Assessments and sensors DO NOTHING to get survivors recovered. You need proper EXACT PROTOCOLS that those sensors will tell you if survivors are following them correctly. First you create protocols, then you can measure stuff. DO THINGS IN THE CORRECT ORDER! Are your mentors and senior researchers that blitheringly stupid!
Oops, I'm not playing by the polite rules of Dale Carnegie, 'How to Win Friends and Influence People'.
Telling supposedly smart stroke medical persons they know nothing about stroke is a no-no even if it is true.
Politeness will never solve anything in stroke. Yes, I'm a bomb thrower and proud of it. Someday a stroke 'leader' will try to ream me out for making them look bad by being truthful, I look forward to that day.
A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2024.DOI
A Survey of Wearable Sensors and
Machine Learning Algorithms for
Automated Stroke Rehabilitation
NANDINI SENGUPTA1, ARAVINDA S. RAO1, (Senior Member, IEEE), BERNARD YAN2, AND
MARIMUTHU PALANISWAMI.1, (Fellow, IEEE)
1 Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria—3010, Australia (e-mail:
nsengupta@student.unimelb.edu.au, aravinda.rao@unimelb.edu.au, palani@unimelb.edu.au)
2 The Royal Melbourne Hospital, Parkville, Victoria—3052, Australia (e-mail: bernard.yan@mh.org.au)
Corresponding author: Nandini Sengupta (e-mail: nsengupta@student.unimelb.edu.au).
This research was supported partially by the Australian Government through the Australian Research Council’s Discovery Projects funding
scheme (project DP190101248). The views expressed herein are those of the authors and are not necessarily those of the Australian
Government or Australian Research Council.
Digital Object Identifier 10.1109/ACCESS.2024.DOI
A Survey of Wearable Sensors and
Machine Learning Algorithms for
Automated Stroke Rehabilitation
NANDINI SENGUPTA1, ARAVINDA S. RAO1, (Senior Member, IEEE), BERNARD YAN2, AND
MARIMUTHU PALANISWAMI.1, (Fellow, IEEE)
1 Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria—3010, Australia (e-mail:
nsengupta@student.unimelb.edu.au, aravinda.rao@unimelb.edu.au, palani@unimelb.edu.au)
2 The Royal Melbourne Hospital, Parkville, Victoria—3052, Australia (e-mail: bernard.yan@mh.org.au)
Corresponding author: Nandini Sengupta (e-mail: nsengupta@student.unimelb.edu.au).
This research was supported partially by the Australian Government through the Australian Research Council’s Discovery Projects funding
scheme (project DP190101248). The views expressed herein are those of the authors and are not necessarily those of the Australian
Government or Australian Research Council.
ABSTRACT
Stroke is one of the leading causes of disability among the elderly population and is a
significant public health problem worldwide. The main impact of stroke is functional disabilities due to motor impairment after stroke. Advances in modern medicine and technology have significantly improved diagnosis and treatment; however, most post-stroke care is based on the effectiveness of rehabilitation. Stroke rehabilitation depends on two main components: (i) training (or therapy) to restore the patient to pre-stroke mobility and (ii) assessing motor functionality of affected patients performing activities to track motor recovery. This article highlights how combining wearable devices and machine learning (ML)produces new pathways for effective stroke rehabilitation. While wearable devices help capture patient movements at much finer time resolutions, ML allows us to build predictive models from wearable data
to assist clinicians in diagnosis and treatments. Specifically, we expand on how wearable devices and ML can improve monitoring quality in training intervention, assessment, and remote monitoring. In addition, we provide our main findings from the literature, research challenges, and future directions in post-stroke therapies using wearable devices and ML.
INDEX TERMS Stroke rehabilitation, wearable devices, machine learning, interventions, remote monitor-
ing.
I. INTRODUCTION
STROKE is the second leading cause of death, and 17 million people worldwide suffer from stroke each year[1]. Stroke can have devastating effects, including death or severe disability, which can cause social and family burdens. Even though the majority of the victims are older adults, the number of people 60 years of age or older is projected to increase from an estimated 488 million in 1990 to nearly 1,363 million in 2030 [2]. Since stroke is the leading cause
of adult disability in the world and 70–85% of stroke patients have hemiplegia after the first stroke [3], motor recovery is one of the most crucial aspects for stroke victims. Wearable
devices, when paired with machine learning (ML), enable us to continuously monitor subjects and ascertain the progressive course towards enhanced motor recovery. It is vital to monitor stroke patients’ daily activities and exercise schedules in the absence of a physician to monitor progression regularly. Wearable devices will enable us to collect patient data continuously, which would otherwise lead to missed opportunities for diagnosis and treatment. Combining ML
with wearable devices will improve the remote monitoring of stroke patients. Figure 1 provides an overview of stroke monitoring using wearable devices, smartphones, cloud computing, and the Internet of Things. To improve and regain mobility following strokes, we require two primary components: (i) more practice and training(including physiotherapist instruction sessions and personalpractice) and (ii) periodic evaluations (to gauge the impact of training). Therefore, monitoring the performance to assess the progress remotely is essential when the patient is at home or practicing exercises alone. It is imperative to conduct objective monitoring without any biases or prejudices. No single measure is capable of predicting all dimensions of recovery and disability [4], despite the availability of several stroke assessment scales to measure functional outcomes at each level of the World Health Organization International Classification of Functioning, Disability, and Health (WHO-ICF) [5]. For further details about stroke assessment scales, readers are referred to Table 1 (with 17 scales for impairment), Table 2 (with 16 scales for activity), and Table 3 (with eight scales for participation restriction class) of the included supplementary material.
significant public health problem worldwide. The main impact of stroke is functional disabilities due to motor impairment after stroke. Advances in modern medicine and technology have significantly improved diagnosis and treatment; however, most post-stroke care is based on the effectiveness of rehabilitation. Stroke rehabilitation depends on two main components: (i) training (or therapy) to restore the patient to pre-stroke mobility and (ii) assessing motor functionality of affected patients performing activities to track motor recovery. This article highlights how combining wearable devices and machine learning (ML)produces new pathways for effective stroke rehabilitation. While wearable devices help capture patient movements at much finer time resolutions, ML allows us to build predictive models from wearable data
to assist clinicians in diagnosis and treatments. Specifically, we expand on how wearable devices and ML can improve monitoring quality in training intervention, assessment, and remote monitoring. In addition, we provide our main findings from the literature, research challenges, and future directions in post-stroke therapies using wearable devices and ML.
INDEX TERMS Stroke rehabilitation, wearable devices, machine learning, interventions, remote monitor-
ing.
I. INTRODUCTION
STROKE is the second leading cause of death, and 17 million people worldwide suffer from stroke each year[1]. Stroke can have devastating effects, including death or severe disability, which can cause social and family burdens. Even though the majority of the victims are older adults, the number of people 60 years of age or older is projected to increase from an estimated 488 million in 1990 to nearly 1,363 million in 2030 [2]. Since stroke is the leading cause
of adult disability in the world and 70–85% of stroke patients have hemiplegia after the first stroke [3], motor recovery is one of the most crucial aspects for stroke victims. Wearable
devices, when paired with machine learning (ML), enable us to continuously monitor subjects and ascertain the progressive course towards enhanced motor recovery. It is vital to monitor stroke patients’ daily activities and exercise schedules in the absence of a physician to monitor progression regularly. Wearable devices will enable us to collect patient data continuously, which would otherwise lead to missed opportunities for diagnosis and treatment. Combining ML
with wearable devices will improve the remote monitoring of stroke patients. Figure 1 provides an overview of stroke monitoring using wearable devices, smartphones, cloud computing, and the Internet of Things. To improve and regain mobility following strokes, we require two primary components: (i) more practice and training(including physiotherapist instruction sessions and personalpractice) and (ii) periodic evaluations (to gauge the impact of training). Therefore, monitoring the performance to assess the progress remotely is essential when the patient is at home or practicing exercises alone. It is imperative to conduct objective monitoring without any biases or prejudices. No single measure is capable of predicting all dimensions of recovery and disability [4], despite the availability of several stroke assessment scales to measure functional outcomes at each level of the World Health Organization International Classification of Functioning, Disability, and Health (WHO-ICF) [5]. For further details about stroke assessment scales, readers are referred to Table 1 (with 17 scales for impairment), Table 2 (with 16 scales for activity), and Table 3 (with eight scales for participation restriction class) of the included supplementary material.
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