arXiv:2606. 23771v1 Announce Type: cross Abstract: Extended Reality (XR) presents a challenging use case for 5G and 6G networks, requiring high data-rates and lowlatency communication to deliver a truly immersive experience.
By Nabeel Nisar Bhat, Javad Sameri, Rreze Halili, Rafael Berkvens, Maria Torres Vega, Jeroen Famaey
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:2512. 07997v2 Announce Type: replace-cross Abstract: Gestures are an integral part of our daily interactions with the environment.
By Soroush Baghernezhad, Elaheh Mohammadreza, Vinicius Prado da Fonseca, Ting Zou, Xianta Jiang
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:2606. 15004v1 Announce Type: cross Abstract: Deploying neural networks on low-power microcontrollers (MCUs) requires selecting model architectures under tight memory, latency, and energy constraints.
By Joseph Q. Zales, Pragya Sharma, Mani Srivastava
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