Non-obtrusive 3D body tracking for automated mobility assessment in independently living older persons. Results of a pilot trial

被引：0
作者：
Lumetzberger J. [1 ]
Münzer T. [2 ]
Kampel M. [1 ]
机构：
[1] Computer Vision Lab, Vienna University of Technology, Favoritenstr. 9, Vienna
[2] Geriatrische Klinik St. Gallen, Rorschacher Str. 94, St. Gallen
来源：
EAI Endorsed Transactions on Pervasive Health and Technology | 2021年 / 7卷 / 26期
关键词：
AAL; Depth data; Gait speed; Non-obtrusive mobility assessment; Physiotherapist; Privacy;
D O I：
10.4108/eai.4-3-2021.168863
中图分类号：
X9 [安全科学];
学科分类号：
0837 ;
摘要：
INTRODUCTION: With rising age, functional deficit and frequent falls may lead to long-term care admission. Mobility assessment tests can detect fall risk and may induce interventions that prevent a fall. OBJECTIVES: To assess mobility of older persons using real time data and to compare these data with the mobility assessment of physiotherapists. METHODS: 20 older people aged 74±5 (mean ± SD) were monitored over 10 months to investigate the performance of an automated mobility tracker. Physiotherapists performed periodic mobility assessments. Annotated 3d recordings served as ground truth data. RESULTS: High correlation (r=0.684) of annotated and tracked gait speed was found. The mean absolute error is 0.16 m/s. CONCLUSION: 3D mobility trackers can be used to collect long-term mobility data. Since changes in mobility might indicate functional decline, long-term tracking allows to react to changes in mobility. Such a technology may have essential medical and social value. © 2021 J. Lumetzberger et al., licensed to EAI.
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