Why Personalization Matters: Cross-Subject Challenges in EMG-IMU-based HRI Activity Recognition

2026-08-21

AI Summary

研究探讨了基于EMG-IMU信号的人类活动识别在人机交互中的应用，通过构建MAGIC-HRI数据集和使用多种分类器，强调个性化适应的重要性以提高识别准确性。

Abstract

This paper investigates wearable-based recognition of human activities and gestures to support Human-Robot Interaction (HRI) in object-handover and assembly-like scenarios. Electromyography (EMG) and Inertial Measurement Unit (IMU) signals were collected using a Myo armband, culminating in a novel dataset introduced as MAGIC-HRI (Multimodal Activity, Gesture and Intention Collection) with a large taxonomy of 53 movement classes, including Brazilian Sign Language (LIBRAS) numbers (0-9), hand gestures, object/tool handover actions (pick up/give/hold), tool-manipulation tasks, and generic assembly/idle motions, collected from 11 participants with 10 samples per class (530 samples per participant). Signals are segmented by detecting muscle activation via an EMG energy envelope, then processed using sliding windows; time- and frequency-domain features are extracted. Multiple classical classifiers are tuned via cross-validated grid search, with Random Forest as the strongest baseline. A Leave-One-Subject-Out (LOSO) protocol reveals a large generalization gap, indicating substantial subject dependence. A personalized adaptation experiment suggests that injecting a small number of samples from a new user can markedly improve recognition. Overall, the study contributes a broad, HRI-driven multimodal dataset, a rigorous evaluation emphasizing generalization, and practical evidence that personalization is likely required for robust deployment in practical HRI.

Problem

• Personalization
• Cross-Subject
• EMG-IMU
• Human-Robot Interaction
• Activity Recognition

Innovation

• Personalization
• Cross-Subject Challenges
• EMG-IMU
• HRI
• MAGIC-HRI