Application of Machine Vision in Classifying Gait Frailty Among Older Adults.

Yixin Liu,Linbo Qing,Lixing Zhou,Birong Dong,Haopeng Yu,Jiaoyang Chen,Xinyi Lin,Xiaohai He,Junshan Jiang,Xuan He,Qizhi Teng,Chun Xiao,Rui Hu,Zhengyong Wang,Hui Wang,Meiling Ge,Xinyi Li,Xuelian Sun,Yi Mou,Haoran Gong,Renjie Wang,Xiaolei Liu,Lei Xia ,Liangliang Deng ,Zhihai Xu ,Yan Du ,Baoshu Yin

doi:10.3389/fnagi.2021.757823

Abstract

Background: Frail older adults have an increased risk of adverse health outcomes and premature death. They also exhibit altered gait characteristics in comparison with healthy individuals.Methods: In this study, we created a Fried’s frailty phenotype (FFP) labelled casual walking video set of older adults based on the West China Health and Aging Trend study. A series of hyperparameters in machine vision models were evaluated for body key point extraction (AlphaPose), silhouette segmentation (Pose2Seg, DPose2Seg, and Mask R-CNN), gait feature extraction (Gaitset, LGaitset, and DGaitset), and feature classification (AlexNet and VGG16), and were highly optimised during analysis of gait sequences of the current dataset.Results: The area under the curve (AUC) of the receiver operating characteristic (ROC) at the physical frailty state identification task for AlexNet was 0.851 (0.827–0.8747) and 0.901 (0.878–0.920) in macro and micro, respectively, and was 0.855 (0.834–0.877) and 0.905 (0.886–0.925) for VGG16 in macro and micro, respectively. Furthermore, this study presents the machine vision method equipped with better predictive performance globally than age and grip strength, as well as than 4-m-walking-time in healthy and pre-frailty classifying.Conclusion: The gait analysis method in this article is unreported and provides promising original tool for frailty and pre-frailty screening with the characteristics of convenience, objectivity, rapidity, and non-contact. These methods can be extended to any gait-related disease identification processes, as well as in-home health monitoring.

Highlights

Frailty is a state of increased vulnerability to stress, which may lead to a diminished homeostatic capacity across multiple physiological systems (Fried et al, 2009)
We found no significant differences in age, gender, education level, marital status, and physical frailty status prevalence between the training/test and validation sets
The sample of merged images with the original image and key points in the current set evaluated by human vision presented satisfactory performance in body key point recognition for the current method

Summary

Introduction

Frailty is a state of increased vulnerability to stress, which may lead to a diminished homeostatic capacity across multiple physiological systems (Fried et al, 2009). Frail older adults are at an increased risk of premature death and various adverse health outcomes, including falls, fractures, disability, and dementia, all of which could result in a poor quality of life and an increased cost of healthcare resources, such as emergency department visits, hospitalisation, and institutionalisation (Kojima et al, 2019). Human locomotion is a common daily activity and is an acquired yet complex behaviour It requires the involvement of the nervous system, many parts of the musculoskeletal apparatus, and the cardiorespiratory system (Adolph and Franchak, 2017). Frail older adults have an increased risk of adverse health outcomes and premature death. They exhibit altered gait characteristics in comparison with healthy individuals

Objectives

Methods

Results

Conclusion