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Author

A. Lazzaretti

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2 papers indexed here

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Preprint Aug 2026

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

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.

Ruan Rithelle Chagas de Faria Carminati, Giovanni Braglia, L. Biagiotti et al. · 0 citations
Conference Jul 2026

Learning-Based Motion Estimation for Autonomous Inspection Robots: Temporally Aligned IMU–RTK Data Fusion

This work addresses the problem of increasing the temporal resolution of robot positioning in outdoor inspection tasks by leveraging high-frequency inertial measurements. A learning-based approach is proposed to estimate incremental displacement from IMU data combined with GNSS/RTK positioning, using data collected along a predefined trajectory with a mobile robotic platform. Two neural architectures, LSTM and Transformer, are evaluated under different data preparation strategies. Offline validation shows that variations in hyperparameters have limited impact on performance, while the adopted data representation plays a more significant role, with high-resolution IMU–GNSS alignment outperforming feature-based approaches. The selected models were deployed on the robotic platform and tested in the same environment used for data collection, demonstrating real-time operation at the IMU sampling rate and achieving mean errors of approximately 0.140 m and 0.113 m for LSTM and Transformer, respectively. These results indicate that the proposed approach can enhance positioning update rates and support real-time motion estimation in robotic inspection tasks.

José Mario Nishihara de Albuquerque, João Henrique Campos Soares, Giovanni Braglia et al. · 0 citations