Continues online exercise monitoring and assessment system with visual guidance feedback for stroke rehabilitation

Mortazavi, Fatemeh; Nadian-Ghomsheh, Ali

doi:10.1007/s11042-019-08020-2

Cited by 13 publications

(19 citation statements)

References 56 publications

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“…It is a subset of physical activity that includes body conditioning and sports activities [20]. If we decompose the above definition, planned or goal-oriented exercise motions are not reactive to an event but are performed to achieve a specific outcome [21]. Structured or task-oriented exercise motions have common characteristics that are specific to the task at hand [21].…”

Section: A Hmqa During Exercisementioning

confidence: 99%

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.

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Section: A Hmqa During Exercisementioning

confidence: 99%

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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“…The authors studied the accuracy of Kinect for hand ROM, and the results showed there is a low correlation between Kinect reading for hand ROM measurement. This is mostly due to the fact that Kinect was not designed for the extraction of hand joints exclusively [ 29 , 35 ]. Marker-less hand motion capture technology has been less considered for hand rehabilitation.…”

Section: Range Of Motion For Monitoring Physical Rehabilitationmentioning

confidence: 99%

“…In the case of continuous exercise recognition, our approach for upper body ROM exercises explained in [ 29 ] can be applied repeated. Continuous exericess can be considered as a set of ROM motions performed in a sequence.…”

Section: Proposed Solutionmentioning

confidence: 99%

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

Nadian-Ghomsheh

Farahani

Kavian

2021

Multimed Tools Appl

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The healthcare industry requires the integration of digital technologies, such as Artificial Intelligence (AI) and the Internet of Things (IoT), to their full potential, particularly during this challenging time and the recent outbreak of the COVID-19 pandemic, which resulted in the disruptions in healthcare delivery, service operations, and shortage of healthcare personnel. However, every opportunity has barriers and bumps, and when it comes to IoT healthcare, data privacy is one of the main growing issues. Despite the recent advances in the development of IoT healthcare architectures, most of them are invasive for the data subjects. In this context, the broad applications of AI in the IoT domain have also been hindered by emerging strict legal and ethical requirements to protect individual privacy. Camera-based solutions that monitor human subjects in everyday settings, e.g., for Online Range of Motion (ROM) detection, are making this problem even worse. One actively practiced branch of such solutions is telerehabilitation, which provides remote solutions for the physically impaired to regain their strength and get back to their normal daily routines. The process usually involves transmitting video/images from the patient performing rehabilitation exercises and applying Machine Learning (ML) techniques to extract meaningful information to help therapists devise further treatment plans. Thereby, real-time measurement and assessment of rehabilitation exercises in a reliable, accurate, and Privacy-Preserving manner is imperative. To address the privacy issue of existing solutions, this paper proposes a holistic Privacy-Preserving (PP) hierarchical IoT solution that simultaneously addresses the utilization of AI-driven IoT and the demands for data protection. Furthermore, the efficiency of the proposed architecture is demonstrated by a novel machine learning-based system that allows immediate assessment and extraction of ROM as the critical information for analyzing the progress of patients.

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“…Kinect is a composite device that contains an infrared (IR) projector, an IR camera and a color (RGB) camera [16]. Some studies have validated the capability of Kinect as a marker-less motion tracking device for rehabilitation exercises monitoring [17][18][19][20]. These systems capture the user's motions using the Kinect and provide a visual feedback during exercise sessions.…”

Section: Introductionmentioning

confidence: 99%

Exercise Monitoring and Assessment System for Home-Based Respiratory Rehabilitation

et al. 2022

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Exercise training is a key component of respiratory rehabilitation. Exercise monitoring and assessment have shown to be beneficial and have significant improvement in the outcome of exercise training. In this paper, we propose a non-invasive exercise monitoring and assessment system (EMAS) to home-based respiratory rehabilitation exercises. EMAS exploits multi-band microwave sensing technique to monitor in real-time the patient's rehabilitation exercise training, and synchronously provide an online visual feedback for helping the patient better training. Then EMAS extracts exercise information (duration, intensity and breathing changes) from all exercise data to assess the quality of rehabilitation exercise. We implement EMAS on the designed compact and portable prototypes and deploy it to monitor 4 subjects, resulting in one week of exercise data in total. System evaluations demonstrate that EMAS can accurately monitor rehabilitation exercises of each subject and obtain the exercise duration, exercise intensity and breathing changes during rehabilitation exercises to achieve exercise assessment.

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Continues online exercise monitoring and assessment system with visual guidance feedback for stroke rehabilitation

Cited by 13 publications

References 56 publications

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

Exercise Monitoring and Assessment System for Home-Based Respiratory Rehabilitation

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