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

• URL: http://arxiv.org/abs/2608.21620v1
• Date: Fri, 21 Aug 2026 20:36:57 GMT
• Title: Why Personalization Matters: Cross-Subject Challenges in EMG-IMU-based HRI Activity Recognition
• Abstract summary: This paper investigates wearable-based recognition of human activities and gestures to support Human-Robot Interaction (HRI) in object-handover and assembly-like scenarios.<n>Electromyography (EMG) and Inertial Measurement Unit (IMU) signals were collected using a Myo armband.
• Score: 0.23453441553817042
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound [22.090898322586824]
  This work introduces HARMES, a multi-modal wearable dataset combining three wrist-recorded modalities.<n>HarMES totals over 80 hours of recorded data, with approximately three hours of labeled activity data per participant.<n>To the best of our knowledge, HARMES is the first dataset to combine this particular sensor trio.
  arXiv  Detail & Related papers  (2026-05-04T13:47:41Z)
• Zero-shot HOI Detection with MLLM-based Detector-agnostic Interaction Recognition [71.5328300638085]
  Zero-shot Human-object interaction (HOI) detection aims to locate humans and objects in images and recognize their interactions.<n>Existing methods, including two-stage methods, tightly couple interaction recognition with a specific detector.<n>We propose a decoupled framework that separates object detection from IR and leverages multi-modal large language models (MLLMs) for zero-shot IR.
  arXiv  Detail & Related papers  (2026-02-16T19:01:31Z)
• A Comparative Study of EMG- and IMU-based Gesture Recognition at the Wrist and Forearm [3.990794855710089]
  IMU signals contain sufficient information to serve as the sole input sensor for static gesture recognition.<n> tendon-induced micro-movement captured by IMUs is a major contributor to static gesture recognition.
  arXiv  Detail & Related papers  (2025-12-08T19:36:10Z)
• Improving Out-of-distribution Human Activity Recognition via IMU-Video Cross-modal Representation Learning [3.177649348456073]
  Human Activity Recognition (HAR) based on wearable inertial sensors plays a critical role in remote health monitoring.<n>We propose a new cross-modal self-supervised pretraining approach to learn representations from large-sale unlabeled IMU-video data.<n>Our results indicate that the proposed cross-modal pretraining approach outperforms the current state-of-the-art IMU-video pretraining approach.
  arXiv  Detail & Related papers  (2025-07-17T18:47:46Z)
• VR Based Emotion Recognition Using Deep Multimodal Fusion With Biosignals Across Multiple Anatomical Domains [3.303674512749726]
  We introduce a novel multi-scale attention-based LSTM architecture, combined with Squeeze-and-Excitation (SE) blocks.<n>The proposed architecture, validated in a user study, demonstrates superior performance in classifying valance and arousal level.
  arXiv  Detail & Related papers  (2024-12-03T08:59:12Z)
• Scaling Wearable Foundation Models [54.93979158708164]
  We investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM. Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
  arXiv  Detail & Related papers  (2024-10-17T15:08:21Z)
• FORS-EMG: A Novel sEMG Dataset for Hand Gesture Recognition Across Multiple Forearm Orientations [1.3852370777848657]
  Surface electromyography (sEMG) signals hold significant potential for gesture recognition and robust prosthetic hand development. This study introduces a novel MFI sEMG dataset to evaluate commonly used hand gestures across three distinct orientations.
  arXiv  Detail & Related papers  (2024-09-03T14:23:06Z)
• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning [53.00683059396803]
  Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
  arXiv  Detail & Related papers  (2023-10-06T10:40:46Z)
• Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation [48.267001230607306]
  We propose a pretraining framework that enhances multiscale consistency in EM volumes. Our approach leverages a Siamese network architecture, integrating strong and weak data augmentations. It effectively captures voxel and feature consistency, showing promise for learning transferable representations for EM analysis.
  arXiv  Detail & Related papers  (2023-08-19T05:49:13Z)
• cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities [0.0]
  A novel GAN network called TheraGAN was developed to generate IMU signals associated with rehabilitation activities. The generated signals closely mimicked the real signals, and adding generated data resulted in a significant improvement in the performance of all tested networks.
  arXiv  Detail & Related papers  (2023-02-16T00:08:28Z)
• Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle Activity for Enhanced Myoelectric Control of Hand Prostheses [78.120734120667]
  We introduce a novel method, based on a long short-term memory (LSTM) network, to continuously map forearm EMG activity onto hand kinematics. Ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset. Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.
  arXiv  Detail & Related papers  (2021-04-29T00:11:32Z)
• Detecting Human-Object Interaction via Fabricated Compositional Learning [106.37536031160282]
  Human-Object Interaction (HOI) detection is a fundamental task for high-level scene understanding. Human has extremely powerful compositional perception ability to cognize rare or unseen HOI samples. We propose Fabricated Compositional Learning (FCL) to address the problem of open long-tailed HOI detection.
  arXiv  Detail & Related papers  (2021-03-15T08:52:56Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.