I read everything in your results and conclusion section and couldn't make heads or tails if this provided recovery or not! USELESS!
Revolutionizing Stroke Rehabilitation: Dynamic Glove-Based
Rehabilitation System Empowered by CNN for Spastic Hands
Mohamed A. Massoud1,*, Gehan A. Mahmoud2, Ali W. Y3, and Wael
Abouelwafa Ahmed1
*1Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, 61519, Egypt.
2D epartment of Rheumatology and Rehabilitation, Faculty of Medicine, Minia University
3 3Department of Production Engineering and Mechanical Design, Faculty of Engineering, Minia Univer-
sity, Minia 61111, Egypt.
*Corresponding author(s). E-mail(s): dr.massoud@mu.edu.eg
Contributing authors: wahyos@hotmail.com; gehanabdelwahab@yahoo.com; wael.wafa@minia.edu.eg
a camera, a telecom unit, and a computer unit. Biometric measurement gloves with sensors
measure patient features. Data inputs include biometric measurements and camera captured images. Computer programs consist of a clinical biometric program and a CNN program, specifically ResNet50 architecture. The telecom unit facilitates communication between the computer unit and rehabilittion gloves, doctor section, and patient section.
The smart rehabilitation system offers advantages such as user-friendly operation,
cost-effectiveness, elimination of physical visits to rehabilitation centers, and exceptional
accuracy with a 99% validation accuracy rate and 0.0053 validation loss in the CNN frame-
work. The clinical biometric program is used to analyze programs with high accuracy. This
study presents an innovative rehabilitation system. It includes biometric measurement
gloves for patient assessment and rehabilitation gloves for hand exercises. Two programs, a
clinical biometric program, and an intelligent CNN-based program, diagnose and therapies
based on biometric data and image analysis. The mobile application communicates be-
tween the system, patients, and healthcare providers.
Keywords: Spasticity, Dynamic splint, soft gloves, CNN, a smart rehabilitation system.
1. Introduction
Abouelwafa Ahmed1
*1Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, 61519, Egypt.
2D epartment of Rheumatology and Rehabilitation, Faculty of Medicine, Minia University
3 3Department of Production Engineering and Mechanical Design, Faculty of Engineering, Minia Univer-
sity, Minia 61111, Egypt.
*Corresponding author(s). E-mail(s): dr.massoud@mu.edu.eg
Contributing authors: wahyos@hotmail.com; gehanabdelwahab@yahoo.com; wael.wafa@minia.edu.eg
Abstract
Hand spasticity poses a significant challenge for stroke survivors, impacting hand functionality and hindering daily activities. The study introduces a smart rehabilitation system engineered for post-stroke hand spasticity. Comprising four units includes biometric measurement gloves, rehabilitation gloves,a camera, a telecom unit, and a computer unit. Biometric measurement gloves with sensors
measure patient features. Data inputs include biometric measurements and camera captured images. Computer programs consist of a clinical biometric program and a CNN program, specifically ResNet50 architecture. The telecom unit facilitates communication between the computer unit and rehabilittion gloves, doctor section, and patient section.
The smart rehabilitation system offers advantages such as user-friendly operation,
cost-effectiveness, elimination of physical visits to rehabilitation centers, and exceptional
accuracy with a 99% validation accuracy rate and 0.0053 validation loss in the CNN frame-
work. The clinical biometric program is used to analyze programs with high accuracy. This
study presents an innovative rehabilitation system. It includes biometric measurement
gloves for patient assessment and rehabilitation gloves for hand exercises. Two programs, a
clinical biometric program, and an intelligent CNN-based program, diagnose and therapies
based on biometric data and image analysis. The mobile application communicates be-
tween the system, patients, and healthcare providers.
Keywords: Spasticity, Dynamic splint, soft gloves, CNN, a smart rehabilitation system.
1. Introduction
Worldwide, the incidence of stroke has increased resulting in disability and death [1]. Affection of Hand function and activities of daily living (ADL) and independence occurs in many stroke patients [2,3].
Stroke or cerebrovascular disease (CVA) is characterized by interruption of the cerebral circulation at any part by occlusion or rupture of blood vessels [4]. CVA is the main cause of disabilities affecting mid to late adulthood worldwide [4–6]. Post-stroke, the spasticity of upper limbs or lower limbs develops depending on the affected area of cerebral circulation [7, 8]. Spasticity frequently affects the upper limbs as the middle cerebral artery is the most affected artery by stroke [9, 10]. Spasticity resulting from hyperexcitability of the stretch reflex is characterized by an increase in tonic stretch reflexes and muscle tone with exaggerated tendon jerks [11]. Spasticity of the upper limb in stroke patients poses functional challenges in doing activities of daily living [10].
If spasticity is not treated muscle weakness, muscle atrophy as well as poor hand functional skills occur in the affected upper limb [10]. Hand spasticity is called ‘flexor synergy’ which is characterized by flexion at the elbow, wrist, and finger joints combined with internal rotation and adduction of the shoulder [12, 13]. This causes unequal forces between the agonist and antagonist muscles of the upper limbs resulting in the static joint position and dynamic limb movements [12]. Neglecting to treat upper limb spasticity results in contractures due to abnormal shortening of the soft tissue structures surrounding the joints such as joint capsules, ligaments; tendons; muscles, and the skin [14]. mobilization and spasticity resulting in musculoskeletal tightness which decreases the functional recovery of the hand [15]. When left in the immobilized state with the flexor synergy; the condition of the upper limbs progresses to a fibrotic state which triggers the development of early contractures [15]. Pain: fibrosis, contracture, movement disorders, and muscle weakness usually accompanied by spasticity. One of the rehabilitation treatments in stroke patients is using dynamic splints to reduce spasticity of the Hand and maintain muscle tissue length [16], It increases joint range of motion by providing a low-load prolonged timed tissue stretch. Dynamic splints can improve hand function by maintaining the peripheral muscle and joint structures at a functional length [15]. Hand mobility should be facilitated by using dynamic splints which stretch the muscles; tendons and ligaments to maintain their length, so reducing spasticity [14]. Spasticity and impaired hand motor skills can be treated by harnessing the plasticity property of the brain through mass movement and task-oriented arm training [14]. Hand splints can be used to train motor learning and improve neural plasticity in
the brain [15]. Using a soft robotic glove as an assistive device was studied before [17–21]. Different wearable hand robots have been developed recently to assist hand function. Soft robotic gloves, exoskeletons lightweight, and low-cost were developed for hand rehabilitation after stroke [22, 23].
In recent years, there has been a growing interest in developing innovative technologies to revolutionize stroke rehabilitation. Among these advancements, the integration of wearable devices and artificial intelligence (AI) techniques has shown promise in creating personalized and effective rehabilitation strategies.[24–31]Specifically, the utilization of dynamic glove-based rehabilitation systems empowered by Convolutional Neural Networks (CNNs) presents a novel approach to address the challenges associated with spastic hands post-stroke. The integration of CNN technology into the rehabilitation system enables continuous learning and adaptation. By analyzing patterns in hand movements and muscle responses, the CNN algorithm can dynamically adjust the rehabilitation regimen, optimizing the therapy's effectiveness over time. Moreover, the system's user-friendly interface and interactive feedback mechanisms engage stroke survivors actively in their rehabilitation journey, promoting motivation and adherence to the prescribed exercises.[32–34]
Stroke or cerebrovascular disease (CVA) is characterized by interruption of the cerebral circulation at any part by occlusion or rupture of blood vessels [4]. CVA is the main cause of disabilities affecting mid to late adulthood worldwide [4–6]. Post-stroke, the spasticity of upper limbs or lower limbs develops depending on the affected area of cerebral circulation [7, 8]. Spasticity frequently affects the upper limbs as the middle cerebral artery is the most affected artery by stroke [9, 10]. Spasticity resulting from hyperexcitability of the stretch reflex is characterized by an increase in tonic stretch reflexes and muscle tone with exaggerated tendon jerks [11]. Spasticity of the upper limb in stroke patients poses functional challenges in doing activities of daily living [10].
If spasticity is not treated muscle weakness, muscle atrophy as well as poor hand functional skills occur in the affected upper limb [10]. Hand spasticity is called ‘flexor synergy’ which is characterized by flexion at the elbow, wrist, and finger joints combined with internal rotation and adduction of the shoulder [12, 13]. This causes unequal forces between the agonist and antagonist muscles of the upper limbs resulting in the static joint position and dynamic limb movements [12]. Neglecting to treat upper limb spasticity results in contractures due to abnormal shortening of the soft tissue structures surrounding the joints such as joint capsules, ligaments; tendons; muscles, and the skin [14]. mobilization and spasticity resulting in musculoskeletal tightness which decreases the functional recovery of the hand [15]. When left in the immobilized state with the flexor synergy; the condition of the upper limbs progresses to a fibrotic state which triggers the development of early contractures [15]. Pain: fibrosis, contracture, movement disorders, and muscle weakness usually accompanied by spasticity. One of the rehabilitation treatments in stroke patients is using dynamic splints to reduce spasticity of the Hand and maintain muscle tissue length [16], It increases joint range of motion by providing a low-load prolonged timed tissue stretch. Dynamic splints can improve hand function by maintaining the peripheral muscle and joint structures at a functional length [15]. Hand mobility should be facilitated by using dynamic splints which stretch the muscles; tendons and ligaments to maintain their length, so reducing spasticity [14]. Spasticity and impaired hand motor skills can be treated by harnessing the plasticity property of the brain through mass movement and task-oriented arm training [14]. Hand splints can be used to train motor learning and improve neural plasticity in
the brain [15]. Using a soft robotic glove as an assistive device was studied before [17–21]. Different wearable hand robots have been developed recently to assist hand function. Soft robotic gloves, exoskeletons lightweight, and low-cost were developed for hand rehabilitation after stroke [22, 23].
In recent years, there has been a growing interest in developing innovative technologies to revolutionize stroke rehabilitation. Among these advancements, the integration of wearable devices and artificial intelligence (AI) techniques has shown promise in creating personalized and effective rehabilitation strategies.[24–31]Specifically, the utilization of dynamic glove-based rehabilitation systems empowered by Convolutional Neural Networks (CNNs) presents a novel approach to address the challenges associated with spastic hands post-stroke. The integration of CNN technology into the rehabilitation system enables continuous learning and adaptation. By analyzing patterns in hand movements and muscle responses, the CNN algorithm can dynamically adjust the rehabilitation regimen, optimizing the therapy's effectiveness over time. Moreover, the system's user-friendly interface and interactive feedback mechanisms engage stroke survivors actively in their rehabilitation journey, promoting motivation and adherence to the prescribed exercises.[32–34]
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