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Related Experiment Video

Updated: Dec 23, 2025

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation.

Md Atiqur Rahman Ahad^1,2, Thanh Trung Ngo¹, Anindya Das Antar³

¹Department of Media Intelligent, Osaka University, Ibaraki 567-0047, Japan.

Sensors (Basel, Switzerland)

|April 30, 2020

Summary

This summary is machine-generated.

Deep learning methods significantly improve automatic age and gender estimation from wearable sensor gait data. This analysis of a biometric challenge reveals deep learning outperforms traditional methods for accurate human attribute prediction.

Keywords:

age estimation gait gender recognition smartphone wearable sensor

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Home-Based Monitor for Gait and Activity Analysis

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Published on: August 8, 2019

Area of Science:

Biometrics and Human-Computer Interaction
Wearable Sensor Technology
Machine Learning for Healthcare

Background:

Wearable sensors are increasingly used in healthcare for various applications.
Automatic age and gender estimation from human gait is a significant area of research.
Gait analysis using wearable sensors offers a unique biometric cue.

Purpose of the Study:

To analyze and compare methods for age and gender estimation from sensor-based gait data.
To evaluate the performance of different approaches in a competitive challenge setting.
To identify the most effective techniques for gait-based human attribute estimation.

Main Methods:

Utilized a large wearable sensor-based gait dataset comprising 745 subjects for training and 58 for testing.
Collected gait data using three IMUZ sensors placed on the waist-belt or backpack.
Analyzed 67 solutions submitted by ten teams, focusing on deep learning and conventional handcrafted methods.

Main Results:

Deep learning-based solutions demonstrated superior performance compared to conventional handcrafted methods.
The top-performing method achieved a 24.23% prediction error for gender estimation.
The best age estimation achieved a mean absolute error of 5.39 years.

Conclusions:

Deep learning approaches are highly effective for automatic age and gender estimation from gait.
The study highlights the potential of wearable sensor data for biometric applications.
Angle embedded gait dynamic images and temporal convolution networks show promise for accurate estimation.