Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants

N A Capela, E D Lemaire, N Baddour, M Rudolf, N Goljar, H Burger, N A Capela, E D Lemaire, N Baddour, M Rudolf, N Goljar, H Burger

Abstract

Background: Mobile health monitoring using wearable sensors is a growing area of interest. As the world's population ages and locomotor capabilities decrease, the ability to report on a person's mobility activities outside a hospital setting becomes a valuable tool for clinical decision-making and evaluating healthcare interventions. Smartphones are omnipresent in society and offer convenient and suitable sensors for mobility monitoring applications. To enhance our understanding of human activity recognition (HAR) system performance for able-bodied and populations with gait deviations, this research evaluated a custom smartphone-based HAR classifier on fifteen able-bodied participants and fifteen participants who suffered a stroke.

Methods: Participants performed a consecutive series of mobility tasks and daily living activities while wearing a BlackBerry Z10 smartphone on their waist to collect accelerometer and gyroscope data. Five features were derived from the sensor data and used to classify participant activities (decision tree). Sensitivity, specificity and F-scores were calculated to evaluate HAR classifier performance.

Results: The classifier performed well for both populations when differentiating mobile from immobile states (F-score > 94 %). As activity recognition complexity increased, HAR system sensitivity and specificity decreased for the stroke population, particularly when using information derived from participant posture to make classification decisions.

Conclusions: Human activity recognition using a smartphone based system can be accomplished for both able-bodied and stroke populations; however, an increase in activity classification complexity leads to a decrease in HAR performance with a stroke population. The study results can be used to guide smartphone HAR system development for populations with differing movement characteristics.

Figures

Fig. 1
Fig. 1
WMMS Decision Tree Structure
Fig. 2
Fig. 2
Plots of L-SMA, SOR and SoSD showing how these features change during mobile and immobile activities
Fig. 3
Fig. 3
Plot of DifftoY showing how this feature change during waling and lying down activities
Fig. 4
Fig. 4
Plot of Sum of variance, SMAvar and G-SMAvar showing how these features change during waling and stair climbing activities

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