Human Activity Recognition Using Multiple Smartphone Sensors
Abdul Kadar Muhammad Masum, Arnab Barua, Erfanul Hoque Bahadur, Mohammad Robiul Alam, Md. Akib Uz Zaman Chowdhury, Mohammed Shamsul Alam · 2018
Due to the availability of various sensors in the smartphones, used by millions of people for communication, a new research arena is identified for data mining and machine learning. This paper aims to recognise ten human activities, i.e., sitting, walking, jogging, lying, walking upstairs and downstairs, cycling, standing, squatting in a toilet and fallen down, through smartphone sensors. For the implementation of our models, we collected labeled Gyroscope data, Accelerometer data, Temperature data and Humidity data from three users regarding their daily activities and summarised in 1Hz frequency. Then we used our training dataset to deduct a model for the prediction of activity recognition. Our work is noble in term of our system of data collection along with recognition of new activities with higher accuracy in recognition. These works have a wide range of applications as it may predict disease related to physical activities, monitor physical activities and elderly care.