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Instrumented shoes for activity classification in the elderly.

Christopher Moufawad el Achkar1, Constanze Lenoble-Hoskovec2, Anisoara Paraschiv-Ionescu1

  • 1Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.

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Summary

Instrumented shoes accurately track daily physical activity in older adults, aiding fall and frailty risk assessment. This technology provides valuable gait and activity data for enhanced mobility monitoring.

Keywords:
Barometric sensorInertial sensorsPhysical activityPlantar pressure sensorsWearable sensors

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Area of Science:

  • Biomedical Engineering
  • Gerontology
  • Rehabilitation Science

Background:

  • Quantifying physical activity in older adults is crucial for monitoring fall risk and frailty.
  • Current methods may be obtrusive or lack comprehensive data.
  • Instrumented footwear offers a potential unobtrusive solution for daily activity monitoring.

Purpose of the Study:

  • To develop and validate an activity classification algorithm using data from instrumented shoes.
  • To assess the system's ability to distinguish between different activities and postural transitions.
  • To evaluate the clinical utility of combined activity and gait parameters for elderly mobility assessment.

Main Methods:

  • Ten elderly participants wore instrumented shoes with insoles and inertial measurement units.
  • Activities of daily life were performed under a semi-structured protocol.
  • A decision tree algorithm, informed by biomechanics, classified activities based on foot loading, orientation, and elevation data.

Main Results:

  • The activity classification algorithm achieved 97% overall accuracy compared to a reference system.
  • The system successfully identified all postural transitions and locomotion with elevation changes.
  • The algorithm demonstrated robustness against minor parameter adjustments.

Conclusions:

  • Instrumented shoes provide a suitable method for unobtrusive daily activity monitoring in older adults.
  • The system can derive gait and activity parameters, offering valuable clinical insights into elderly mobility.
  • This technology supports enhanced assessment of fall risk and frailty in geriatric populations.