Keshav Thapa, Zubaer Md. Abdhulla AI, Yang Sung-Hyun*
CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1653-1670, 2021, DOI:10.32604/cmc.2021.015660
Abstract In this era, deep learning methods offer a broad spectrum of efficient and original algorithms to recognize or predict an output when given a sequence of inputs. In current trends, deep learning methods using recent long short-term memory (LSTM) algorithms try to provide superior performance, but they still have limited effectiveness when detecting sequences of complex human activity. In this work, we adapted the LSTM algorithm into a synchronous algorithm (sync-LSTM), enabling the model to take multiple parallel input sequences to produce multiple parallel synchronized output sequences. The proposed method is implemented for simultaneous human activity recognition (HAR) using heterogeneous… More >
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