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Open AccessArticle

Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty

by Hadi Rahemi 1,2,†, Hung Nguyen 1,†, Hyoki Lee 1,3 and Bijan Najafi 1,*
1
Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
2
Circulation Concepts Inc., Houston, TX 77030, USA
3
BioSensics LLC, Watertown, MA 02472, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to the manuscript.
Sensors 2018, 18(6), 1763; https://doi.org/10.3390/s18061763
Received: 31 March 2018 / Revised: 25 May 2018 / Accepted: 25 May 2018 / Published: 1 June 2018
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
Frailty assessment is dependent on the availability of trained personnel and it is currently limited to clinic and supervised setting. The growing aging population has made it necessary to find phenotypes of frailty that can be measured in an unsupervised setting for translational application in continuous, remote, and in-place monitoring during daily living activity, such as walking. We analyzed gait performance of 161 older adults using a shin-worn inertial sensor to investigate the feasibility of developing a foot-worn sensor to assess frailty. Sensor-derived gait parameters were extracted and modeled to distinguish different frailty stages, including non-frail, pre-frail, and frail, as determined by Fried Criteria. An artificial neural network model was implemented to evaluate the accuracy of an algorithm using a proposed set of gait parameters in predicting frailty stages. Changes in discriminating power was compared between sensor data extracted from the left and right shin sensor. The aim was to investigate the feasibility of developing a foot-worn sensor to assess frailty. The results yielded a highly accurate model in predicting frailty stages, irrespective of sensor location. The independent predictors of frailty stages were propulsion duration and acceleration, heel-off and toe-off speed, mid stance and mid swing speed, and speed norm. The proposed model enables discriminating different frailty stages with area under curve ranging between 83.2–95.8%. Furthermore, results from the neural network suggest the potential of developing a single-shin worn sensor that would be ideal for unsupervised application and footwear integration for continuous monitoring during walking. View Full-Text
Keywords: frailty; gait; artificial neural network; propulsion; aging; wearable sensor; walking; smart footwear frailty; gait; artificial neural network; propulsion; aging; wearable sensor; walking; smart footwear
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Rahemi, H.; Nguyen, H.; Lee, H.; Najafi, B. Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty. Sensors 2018, 18, 1763.

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