Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person
Abstract Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilized the biomedical signals acquired from th...
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Nature Portfolio
2024-12-01
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| Online Access: | https://doi.org/10.1038/s41598-024-82624-z |
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| author | Mustufa Haider Abidi |
| author_facet | Mustufa Haider Abidi |
| author_sort | Mustufa Haider Abidi |
| collection | DOAJ |
| description | Abstract Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilized the biomedical signals acquired from the human body's particular organ, cells, or tissues. The human motion intention prediction mechanism plays an essential role in various applications, such as assistive and rehabilitation robots, that execute specific tasks among elders and physically impaired individuals. However, more complications are increased in the human–machine-based interaction techniques, creating more scope for personalized assistance for the human motion intention prediction system. Therefore, in this paper, an Adaptive Hybrid Network (AHN) is implemented for effective human motion intention prediction. Initially, multimodal data like electroencephalogram (EEG)/Electromyography (EMG) signals and sensor measures data are collected from the available data resource. The gathered EEG/EMG signals are then converted into spectrogram images and sent to AH-CNN-LSTM, which is the integration of an Adaptive Hybrid Convolution Neural Network (AH-CNN) with a Long Short-Term Memory (LSTM) network. Similarly, the data details of sensor measures are directly subjected to AH-CNN-Res-LSTM, which is the combination of Adaptive Hybrid CNN with Residual Network and LSTM (Res-LSTM) to get the predictive result. Further, to enhance the prediction, the parameters in both the AH-CNN-LSTM and AH-CNN-Res-LSTM techniques are optimized using the Improved Yellow Saddle Goatfish Algorithm (IYSGA). The efficiency of the implemented model is computed by conducting the comparison experiment of the proposed technique with other standard models. The performance outcome of the developed method outperformed the other traditional methods. |
| format | Article |
| id | doaj-art-09bcaffde6c64b1ea55d417deec22d02 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-09bcaffde6c64b1ea55d417deec22d022025-08-20T02:43:25ZengNature PortfolioScientific Reports2045-23222024-12-0114112710.1038/s41598-024-82624-zMultimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged personMustufa Haider Abidi0Advanced Manufacturing Institute, King Saud UniversityAbstract Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilized the biomedical signals acquired from the human body's particular organ, cells, or tissues. The human motion intention prediction mechanism plays an essential role in various applications, such as assistive and rehabilitation robots, that execute specific tasks among elders and physically impaired individuals. However, more complications are increased in the human–machine-based interaction techniques, creating more scope for personalized assistance for the human motion intention prediction system. Therefore, in this paper, an Adaptive Hybrid Network (AHN) is implemented for effective human motion intention prediction. Initially, multimodal data like electroencephalogram (EEG)/Electromyography (EMG) signals and sensor measures data are collected from the available data resource. The gathered EEG/EMG signals are then converted into spectrogram images and sent to AH-CNN-LSTM, which is the integration of an Adaptive Hybrid Convolution Neural Network (AH-CNN) with a Long Short-Term Memory (LSTM) network. Similarly, the data details of sensor measures are directly subjected to AH-CNN-Res-LSTM, which is the combination of Adaptive Hybrid CNN with Residual Network and LSTM (Res-LSTM) to get the predictive result. Further, to enhance the prediction, the parameters in both the AH-CNN-LSTM and AH-CNN-Res-LSTM techniques are optimized using the Improved Yellow Saddle Goatfish Algorithm (IYSGA). The efficiency of the implemented model is computed by conducting the comparison experiment of the proposed technique with other standard models. The performance outcome of the developed method outperformed the other traditional methods.https://doi.org/10.1038/s41598-024-82624-zHuman motion intention predictionMultimodal dataImproved yellow saddle goatfish algorithmAdaptive hybrid convolution neural network with long short-term memoryAdaptive hybrid convolution neural network with residual network and long short-term memory |
| spellingShingle | Mustufa Haider Abidi Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person Scientific Reports Human motion intention prediction Multimodal data Improved yellow saddle goatfish algorithm Adaptive hybrid convolution neural network with long short-term memory Adaptive hybrid convolution neural network with residual network and long short-term memory |
| title | Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person |
| title_full | Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person |
| title_fullStr | Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person |
| title_full_unstemmed | Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person |
| title_short | Multimodal data-based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person |
| title_sort | multimodal data based human motion intention prediction using adaptive hybrid deep learning network for movement challenged person |
| topic | Human motion intention prediction Multimodal data Improved yellow saddle goatfish algorithm Adaptive hybrid convolution neural network with long short-term memory Adaptive hybrid convolution neural network with residual network and long short-term memory |
| url | https://doi.org/10.1038/s41598-024-82624-z |
| work_keys_str_mv | AT mustufahaiderabidi multimodaldatabasedhumanmotionintentionpredictionusingadaptivehybriddeeplearningnetworkformovementchallengedperson |