HUMAN ACTIVITY RECOGNITION USING SMART PHONE
Activity recognition is one of the most important technology behind many applications such as medical research, human survey system and it is an active research topic in health care and smart homes. Smart phones are equipped with various built-in sensing platforms like accelerometer, gyroscope,GPS, compass sensor and barometer. We can design a system to capture the state of the user. Activity recognition system takes the raw sensor reading from mobile sensors as inputs and estimates a human motion activities and users usually carry their smartphone with them. These facts make HUMAN ACTIVITY RECOGNITION more important and popular. This work focuses on recognition of human activity using smartphone sensors using deep learning approaches like Recurrent Neural Network (RNN). This paper presents a human activity recognition (HAR) system that uses accelerometer and gyroscope data obtained from a smartphone as inputs to a bidirectional long short-term memory (LSTM) model of the RNN model network. Six human activities were recognized: sitting, standing, laying, walking, walking upstairs, and walking downstairs. Results of the approaches used are compared in terms of efficiency and precision.
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