Enhancing Rehabilitation in Stroke Survivors: A Deep Learning Approach to Assess Upper Extremity Movement Using Accelerometry Data

Assessments DO NOTHING unless you map EXACT RECOVERY PROTOCOLS TO THEM! This was absolutely useless, NOTHING ON PROTOCOLS THAT WILL DELIVER RECOVERY!  

 Enhancing Rehabilitation in Stroke Survivors: A Deep Learning Approach to Assess Upper Extremity Movement Using Accelerometry Data

Tan  TranTan Tran1*Lin-Ching  ChangLin-Ching Chang1,2Peter  LumPeter Lum3

1Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, D.C., United States

2Department of Informatics, New Jersey Institute of Technology, Newark, New Jersey, United States

3Department of Biomedical Engineering, The Catholic University of America, Washington, D.C., United States

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Upper Extremity (UE) rehabilitation is crucial for stroke survivors, aiming to improve the use of the paretic UE in everyday activities. However, assessing the effectiveness of these treatments is challenging due to a lack of objective measurement tools. Traditional methods, such as clinician-rated motor ability or patient self-reports, often fail to measure UE performance in real-life settings accurately. Evidence suggests that currently used clinical assessments do not reliably capture actual UE use at home or in the community. This study investigates the application of Convolutional Neural Networks (CNNs) combined with Dense layers using accelerometry data from wrist-worn sensors to classify functional and non-functional UE movements of stroke survivors. Two types of models were developed: one trained on data from individual subjects (intrasubject model) and another trained on data across all subjects (intersubject model). The intrasubject model for the paretic UE achieved an average accuracy of 0.90 ± 0.05, while the intersubject model reached an accuracy of 0.79 ± 0.06. When incorporating signals from the non-paretic arm, the intersubject model's accuracy improves to 0.88 ± 0.10. Notably, this method utilized raw accelerometry data, eliminating the need for manual feature extraction, which is commonly required in traditional machine learning, and yielded higher accuracy than previously reported methods. This proposed deep learning approach incorporates CNNs with Dense layers, offering a cost-effective and adaptable method for monitoring UE functionality in real-world settings. The results from this study have the potential to inform the development of personalized rehabilitation strategies for stroke survivors, offering valuable insights for clinical practice.