ArticleSensors (Basel, Switzerland)2020
Wearable Sensor-Based Gait Analysis for Age and Gender Estimation.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 59 citations in OpenAlex.
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- Diagnostic utility of smartphone-integrated gait analysis in the assessment of BPPV.Frontiers in neurology · 2025Article
- A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data.Sensors (Basel, Switzerland) · 2023Article
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- A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor.Computational intelligence and neuroscience · 2021Article
- Unicompartmental knee arthroplasty results in a better gait pattern than total knee arthroplasty: Gait analysis with a smartphone application.Joint diseases and related surgery · 2021Article
- Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning.Sensors (Basel, Switzerland) · 2020Article
- Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams-for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.
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