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Identification of phantom movements with an ensemble learning approach

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journal contribution
submitted on 2023-11-22, 11:22 and posted on 2023-11-22, 12:24 authored by Akhan Akbulut, Feray Gungor, Ela Tarakci, Muhammed Ali Aydin, Abdul Halim Zaim, Cagatay Catal

Phantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.

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Published in: Computers in Biology and Medicine
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Open Access funding provided by the Qatar National Library.



  • English



Publication Year

  • 2022

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This Item is licensed under the Creative Commons Attribution 4.0 International License.

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  • Qatar University
  • College of Engineering - QU

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