Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition
With the growth in the wearable device market, wearable sensor-based human activity recognition systems have been gaining increasing interest in research because of their rising demands in many areas. This research presents a novel sensor-based human activity recognition system that utilizes a uniqu...
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| مؤلفون آخرون: | |
| التنسيق: | article |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/25564 |
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| _version_ | 1864513444170956800 |
|---|---|
| author | Albadawi, Yaman |
| author2 | Shanableh, Tamer |
| author2_role | author |
| author_facet | Albadawi, Yaman Shanableh, Tamer |
| author_role | author |
| dc.creator.none.fl_str_mv | Albadawi, Yaman Shanableh, Tamer |
| dc.date.none.fl_str_mv | 2024-07-21T08:25:11Z 2024-07-21T08:25:11Z 2024-07-10 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Albadawi, Y., & Shanableh, T. (2024). Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition. In IEEE Sensors Journal (pp. 1–1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/jsen.2024.3422272 1558-1748 https://hdl.handle.net/11073/25564 10.1109/JSEN.2024.3422272 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/JSEN.2024.3422272 |
| dc.subject.none.fl_str_mv | Bidirectional long-short-term-memory Long-short-term-memory Sensor-based human activity recognition Attention Time-series differencing |
| dc.title.none.fl_str_mv | Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition |
| dc.type.none.fl_str_mv | Peer-Reviewed Postprint info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | With the growth in the wearable device market, wearable sensor-based human activity recognition systems have been gaining increasing interest in research because of their rising demands in many areas. This research presents a novel sensor-based human activity recognition system that utilizes a unique feature extraction technique associated with a deep learning method for classification. One of the main contributions of this work is dividing the sensor sequences time-wise into non-overlapping 2D segments. Then, statistical features are computed from each 2D segment using two approaches; the first approach computes features from the raw sensor readings, while the second approach applies time-series differencing to sensor readings prior to feature calculations. Applying time-series differencing to 2D segments helps in identifying the underlying structure and dynamics of the sensor reading across time. This work experiments with different numbers of 2D segments of sensor reading sequences. Also, it reports results with and without the use of different components of the proposed system. Additionally, it analyses the best-performing models’ complexity, comparing them with other models trained by integrating the proposed method with an existing transformer network. All of these arrangements are tested with different deep-learning architectures supported by an attention layer to enhance the model. Four benchmark datasets are used to perform several experiments, namely, mHealth, USC-HAD, UCI-HAR, and DSA. The experimental results revealed that the proposed system outperforms human activity recognition rates reported in the most recent studies. Specifically, this work reports recognition rates of 99.17%, 81.07%, 99.44%, and 94.03% for the four datasets, respectively. |
| format | article |
| id | aus_491b48522bf84b04a96b6b13f0ecc3dc |
| identifier_str_mv | Albadawi, Y., & Shanableh, T. (2024). Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition. In IEEE Sensors Journal (pp. 1–1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/jsen.2024.3422272 1558-1748 10.1109/JSEN.2024.3422272 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25564 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity RecognitionAlbadawi, YamanShanableh, TamerBidirectional long-short-term-memoryLong-short-term-memorySensor-based human activity recognitionAttentionTime-series differencingWith the growth in the wearable device market, wearable sensor-based human activity recognition systems have been gaining increasing interest in research because of their rising demands in many areas. This research presents a novel sensor-based human activity recognition system that utilizes a unique feature extraction technique associated with a deep learning method for classification. One of the main contributions of this work is dividing the sensor sequences time-wise into non-overlapping 2D segments. Then, statistical features are computed from each 2D segment using two approaches; the first approach computes features from the raw sensor readings, while the second approach applies time-series differencing to sensor readings prior to feature calculations. Applying time-series differencing to 2D segments helps in identifying the underlying structure and dynamics of the sensor reading across time. This work experiments with different numbers of 2D segments of sensor reading sequences. Also, it reports results with and without the use of different components of the proposed system. Additionally, it analyses the best-performing models’ complexity, comparing them with other models trained by integrating the proposed method with an existing transformer network. All of these arrangements are tested with different deep-learning architectures supported by an attention layer to enhance the model. Four benchmark datasets are used to perform several experiments, namely, mHealth, USC-HAD, UCI-HAR, and DSA. The experimental results revealed that the proposed system outperforms human activity recognition rates reported in the most recent studies. Specifically, this work reports recognition rates of 99.17%, 81.07%, 99.44%, and 94.03% for the four datasets, respectively.American University of SharjahIEEE2024-07-21T08:25:11Z2024-07-21T08:25:11Z2024-07-10Peer-ReviewedPostprintinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAlbadawi, Y., & Shanableh, T. (2024). Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition. In IEEE Sensors Journal (pp. 1–1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/jsen.2024.34222721558-1748https://hdl.handle.net/11073/2556410.1109/JSEN.2024.3422272en_UShttps://doi.org/10.1109/JSEN.2024.3422272oai:repository.aus.edu:11073/255642024-08-22T12:07:53Z |
| spellingShingle | Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition Albadawi, Yaman Bidirectional long-short-term-memory Long-short-term-memory Sensor-based human activity recognition Attention Time-series differencing |
| status_str | publishedVersion |
| title | Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition |
| title_full | Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition |
| title_fullStr | Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition |
| title_full_unstemmed | Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition |
| title_short | Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition |
| title_sort | Hand-Crafted Features With A Simple Deep Learning Architecture For Sensor-Based Human Activity Recognition |
| topic | Bidirectional long-short-term-memory Long-short-term-memory Sensor-based human activity recognition Attention Time-series differencing |
| url | https://hdl.handle.net/11073/25564 |