パーソナライゼーションが重要な理由：EMG-IMUベースのHRI活動認識における被験者間課題
Why Personalization Matters: Cross-Subject Challenges in EMG-IMU-based HRI Activity Recognition
EMGとIMU信号を用いた人間とロボットのインタラクション（HRI）における活動・ジェスチャ認識を研究し、53クラスの新しいデータセットMAGIC-HRIを導入。被験者間の一般化ギャップを明らかにし、少数のサンプル注入によるパーソナライゼーションが認識性能を大幅に向上させることを示した。
著者: Ruan Rithelle Chagas de Faria Carminati, Giovanni Braglia, Luigi Biagiotti, Ronnier Frates Rohrich, Andre Schneider de Oliveira, Mikael Nedel Hartmann, André Eugenio Lazzaretti
分類: cs.RO
原文アブストラクト
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.