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MEMS Devices-Based Hand Gesture Recognition via Wearable Computing

Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, o...

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Autores principales: Wang, Huihui, Ru, Bo, Miao, Xin, Gao, Qin, Habib, Masood, Liu, Long, Qiu, Sen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223752/
https://www.ncbi.nlm.nih.gov/pubmed/37241571
http://dx.doi.org/10.3390/mi14050947
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author Wang, Huihui
Ru, Bo
Miao, Xin
Gao, Qin
Habib, Masood
Liu, Long
Qiu, Sen
author_facet Wang, Huihui
Ru, Bo
Miao, Xin
Gao, Qin
Habib, Masood
Liu, Long
Qiu, Sen
author_sort Wang, Huihui
collection PubMed
description Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and normalization algorithms. Magnetometer correction is performed using ellipsoidal fitting methods. An auxiliary segmentation algorithm is employed to segment the gesture data, and a gesture dataset is constructed. For static gesture recognition, we focus on four machine learning algorithms, namely support vector machine (SVM), backpropagation neural network (BP), decision tree (DT), and random forest (RF). We evaluate the model prediction performance through cross-validation comparison. For dynamic gesture recognition, we investigate the recognition of 10 dynamic gestures using Hidden Markov Models (HMM) and Attention-Biased Mechanisms for Bidirectional Long- and Short-Term Memory Neural Network Models (Attention-BiLSTM). We analyze the differences in accuracy for complex dynamic gesture recognition with different feature datasets and compare them with the prediction results of the traditional long- and short-term memory neural network model (LSTM). Experimental results demonstrate that the random forest algorithm achieves the highest recognition accuracy and shortest recognition time for static gestures. Moreover, the addition of the attention mechanism significantly improves the recognition accuracy of the LSTM model for dynamic gestures, with a prediction accuracy of 98.3%, based on the original six-axis dataset.
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spelling pubmed-102237522023-05-28 MEMS Devices-Based Hand Gesture Recognition via Wearable Computing Wang, Huihui Ru, Bo Miao, Xin Gao, Qin Habib, Masood Liu, Long Qiu, Sen Micromachines (Basel) Article Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and normalization algorithms. Magnetometer correction is performed using ellipsoidal fitting methods. An auxiliary segmentation algorithm is employed to segment the gesture data, and a gesture dataset is constructed. For static gesture recognition, we focus on four machine learning algorithms, namely support vector machine (SVM), backpropagation neural network (BP), decision tree (DT), and random forest (RF). We evaluate the model prediction performance through cross-validation comparison. For dynamic gesture recognition, we investigate the recognition of 10 dynamic gestures using Hidden Markov Models (HMM) and Attention-Biased Mechanisms for Bidirectional Long- and Short-Term Memory Neural Network Models (Attention-BiLSTM). We analyze the differences in accuracy for complex dynamic gesture recognition with different feature datasets and compare them with the prediction results of the traditional long- and short-term memory neural network model (LSTM). Experimental results demonstrate that the random forest algorithm achieves the highest recognition accuracy and shortest recognition time for static gestures. Moreover, the addition of the attention mechanism significantly improves the recognition accuracy of the LSTM model for dynamic gestures, with a prediction accuracy of 98.3%, based on the original six-axis dataset. MDPI 2023-04-27 /pmc/articles/PMC10223752/ /pubmed/37241571 http://dx.doi.org/10.3390/mi14050947 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Huihui
Ru, Bo
Miao, Xin
Gao, Qin
Habib, Masood
Liu, Long
Qiu, Sen
MEMS Devices-Based Hand Gesture Recognition via Wearable Computing
title MEMS Devices-Based Hand Gesture Recognition via Wearable Computing
title_full MEMS Devices-Based Hand Gesture Recognition via Wearable Computing
title_fullStr MEMS Devices-Based Hand Gesture Recognition via Wearable Computing
title_full_unstemmed MEMS Devices-Based Hand Gesture Recognition via Wearable Computing
title_short MEMS Devices-Based Hand Gesture Recognition via Wearable Computing
title_sort mems devices-based hand gesture recognition via wearable computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10223752/
https://www.ncbi.nlm.nih.gov/pubmed/37241571
http://dx.doi.org/10.3390/mi14050947
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