Machine Learning Rehabilitation for Stroke Patients

Ramesh Prabhakaran R.^1,*, Angel Maanu P.², Niranjan G.³, Karthika K.⁴

¹Department of Computer Engineering, Mizoram University, Mizoram, India

²Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, India

³Department of Mechanical Engineering, Panimalar Engineering College, Chennai, India

⁴Department of Information Technology, Karpagam College of Engineering, Coimbatore, India

Emails: rkrrameshsamy5@gmail.com; maanuangel87@gmail.com; janniran00@gmail.com; karthikakit141@gmail.com

Abstract

This study explores the use of algorithmic for learning (ML) techniques in stroke rehabilitation to enhance patient outcomes and care. Machine learning offers potential uses in outcome prediction, progress tracking, customized treatment planning, and assessment. Algorithms based on machine learning (ML) can assist doctors with seriousness of stroke assessment, which is treatment plan customization, monitoring of progress, and long-term result prediction by leveraging a range of data sources, such as sensor data, doctor's notes, and medical images. Through personalized interventions and timely feedback, machine learning (ML) can optimize rehabilitation efforts and improve the standard of life for stroke patients. Interdisciplinary cooperation and ethical considerations are required to ensure the responsible and effective application of ML in physiotherapy after a stroke treatment. This study highlights the significant impact on the treatment of patients and their outcomes as it investigates the potential applications of algorithms for learning (ML) in recovery from stroke. These applications include result prediction, customized treatment planning, assessment methods, and progress monitoring. Through a convergence of current research findings and technological advancements, we illustrate how machine learning (ML) approaches can exploit many information modalities to assist professionals in providing tailored rehabilitation therapies and optimizing patient care. Despite the benefits that seem obvious, adoption needs to be fair and responsible. Problems like algorithmic bias, concerns about data privacy, and barriers to integrating clinical information need to be fixed.

Keywords: Stroke Rehabilitation; Neural Networks; Regression Model; Wearable Sensors; Adaptive Therapy