensing are rarely compared under identical deployment conditions, as existing studies typically differ in hardware, datasets, and evaluation methodologies.

By Anton Lambrecht, Reda El Hail, Xianjun Jiao, Pieter Crombez, Dominique Schreurs, Peter Karsmakers, Adnan Shahid, Eli De Poorter
Hugging Face Trending Papers
5d ago

One Sensor, Whole Body - 3D Body Pose from a Single Consumer Earbud IMU

The study investigates how well a single consumer earbud IMU can estimate 3D body pose and whether adding foot IMUs improves accuracy. Using a multimodal capture pipeline with RGB‑D video, an AirPods head IMU, and Striv insole IMUs, the authors benchmark pose estimation across various motions and train recurrent models (IMUPoser and MobilePoser). Results show that a head IMU alone achieves 79.0 mm rigid‑MPJPE and 0.809 macro‑F1 for foot contact, while adding foot IMUs does not significantly improve pose and can even degrade performance due to insole orientation errors.

arXiv Machine Learning
Jun 9

AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection

arXiv:2602. 08916v3 Announce Type: replace-cross Abstract: Objective: Acute mountain sickness (AMS) is the most prevalent altitude illness, affecting unacclimatized individuals ascending above 2,500 m and potentially escalating to life threatening cerebral or pulmonary edema.

By Abu Masum, Mehran Moghadam, M. Hassan Najafi, Bige Unluturk, Ulkuhan Guler, Beth A. Beidleman, Sercan Aygun