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

Muscle Fatigue Assessment in Healthcare Application by Using Surface Electromyography: A Transfer Learning Approach.

Sensors (Basel, Switzerland)·2026

Same author

A Systematic Review of Surface Electromyography in Sarcopenia: Muscles Involved, Signal Processing Techniques, Significant Features, and Artificial Intelligence Approaches.

Sensors (Basel, Switzerland)·2025

Same author

A Transfer Learning Approach for Toe Walking Recognition Using Surface Electromyography on Leg Muscles.

Sensors (Basel, Switzerland)·2025

Same author

Preliminary Study on Wearable Smart Socks with Hydrogel Electrodes for Surface Electromyography-Based Muscle Activity Assessment.

Sensors (Basel, Switzerland)·2025

Same author

Exploring Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study.

JMIR aging·2025

Same author

Movement Disorders and Smart Wrist Devices: A Comprehensive Study.

Sensors (Basel, Switzerland)·2025

Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026

Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026

Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026

Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026

See all related articles

Search research articles

Home
Radar-based Activity Recognition In Strictly Privacy-sensitive Settings Through Deep Feature Learning.

Home
Radar-based Activity Recognition In Strictly Privacy-sensitive Settings Through Deep Feature Learning.

Related Experiment Video

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

Radar-Based Activity Recognition in Strictly Privacy-Sensitive Settings Through Deep Feature Learning.

Giovanni Diraco¹, Gabriele Rescio¹, Alessandro Leone¹

¹National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy.

Biomimetics (Basel, Switzerland)

|April 25, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Radar technology offers a privacy-preserving solution for human activity recognition in sensitive areas like bathrooms. This system accurately identifies daily living activities without compromising user privacy, unlike camera-based methods.

Keywords:

FMCW radar deep feature learning human activity recognition privacy

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

Related Experiment 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

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

Area of Science:

Engineering
Computer Science
Human-Computer Interaction

Background:

Privacy concerns limit traditional vision-based and wearable sensor methods for human activity recognition in sensitive environments.
Non-invasive and anonymous monitoring is crucial for applications in settings like bathrooms.

Purpose of the Study:

To investigate the feasibility of using Doppler radar for human activity recognition in privacy-sensitive bathroom environments.
To develop and evaluate deep learning models for classifying daily living activities using radar data.

Main Methods:

Utilized a BGT60TR13C Xensiv 60 GHz radar sensor for data collection.
Collected a dataset of ten daily living activities from seven volunteers in a bathroom setting.
Employed deep learning models, including DenseNet201 and ResNet50, with bidirectional long short-term memory networks for activity classification.

Main Results:

Achieved high overall accuracy, with DenseNet201 reaching 97.02% and ResNet50 reaching 94.57%.
Demonstrated strong recognition performance for most activities, including face washing, teeth brushing, and dressing/undressing.
Identified 'lying down' and 'getting up' as challenging activities due to motion similarity.

Conclusions:

Doppler radar-based human activity recognition is a viable and privacy-preserving alternative to invasive monitoring systems.
The proposed radar approach offers an effective solution for smart home and healthcare applications requiring non-invasive monitoring.