Single triaxial accelerometer-gyroscope classification for human activity recognition

Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. For recognizing the activity, the accelerometer was popular sensors. As well as a gyroscope, in addition to dimension, low computation, and can be embedded in a smartphone. Used smartphone with an accelerometer as a popular solution for recognized daily activity. Signal was generated from the accelerometer as a time-series data is an actual approach like a human activity pattern. Traditional machine learning method in mid of the modern method worth it considering. Single position triaxial accelerometer-gyroscope Motion data have acquired in an of 30 volunteers. Basic actives (Laying, Standing, Sitting, Walking, Walking Upstairs, Walking Downstairs) were collected from volunteers. Decision Tree, Random Forest, Extra Trees Classifier, KNN, Logistic Regression, SVC, Ensemble Vote Classifier. The purposed method, logistic regression, achieves 98% accuracy. Furthermore, any feature selection and extraction method were not used.