Adapted Long Short-Term Memory (LSTM) for Concurrent\\ Human Activity Recognition

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 th...

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Bibliographic Details
Published in:Computers, materials & continua Vol. 69; no. 2; pp. 1653 - 1670
Main Authors: Thapa, Keshav, Md. Abdhulla AI, Zubaer, Sung-Hyun, Yang
Format: Journal Article
Language:English
Published: Henderson Tech Science Press 2021
Subjects:
Accuracy
Algorithms
Artificial intelligence
Astronomy
Complexity
Deep learning
Human activity recognition
Machine learning
Moving object recognition
Robotics
Smart buildings
Ubiquitous computing
ISSN:1546-2226, 1546-2218, 1546-2226
Online Access:Get full text
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Summary: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 sensor data in a smart home. HAR assists artificial intelligence in providing services to users according to their preferences. The sync-LSTM algorithm improves learning performance and sees its potential for real-world applications in complex HAR, such as concurrent activity, with higher accuracy and satisfactory computational complexity. The adapted algorithm for HAR is also applicable in the fields of ambient assistive living, healthcare, robotics, pervasive computing, and astronomy. Extensive experimental evaluation with publicly available datasets demonstrates the competitive recognition capabilities of our approach. The sync-LSTM algorithm improves learning performance and has the potential for real-life applications in complex HAR. For concurrent activity recognition, our proposed method shows an accuracy of more than 97%.
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ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2021.015660