Abstract
BackgroundMobile 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.MethodsParticipants 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.ResultsThe 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.ConclusionsHuman 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.
Highlights
Mobile health monitoring using wearable sensors is a growing area of interest
No significant differences were found between groups for stage 1, sensitivity and F-score for the stroke population were lower for immobile states and specificity was higher for mobile states
Specificity was significantly lower for stair recognition and sensitivity and small movement recognition was poor for both groups
Summary
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. 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 monitor a person’s mobility activities outside a hospital setting becomes valuable for clinical decisionmaking. Human Activity Recognition (HAR) systems combine wearable sensor and computing technologies. HAR systems typically use accelerometer and gyroscope sensors since these are small, affordable, and generally unobtrusive [1]. Other HAR systems combine sensor types, such as accelerometer and ECG [2], or use multiple sensor locations, such as sternum and thigh [3], or thigh and chest [4]. Smartphones are ubiquitous, carried by most individuals on a daily basis, and many devices contain integrated accelerometer and Capela et al Journal of NeuroEngineering and Rehabilitation (2016) 13:5 gyroscope sensors, which are commonly used to measure posture and movement [5]
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