Sequential Modeling for the Recognition of Activities in Logistics

Authors

  • Zafi Sherhan Syed, Dr. Mehran University of Engineering and Technology
  • Muhammad Zaigham Abbas Shah Syed Mheran University of Engineering and Technology
  • Muhammad Shehram Shah Syed Mehran University of Engineering and Technology
  • Aunsa Shah University of Sindh

DOI:

https://doi.org/10.30537/sjet.v4i1.848

Keywords:

accelerometer, gyroscope, LARA, Logistics activity recognition, Sequential Modeling

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

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Author Biographies

Zafi Sherhan Syed, Dr., Mehran University of Engineering and Technology

Assistant Professor, Department of Telecommunication Engineering, Mehran University of Engineering and Technology, Pakistan.

Muhammad Shehram Shah Syed, Mehran University of Engineering and Technology

Assistant Professor, Department of Software Engineering, Mehran University of Engineering and Technology, Pakistan.

Aunsa Shah, University of Sindh

Department of Electronics, University of Sindh, Pakistan

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Published

2021-06-10