Despite being considered as simple everyday objects, smartphones have the most innovative sensors and electronics technology built in. These features make them powerful, nonintrusive tools for monitoring the user's physical and cognitive performance. This study aims at exploiting smartphone-based physical activity identification, implementing a classification algorithm that makes use of data extracted from in-built smart-phone's accelerometer and gyroscope. Data were gathered from three subjects carrying a standard smartphone equipped with a devoted application able to acquire data from the smartphone' sensors and send them to a remote server. We implemented a specific software that uses K-Nearest Neighbours (KNN) and Support Vector Machines (SVM) classifiers to recognize the type of activity performed with 1.5 seconds granularity. We evaluated the performances of the two classifiers in the cases of 3 (low/medium/high intensity activity) and 4 (rest/walk/stairs/run) activity levels classification. The 3 levels classification showed accuracy and F-1 scores always >90% for both classifiers, whereas the 4 levels classification was not effective in distinguishing between walk and climbing stairs. A reliable classification among low, medium, and high intensity activity demonstrates to be a meaningful achievement for overall monitoring of physical activity level, giving a precise and fairly accurate estimation of type and duration of the activity.
Free context smartphone based application for motor activity levels recognition
Valentina Simonetti
;
2016
Abstract
Despite being considered as simple everyday objects, smartphones have the most innovative sensors and electronics technology built in. These features make them powerful, nonintrusive tools for monitoring the user's physical and cognitive performance. This study aims at exploiting smartphone-based physical activity identification, implementing a classification algorithm that makes use of data extracted from in-built smart-phone's accelerometer and gyroscope. Data were gathered from three subjects carrying a standard smartphone equipped with a devoted application able to acquire data from the smartphone' sensors and send them to a remote server. We implemented a specific software that uses K-Nearest Neighbours (KNN) and Support Vector Machines (SVM) classifiers to recognize the type of activity performed with 1.5 seconds granularity. We evaluated the performances of the two classifiers in the cases of 3 (low/medium/high intensity activity) and 4 (rest/walk/stairs/run) activity levels classification. The 3 levels classification showed accuracy and F-1 scores always >90% for both classifiers, whereas the 4 levels classification was not effective in distinguishing between walk and climbing stairs. A reliable classification among low, medium, and high intensity activity demonstrates to be a meaningful achievement for overall monitoring of physical activity level, giving a precise and fairly accurate estimation of type and duration of the activity.
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