Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Denoising Respiratory Sinus Arrhythmia of Pulse-to-Pulse Interval Signals Extracted from Photoplethysmogram with an Autoregressive Moving Average Model.

Sensors (Basel, Switzerland)·2026

Same author

Hybrid CNN-LSTM Model for Evaluating Heart Rate Variability from Pulse-to-Pulse Intervals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System.

Sensors (Basel, Switzerland)·2024

Same author

Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds.

Sensors (Basel, Switzerland)·2024

Same author

Marine Oil Pollution Monitoring Based on a Morphological Attention U-Net Using SAR Images.

Sensors (Basel, Switzerland)·2024

Same author

Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals.

Sensors (Basel, Switzerland)·2024

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 5, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with

Tan-Hsu Tan¹, Jyun-Yu Shih¹, Shing-Hong Liu²

¹Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

This study introduces a novel human activity recognition (HAR) system using smartphone and smartwatch sensors. The advanced system accurately classifies 18 physical activities, enhancing mobile health applications.

Keywords:

bidirectional gated recurrent unit (BiGRU)human activity recognition mHealth regularized extreme machine learning (RELM)

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Related Experiment Videos

Last Updated: Aug 5, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Area of Science:

Biomedical Engineering
Computer Science
Digital Health

Background:

Mobile health (mHealth) integrates mobile devices and IoT for enhanced healthcare delivery and daily wellness monitoring.
Human Activity Recognition (HAR) is crucial for understanding the link between daily activities and health outcomes, particularly for elderly care.

Purpose of the Study:

To develop and evaluate a high-performance HAR system for classifying 18 distinct physical activities.
To leverage sensor data from smartphones and smartwatches for accurate health monitoring.

Main Methods:

A hybrid feature extraction model combining Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU).
A Regularized Extreme Learning Machine (RELM) algorithm with a Single-Hidden Layer Feedforward Neural Network (SLFN) for activity classification.

Main Results:

The proposed HAR system achieved high performance metrics: 98.3% average precision, 98.4% recall, 98.4% F1-score, and 98.3% accuracy.
These results demonstrate superior performance compared to existing HAR methodologies.

Conclusions:

The developed HAR system effectively classifies a wide range of physical activities using wearable and mobile sensors.
This technology holds significant potential for improving mHealth, telemedicine, and personalized elderly care solutions.