Learning Age From Gait: A Survey

Abstract

Age is an important human attribute that needs to be determined for various purposes, including security, health, human identification, and law enforcement. Hence, there is an increasing research interest in automatic age estimation using biometric traits such as face and gait. In recent years, gait analysis has received growing attention due to the pervasive nature of video surveillance. Gait signals that measure the manner of walking can be obtained using vision and sensor-based techniques. Individual gait patterns obtainable from videos, images, or sensors are shown unconsciously and are not easily obscured. Additionally, gait signals can be obtained unobtrusively with cameras placed at a long distance because gait does not require high-resolution images. However, the extraction of age-associated gait features is a challenging task due to various gait covariates. These covariates include clothing and view changes for visionbased gait; walking slope and footwear for sensor-based gait. This paper provides a survey of scientific literature on age estimation gait features. We focus on the approaches to extracting age-associated gait features, namely, vision-based and sensor-based approaches, how they may be affected by the different covariates, and domain-specific applications. To make this work useful for as wide of an audience as possible, we also include discussions on key topics such as existing datasets, evaluation strategies, and open challenges that should be addressed in the future.

A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

“…Age is a significant human characteristic that can be used to evaluate a person's identification, security, and health [1]. Because of its significance, age evaluation cannot be based solely on human perception [2].…”

A Survey of Human Gait-Based Artificial Intelligence Applications

“… 2) studies that partially overlap with our studies, such as the literature review on PD diagnosis by Mei et al (2021) , that in addition to PD diagnosis by gait, also discuss other modalities such as voice, handwriting, magnetic resonance imaging (MRI), etc. 3) studies that are in the scope of this review but only cover a specific topic such as human motion trajectory prediction ( Rudenko et al, 2020 ), wearable sensing technologies for sports biomechanics ( Taborri et al, 2020 ), self-powered sensors and systems ( Wu et al, 2020 ), person re-Identification ( Wang et al, 2016 ), ( Nambiar et al, 2019 ), ( Karanam et al, 2019 ), machine learning in soft robotics ( Kim et al, 2021 ), ambient assisted living technologies (mostly AI-enabled and gait-related) ( Cicirelli et al, 2021 ), human action recognition ( Gurbuz and Amin, 2019 ), biomechanics ( Halilaj et al, 2018 ), gait recognition ( Kusakunniran, 2020 ), ( Singh et al, 2018 ), ( Wan C. et al, 2018 ), gait event detection and gait phase recognition ( Prasanth et al, 2021 ), clinical gait diagnostics of knee osteoarthritis ( Parisi et al, 2020 ), knee pathology assessment ( Abid et al, 2019 ), data preprocessing in gait classification ( Burdack et al, 2019 ), age estimation ( Aderinola et al, 2021 ), and banchamrk datasets ( Nunes et al, 2019 ). A survey paper by Alzubaidi et al (2021) provides an overview of deep learning, with helpful definitions and a discussion of strengths, limitations, and future trends of various deep learning techniques.…”