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

Abstract

Background: 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. 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.

Methods: 28 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.

Results: The sensor type, sensor location, activity characteristics and the population specific condition influences the validity of estimation of physical activity metrics using standard proprietary algorithms.

Conclusions: Implementing 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.

Keywords: ActiGraph; Energy expenditure; Metabolic equivalent; Metria-IH1; Spinal cord injury; Step counts; Stroke; Sweat rate; Validation; Wearable devices.

Fig. 1

Devices used and protocol design. a Picture of ActiGraph wG3TX-BT; b picture of Metria-IH1; c The sensor locations used, ActiGraphs (red color) were placed on the right side upper arm, waist and ankle while the Metria-IH1 (grey color) was placed on the back side of left upper arm. The Cosmed K4B2 was body mounted with the rubberized facemask; d) the experimental design and the spectrum of activities executed during the protocol. To execute the study protocol, participants performed a set of structured indoor activities in a controlled laboratory setting. (e) The spectrum of the performed activities was categorized into three levels, (i) sedentary activities: lying down on a treatment table, sitting and standing (with or without assistive device) for two minutes each, (ii) low intensity activity: walk 50 steps, and (iii) high intensity activities: a six-minute walking test (6MWT) and two minute of fast paced multi sit-to-stand activity. Sufficient rests and recovery were provided between all the performed activities. All the three devices, namely, the Actigrpah, Metria and Cosmed K4B2 continuously collected data during the entire protocol

Fig. 2

A visual comparison of the validity maps for bias in the estimated EE and MET. Estimates from both the devices in comparison to the Cosmed for the spectrum of activities performed in Healthy, iSCI and stroke groups. (a,c) Validity map for estimates from ActiGraph wG3TX-BT’s located at waist, ankle and upper arm on the right side (using ActiLife’s SPA), (b,d) Validity map for estimates from Matria-IH1 located at back side of the left upper arm (using Metria-IH1’s Senseware platform SPA). The effect of sensor location on the outcome estimates (EE, MET and step count) when using SPAs for the population with stroke and iSCI from our sample is visually summarized in the map

Fig. 3

Step count estimates. Estimated step counts during the 50 step walk test from Actigraphs at arm, waist, ankle and Metria-IH1 compared to manual count (phone based manual tally) of 50 steps in healthy, SCI and Stroke. * indicates significant differences in estimated step count (*: p < 0.05 (Metria-IH1); p < 0.016(ActiGraph))