7 papers · 1 filter
VSMP-IMU: Video-Grounded Semantic Motion Programs for Sensor-Aware Synthetic IMU Generation
Lala Shakti Swarup Ray, Vitor Fortes Rey, Mengxi Liu +2
Wearable human activity recognition (HAR) is often limited by the scarcity of labeled sensor data, especially in low-resource, class-imbalanced, and subject-generalization settings…
From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation
Mengxi Liu, Lala Shakti Swarup Ray, Sizhen Bian +6
We present NeckSense, a novel wearable system for head pose tracking that leverages multi-channel bio-impedance sensing with soft, dry electrodes embedded in a lightweight, necklac…
TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Sizhen Bian, Mengxi Liu, Vitor Fortes Rey +2
Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces Tini…
SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models
Lala Shakti Swarup Ray, Mengxi Liu, Deepika Gurung +3
Human Activity Recognition (HAR) with wearable sensors is essential for applications in healthcare, fitness, and human-computer interaction. Bio-impedance sensing offers unique adv…
Boosting Classification with Quantum-Inspired Augmentations
Matthias Tschöpe, Vitor Fortes Rey, Sogo Pierre Sanon +3
Understanding the impact of small quantum gate perturbations, which are common in quantum digital devices but absent in classical computers, is crucial for identifying potential ad…
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition
Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh +2
Human activity recognition (HAR) with deep learning models relies on large amounts of labeled data, often challenging to obtain due to associated cost, time, and labor. Self-superv…