Abstract
Upper limb spasticity (ULS) is a medical condition characterized by an increase in muscle tone and joint stiffness upon passive joint movement. Modified Ashworth Scale (MAS) is an assessment tool to classify the severity level of spasticity. An ULS Smart Diagnosis system based on the MAS can assist the assessment process in clinical setting. A graphical user interface (GUI) has been developed to automate the data processing steps into a user-friendly interface, enabling clinicians without technical background to run data-driven machine learning models for the diagnosis of upper limb spasticity. The user interface is designed with the features of receiving sensor data and transfer it to data science platform, record and edit patient personal information, and display diagnosis with assessment result received from the machine learning models on the data science platform.
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Acknowledgments
The authors thank the Ministry of Science, Technology and Innovation Malaysia (MOSTI) for funding this project under the International Collaboration Fund [IF1118C1042: Data Science Platform for Smart Diagnosis of Upper Limb Spasticity], Universiti Tun Hussein Onn Malaysia and the German Federal Ministry of Education and Research [RAISE-MED: Research Alliance for Intelligent Systems in Medical Technology in Malaysia]. The clinical data collection was conducted using the Research Ethics Approval [600-IRMI (5/1/6)] from UiTM Research Ethics Committee.
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Hoe, Z.Y. et al. (2022). User Interface Design for Upper Limb Spasticity Smart Diagnosis System. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_120
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DOI: https://doi.org/10.1007/978-981-16-8129-5_120
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