Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN
Gait recognition with wearable sensors is an effective approach to identifying people by recognizing their distinctive walking patterns. Deep learning-based networks have recently emerged as a promising technique in gait recognition, yielding better performance than template matching and traditional...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9758696/ |
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| author | Md. Al Mehedi Hasan Fuad Al Abir Md. Al Siam Jungpil Shin |
| author_facet | Md. Al Mehedi Hasan Fuad Al Abir Md. Al Siam Jungpil Shin |
| author_sort | Md. Al Mehedi Hasan |
| collection | DOAJ |
| description | Gait recognition with wearable sensors is an effective approach to identifying people by recognizing their distinctive walking patterns. Deep learning-based networks have recently emerged as a promising technique in gait recognition, yielding better performance than template matching and traditional machine learning methods. However, most recent studies have focused on improving gait detection accuracy while neglecting model complexity in the deep learning domain, making them unsuitable for low-power wearable devices. Therefore, inference from these models results in latency due to calculation overhead. This study proposes an efficient network suitable for wearable devices without sacrificing prediction performance. We have modified the residual block and accumulated it in shallow convolutional neural networks with five weighted layers only for gait recognition and proved the efficacy of all the architectural components with extensive experiments over publicly available IMU-based datasets: whuGait and OU-ISIR. Our proposed model outperforms all the state-of-the-art methods regarding recognition accuracy and is more than 85 percent efficient on average in terms of model parameters and memory consumption. |
| format | Article |
| id | doaj-art-575ae9a97a9d492abbb4ae9ee98e7b57 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-575ae9a97a9d492abbb4ae9ee98e7b572024-12-11T00:02:39ZengIEEEIEEE Access2169-35362022-01-0110425774258810.1109/ACCESS.2022.31680199758696Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNNMd. Al Mehedi Hasan0https://orcid.org/0000-0003-2966-7055Fuad Al Abir1https://orcid.org/0000-0002-9091-3078Md. Al Siam2Jungpil Shin3https://orcid.org/0000-0002-7476-2468School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, JapanDepartment of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshSchool of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, JapanGait recognition with wearable sensors is an effective approach to identifying people by recognizing their distinctive walking patterns. Deep learning-based networks have recently emerged as a promising technique in gait recognition, yielding better performance than template matching and traditional machine learning methods. However, most recent studies have focused on improving gait detection accuracy while neglecting model complexity in the deep learning domain, making them unsuitable for low-power wearable devices. Therefore, inference from these models results in latency due to calculation overhead. This study proposes an efficient network suitable for wearable devices without sacrificing prediction performance. We have modified the residual block and accumulated it in shallow convolutional neural networks with five weighted layers only for gait recognition and proved the efficacy of all the architectural components with extensive experiments over publicly available IMU-based datasets: whuGait and OU-ISIR. Our proposed model outperforms all the state-of-the-art methods regarding recognition accuracy and is more than 85 percent efficient on average in terms of model parameters and memory consumption.https://ieeexplore.ieee.org/document/9758696/Computational efficiencygait recognitionlightweight CNNmemory-usage reductionparameter reductionresidual learning |
| spellingShingle | Md. Al Mehedi Hasan Fuad Al Abir Md. Al Siam Jungpil Shin Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN IEEE Access Computational efficiency gait recognition lightweight CNN memory-usage reduction parameter reduction residual learning |
| title | Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN |
| title_full | Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN |
| title_fullStr | Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN |
| title_full_unstemmed | Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN |
| title_short | Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN |
| title_sort | gait recognition with wearable sensors using modified residual block based lightweight cnn |
| topic | Computational efficiency gait recognition lightweight CNN memory-usage reduction parameter reduction residual learning |
| url | https://ieeexplore.ieee.org/document/9758696/ |
| work_keys_str_mv | AT mdalmehedihasan gaitrecognitionwithwearablesensorsusingmodifiedresidualblockbasedlightweightcnn AT fuadalabir gaitrecognitionwithwearablesensorsusingmodifiedresidualblockbasedlightweightcnn AT mdalsiam gaitrecognitionwithwearablesensorsusingmodifiedresidualblockbasedlightweightcnn AT jungpilshin gaitrecognitionwithwearablesensorsusingmodifiedresidualblockbasedlightweightcnn |