Variables influencing wearable sensor outcome estimates in individuals with stroke and incomplete spinal cord injury: a pilot investigation validating two research grade sensors

Chandrasekaran Jayaraman,Lori Mcgee Koch,Alannah Mannix-Slobig,Chaithanya Krishna Mummidisetty,Arun Jayaraman

doi:10.1186/s12984-018-0358-y

Chandrasekaran Jayaraman, Lori Mcgee Koch + Show 3 more

Open Access

https://doi.org/10.1186/s12984-018-0358-y

Copy DOI

Abstract

BackgroundMonitoring physical activity and leveraging wearable sensor technologies to facilitate active living in individuals with neurological impairment has been shown to yield benefits in terms of health and quality of living. In this context, accurate measurement of physical activity estimates from these sensors are vital. However, wearable sensor manufacturers generally only provide standard proprietary algorithms based off of healthy individuals to estimate physical activity metrics which may lead to inaccurate estimates in population with neurological impairment like stroke and incomplete spinal cord injury (iSCI). The main objective of this cross-sectional investigation was to evaluate the validity of physical activity estimates provided by standard proprietary algorithms for individuals with stroke and iSCI. Two research grade wearable sensors used in clinical settings were chosen and the outcome metrics estimated using standard proprietary algorithms were validated against designated golden standard measures (Cosmed K4B2 for energy expenditure and metabolic equivalent and manual tallying for step counts). The influence of sensor location, sensor type and activity characteristics were also studied.Methods28 participants (Healthy (n = 10); incomplete SCI (n = 8); stroke (n = 10)) performed a spectrum of activities in a laboratory setting using two wearable sensors (ActiGraph and Metria-IH1) at different body locations. Manufacturer provided standard proprietary algorithms estimated the step count, energy expenditure (EE) and metabolic equivalent (MET). These estimates were compared with the estimates from gold standard measures. For verifying validity, a series of Kruskal Wallis ANOVA tests (Games-Howell multiple comparison for post-hoc analyses) were conducted to compare the mean rank and absolute agreement of outcome metrics estimated by each of the devices in comparison with the designated gold standard measurements.ResultsThe sensor type, sensor location, activity characteristics and the population specific condition influences the validity of estimation of physical activity metrics using standard proprietary algorithms.ConclusionsImplementing population specific customized algorithms accounting for the influences of sensor location, type and activity characteristics for estimating physical activity metrics in individuals with stroke and iSCI could be beneficial.

Highlights

Monitoring physical activity and leveraging wearable sensor technologies to facilitate active living in individuals with neurological impairment has been shown to yield benefits in terms of health and quality of living
For the group with incomplete spinal cord injury (iSCI), the ActiLife and Metria-IH1, significantly under-estimated the step counts (p < .016) (Fig. 3). These observations with step counts are consistent with previous literature, benchmarking the quality of data collected in this investigation [13, 42]. These results suggest that, (i) the step count estimates from standard algorithms can be influenced by effects such as sensor location and the optimal location to place sensor for step count tracking can vary depending on the specific type of population being studied
This study systematically analyzed the influence of four factors, namely, (i) choice of sensor type ((ActiGraph wG3TX-BT using ActiLife standard proprietary algorithm (SPA)) and Metria-IH1 -Senseware fusion based SPA), (ii) sensor location (ActiGraph wG3TX-BT at arm, waist and ankle and Metria-IH1 at arm) (iii) the activity characteristics and (iv) population effects (healthy, iSCI(ambulatory), Stroke) on the validity of three physical activity outcome metrics estimated by SPAs

Summary

Introduction

Monitoring physical activity and leveraging wearable sensor technologies to facilitate active living in individuals with neurological impairment has been shown to yield benefits in terms of health and quality of living In this context, accurate measurement of physical activity estimates from these sensors are vital. Advanced methods like camera based motion capture, pressure sensor walkway [5] and force plate systems for assessments, significantly reliable, limits data collection to a confined controlled laboratory space and are expensive [6] While such controlled environment tests can provide highresolution information to uncover the underlying biomechanics during in-patient movement assessments, they provide very little to no information about a patient's natural physical activity behavior and compliance in their community or home setting [7]

Objectives

Methods

Results

Discussion

Conclusion