Published Paper Details:

Nowadays human activity detection has come to be an implicit service in lots of smartphones. Everyone is turning into fitness conscious. Smartphones have embedded sensors consisting of accelerometers and gyroscopes. Therefore, the algorithms for such activity recognition should be very light to function on most smartphones or any wearables however correct at the identical time. During this paper, we will develop a lightweight software for human activity detection supported Long Short-Term Memory networks, which can learn features from raw accelerometer information, absolutely bypassing the process of generating manufactured features. We are going to assess our algorithm on information collected in a managed setting, in addition to information collected under field conditions, and we display that our algorithm is robust and performs almost equally well for each scenario even surpassing other approaches from the literature.