Human Activity Recognition

Follow this topic Ask about this topic

Latest papers 217

27 papers in the last four weeks, up 23% on the four weeks before. 0.2% of all new papers.

All topics

Event-Driven On-Sensor Locomotion Mode Recognition Using a Shank-Mounted IMU with Embedded Machine Learning for Exoskeleton Control
Feb 24, 2026Mohammadsaleh Razmi, Iman ShojaeiWearable Sensing Human Activity Recognition
Align Technology, Inc., San Jose, CA 95134, USA
Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition
Jan 7, 2026Nia Touko, Matthew O A Ellis, Cristiano Capone +3EMG-Based Gesture Recognition Test-Time Adaptation
School of Computer Science, University of Sheffield, Sheffield, S10 2TN, United Kingdom · Natl. Center for Radiation Protection and Computational Physics, Istituto Superiore di Sanità, 00161 Rome, Italy · Politecnico di Torino, Italy +1
A Comparative Study of EMG- and IMU-based Gesture Recognition at the Wrist and Forearm
Dec 8, 2025Soroush Baghernezhad, Elaheh Mohammadreza, Vinicius Prado da Fonseca +2Human Activity Recognition Wearable Sensing
Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada · Department of Mechanical and Mechatronics Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
IBIS: A Hybrid Inception-BiLSTM and SVM Ensemble for Robust Doppler-based Human Activity Recognition
Oct 28, 2025Alison M. Fernandes, Hermes I. Del Monego, Bruno S. Chang +3Human Activity Recognition Domain Generalization
Universidade Tecnol´ogica Federal do Paran´a (UTFPR), Programa de P´os-Graduac¸ ˜ao em Engenharia El´etrica e Inform´atica Industrial (CPGEI-CT), Av. Sete de Setembro, 3165, Curitiba, 80230-901, Brazil · INESC TEC, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Porto, Portugal
Social 3D Scene Graphs: Modeling Human Actions and Relations for Interactive Service Robots
Sep 29, 2025Ermanno Bartoli, Dennis Rotondi, Buwei He +3Human Activity Recognition 3D SGG
Faculty of Robotics Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden · Socially Intelligent Robotics Lab at the Institute for Artificial Intelligence, University of Stuttgart, Germany · International Max Planck Research School for Intelligent Systems (IMPRS-IS)
Pose-Based Fall Detection System: Efficient Monitoring on Standard CPUs
Mar 25, 2025Vinayak Mali, Saurabh JaiswalFall Detection Human Activity Recognition
Department of Artificial Intelligence Indian Institue of Technology Kharagpur
GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition
Mar 22, 2024Lei Jiang, Weixin Yang, Xin Zhang +1Skeleton-Based Action Recognition Human Activity Recognition
University College London · University of Oxford · South China University of Technology

Feb 24, 2026J/K move · Enter open · S save

Event-Driven On-Sensor Locomotion Mode Recognition Using a Shank-Mounted IMU with Embedded Machine Learning for Exoskeleton Control

Mohammadsaleh Razmi, Iman Shojaei

Align Technology, Inc., San Jose, CA 95134, USA

Wearable Sensing Human Activity Recognition On-Device Inference

Open paper AskPDF arXiv

This work presents a wearable human activity recognition (HAR) system that performs real-time inference directly inside a shank-mounted inertial measurement unit (IMU) to support low-latency control of a lower-limb exoskeleton. Unlike conventional approaches that continuously stream raw inertial data to a microcontroller for classification, the proposed system executes activity recognition at the sensor level using the embedded Machine Learning Core (MLC) of the STMicroelectronics LSM6DSV16X IMU, allowing the host microcontroller to remain in a low-power state and read only the recognized activity label from IMU registers. While the system generalizes to multiple human activities, this paper focuses on three representative locomotion modes - stance, level walking, and stair ascent - using data collected from adult participants. A lightweight decision-tree model was configured and deployed for on-sensor execution using ST MEMS Studio, enabling continuous operation without custom machine learning code on the microcontroller. During operation, the IMU asserts an interrupt when motion or a new classification is detected; the microcontroller wakes, reads the MLC output registers, and forwards the inferred mode to the exoskeleton controller. This interrupt-driven, on-sensor inference architecture reduces computation and communication overhead while preserving battery energy and improving robustness in distinguishing level walking from stair ascent for torque-assist control.