Sequential Modeling for the Recognition of Activities in Logistics

Abstract

Activity recognition is an important task in cyber physical system research and has been the focus of researchers worldwide. This paper presents a method for activity recognition in logistic operations using data from accelerometer and gyroscope sensors. A Long Short Term Memory (LSTM) recurrent neural network, bidirectional LSTM and a Convolutional LSTM (ConvLSTM) are used to classify between six activities being performed in the logistics operations being carried out. Comparing the performance of the LSTMs to the Conv-LSTM network, the designed Bi-LSTM RNN outperforms the other networks considered