LSTM based approach for diabetic symptomatic activity recognition using smartphone sensors

Abstract

Being concerned about the rising rate of the usage level of smartphone for last few years, researchers are striving to let out the disguised assets of smartphones. Regarding this phrase, the embedding of a variety of sensors such as accelerometer sensor, gyroscope sensor, humidity sensor etc. has attained the considerable attention of researchers for facilitating the human activity recognition task employing the sensor's applications. In this paper, we practiced the Long Short-Term Memory a.k.a. LSTM deep learning model with an aim to recognize thirteen human activities stated as walking, walking upstairs, walking downstairs, sitting, standing, jogging, squatting in the toilet, fallen down, lying, cycling, drinking, eating and genital itching. We opted for these activities with an intention of early diagnosing of diabetes in near future. We amassed data from four sensors stated accelerometer sensor, gyroscope sensor, humidity sensor and temperature sensor subjecting ten volunteers implying a frequency of 10Hz. The data was attained using an android application which was developed by us for the purpose of accumulation of sensor data from the smartphone.