Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes

Abstract

Activity level and gait parameters during daily life are important indicators for clinicians because they can provide critical insights into modifications of mobility and function over time. Wearable activity monitoring has been gaining momentum in daily life health assessment. Consequently, this study seeks to validate an algorithm for the classification of daily life activities and to provide a detailed gait analysis in older adults. A system consisting of an inertial sensor combined with a pressure sensing insole has been developed. Using an algorithm that we previously validated during a semi structured protocol, activities in 10 healthy elderly participants were recorded and compared to a wearable reference system over a 4 h recording period at home. Detailed gait parameters were calculated from inertial sensors. Dynamics of physical behavior were characterized using barcodes that express the measure of behavioral complexity. Activity classification based on the algorithm led to a 93% accuracy in classifying basic activities of daily life, i.e., sitting, standing, and walking. Gait analysis emphasizes the importance of metrics such as foot clearance in daily life assessment. Results also underline that measures of physical behavior and gait performance are complementary, especially since gait parameters were not correlated to complexity. Participants gave positive feedback regarding the use of the instrumented shoes. These results extend previous observations in showing the concurrent validity of the instrumented shoes compared to a body-worn reference system for daily-life physical behavior monitoring in older adults.

Gait Symmetry Assessment with a Low Back 3D Accelerometer in Post-Stroke Patients

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A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function

“…In recent years, the concept of a smart home has been extended from the simple automation and automatic control of the home appliances to a more complex management of the user interaction with several sensors and actuators deployed in the home environment in order to pursue the users' wellbeing and energy sustainability [1][2][3][4][5][6][7][8][9]. The development of wearable sensors has expanded the possibilities available in this context, pushing research towards new solutions based on behavioral monitoring [10][11][12][13]. The role of wearable sensors in this framework is very wide, but recent research has focused on human activity recognition (HAR) as a new service to monitor the amount of activity for health purposes; this can be assessed and considered in order to early detect anomalies possibly relevant to users' wellbeing [14][15][16][17].…”

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