Toward smart footwear to track frailty phenotypes—using propulsion performance to determine frailty

Hadi Rahemi, Hung Nguyen, Hyoki Lee, Bijan Najafi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number1763
JournalSensors (Switzerland)
Volume18
Issue number6
DOIs
StatePublished - Jun 1 2018
Externally publishedYes

Fingerprint

propulsion
Gait
Propulsion
Walking
Foot
sensors
Sensors
gait
Neural Networks (Computer)
Heel
Toes
Activities of Daily Living
Area Under Curve
walking
Phenotype
Neural networks
Population
phenotype
Monitoring
personnel

Keywords

  • Aging
  • Artificial neural network
  • Frailty
  • Gait
  • Propulsion
  • Smart footwear
  • Walking
  • Wearable sensor

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Toward smart footwear to track frailty phenotypes—using propulsion performance to determine frailty. / Rahemi, Hadi; Nguyen, Hung; Lee, Hyoki; Najafi, Bijan.

In: Sensors (Switzerland), Vol. 18, No. 6, 1763, 01.06.2018.

Research output: Contribution to journalArticle

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