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Computer-Assisted Technologies in Sports Medicine and Rehabilitation

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 9170

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Special Issue Editors

Dr. Chung Hyun Goh

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Guest Editor

Department of Mechanical Engineering, The University of Texas at Tyler, Tyler, TX 75799, USA
Interests: robotics and automation; virtual design and manufacturing innovation; rehabilitation-focused research; medical device design and mechanical design and optimization; biomaterials and biomechanics; finite element analysis; crystal plasticity finite element method
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Prof. Dr. Mukul Shirvaikar

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Department of Electrical Engineering, The University of Texas at Tyler, Tyler, TX 75799, USA
Interests: real time systems; image and signal processing; robotics and computer vision; system-on-a-chip design; pattern recognition; neural networks; medical imaging; engineering education
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Special Issue Information

Dear Colleagues,

Computer-assisted technologies to aid in the design, analysis, and manufacture of products in Sports Medicine and Rehabilitation are becoming common as the supporting areas of computers, embedded systems, communication systems, robotics and deep learning become more mature.

This Special Issue aims to publish a collection of research contributions illustrating the recent achievements in all aspects of the development, study and understanding of computer-assisted technologies in sports medicine and rehabilitation.

Examples of areas that are relevant for publication include, but are not limited to, the following: computer-assisted surgery/therapy; robot-assisted rehabilitation technologies; wearable sensors in sports medicine and rehabilitation; gait motion modelling and analysis; virtual reality/augmented reality in sports medicine and rehabilitation; body area networks (BAN); deep learning for sports data analysis; etc. We hope to establish a collection of papers that will be of interest to scholars in the field. Contributions in the form of full papers, reviews, and communications about the related topics are very welcome.

Dr. Chung Hyun Goh
Prof. Dr. Mukul Shirvaikar
Guest Editors

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Keywords

computer-assisted surgery/therapy
robot-assisted rehabilitation technologies
wearable sensors in sports medicine and rehabilitation
gait motion modelling and analysis
virtual reality/augmented reality in sports medicine and rehabilitation

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Published Papers (5 papers)

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Research

13 pages, 4328 KiB

Open AccessArticle

Redesign of Leg Assembly and Implementation of Reinforcement Learning for a Multi-Purpose Rehabilitation Robotic Device (RoboREHAB)

by Jacob Anthony, Chung-Hyun Goh, Alireza Yazdanshenas and Yong Tai Wang

Appl. Sci. 2024, 14(2), 516; https://doi.org/10.3390/app14020516 - 6 Jan 2024

Viewed by 1182

Abstract

Patients who are suffering from neuromuscular disorders or injuries that impair motor control need to undergo rehabilitation to regain mobility. Gait training is commonly prescribed to patients to regain muscle memory. Automated-walking training devices were created to aid this process; while these devices establish accurate ankle-path trajectories, the knee and hip movements are inaccurate. In this work, a redesign of the leg assembly in a multi-purpose rehabilitation robotic device (RoboREHAB) was explored to improve hip- and knee-movement accuracy by adding an extra link and rollers to the assembly. Motion analysis was employed to test feasibility, reinforcement learning was utilized to train the new leg assembly to walk, and the joint motions achieved with the redesign were compared to those achieved by motion-capture (mocap) data. As a key result, the motion analysis showed an improvement in the knee- and hip-path trajectories due to the added roller/joint segment. The redesigned leg assembly, under the reinforcement-learning policy, showed a 5% deviation from the motion-capture joint trajectories with a maximum deviation of 51.177 mm but maintained a similar profile to the mocap trajectory data. This is an improvement over the original two-segment design, which achieved a maximum deviation of 72.084 mm. These results in the knee- and hip-joint movements more closely reflect the mocap and motion-analysis results, validating the redesign and opening it up to further experimentation and technical improvement. Full article

(This article belongs to the Special Issue Computer-Assisted Technologies in Sports Medicine and Rehabilitation)

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13 pages, 2012 KiB

Open AccessArticle

by Woohyoung Jeon, Ahmed Ramadan, Jill Whitall, Nesreen Alissa and Kelly Westlake

Appl. Sci. 2023, 13(15), 9035; https://doi.org/10.3390/app13159035 - 7 Aug 2023

Cited by 3 | Viewed by 1558

Abstract

Falls in older adults are leading causes of fatal and non-fatal injuries, negatively impacting quality of life among those in this demographic. Most elderly falls occur due to unrecoverable limb collapse during balance control in the single-limb support (SLS) phase. To understand why older adults are more susceptible to falls than younger adults, we investigated age-related differences in lower limb kinematics, kinetics, and muscle synergy patterns during SLS, as well as their relationship to postural control strategies. Thirteen older and thirteen younger healthy adults were compared during the SLS phase of balance recovery following an unexpected surface drop perturbation. Compared to younger adults, older adults demonstrated (1) greater trunk flexion, (2) increased hip extension torque and reduced hip abduction torque of the perturbed leg, and (3) higher postural sway. Trunk flexion was correlated with a delayed latency to the start of lateral-to-medial displacement of center of mass from the perturbation onset. The group-specific muscle synergy revealed that older adults exhibited prominent activation of the hip extensors, while younger adults showed prominent activation of the hip abductors. These findings provide insights into targeted balance rehabilitation and indicate ways to improve postural stability and reduce falls in older adults. Full article

(This article belongs to the Special Issue Computer-Assisted Technologies in Sports Medicine and Rehabilitation)

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13 pages, 2081 KiB

Open AccessArticle

Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients

by Joachim Rosiński, Piotr Kotlarz, Izabela Rojek and Dariusz Mikołajewski

Appl. Sci. 2023, 13(9), 5444; https://doi.org/10.3390/app13095444 - 27 Apr 2023

Cited by 1 | Viewed by 1459

Abstract

It is increasingly important to provide post-stroke patients with rapid access to patient-tailored assistive technologies to increase independence, mobility, and participation. Automating the selection of assistive devices based on artificial intelligence could speed up the process and improve accuracy. It would also relieve the burden on diagnosticians and therapists and speed up the introduction of new ranges by automating databases. This article compares selected machine learning classification methods in the area of post-stroke rehabilitation device selection. The article covers the specifics of the selection, the choice of classification methods, and the identification of the best one, as well as the experimental part, the description of the results, the comparison process, and directions for further research. The novelty lies both in the topic, as the choice of classification method has an impact on the accuracy of classification in the selection of medical materials, and in the manner of the comprehensive approach. The possible contribution is of great scientific and clinical relevance, but above all, it has economic and social importance, enabling post-stroke individuals to return more quickly to the community, learning, and work, and relieving the burden on the health care system. Full article

(This article belongs to the Special Issue Computer-Assisted Technologies in Sports Medicine and Rehabilitation)

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13 pages, 1543 KiB

Open AccessArticle

An Exploration of Robot-Mediated Tai Chi Exercise for Older Adults

by Zhi Zheng, Hyunkyoung Oh, Mayesha Mim, Wonchan Choi and Yura Lee

Appl. Sci. 2023, 13(9), 5306; https://doi.org/10.3390/app13095306 - 24 Apr 2023

Viewed by 2207

Abstract

In this fast-aging society, many older adults fail to meet the required level of exercise due to trainer shortages. Therefore, we developed a robot tutor to investigate the feasibility of robot-mediated exercise for older adults. Twenty older adults participated in an experimental study. A pre-exercise survey was used to assess their background. Each participant experienced a 30-min robot-led Tai Chi exercise followed by a post-exercise survey to evaluate the easiness of following the robot and expectations for future robot design. Participants’ Tai Chi performances were evaluated in terms of completion and accuracy. Associations between the surveys and the performance were also analyzed. All participants completed the study. Fifteen out of the twenty subjects had at least one chronic condition, and most practiced Tai Chi before the study but had never interacted with a robot. On average, the participants scored 93.09 and 85.21 out of 100 for movement completion and accuracy, respectively. Their initial movement accuracy was correlated with their attitude towards exercise. Most subjects reported that they could follow the robot’s movements and speeches well and were interested in using a robot tutor in the community. The study demonstrated the initial feasibility of robot-led Tai Chi exercise for older adults. Full article

(This article belongs to the Special Issue Computer-Assisted Technologies in Sports Medicine and Rehabilitation)

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23 pages, 5481 KiB

Open AccessArticle

Analysis of Phonetic Segments of Oesophageal Speech in People Following Total Laryngectomy

by Krzysztof Tyburek, Dariusz Mikołajewski and Izabela Rojek

Appl. Sci. 2023, 13(8), 4995; https://doi.org/10.3390/app13084995 - 16 Apr 2023

Cited by 1 | Viewed by 1544

Abstract

This paper presents an approach to extraction techniques for speaker recognition following total laryngectomy surgery. The aim of the research was to develop a pattern of physical features describing the oesophageal speech in people after experiencing laryngeal cancer. Research results may support the speech rehabilitation of laryngectomised patients by improving the quality of oesophageal speech. The main goal of the research was to isolate the physical features of oesophageal speech and to compare their values with the descriptors of physiological speech. Words (in Polish) used during speech rehabilitation were analyzed. Each of these words was divided into phonetic segments from which the physical features of speech were extracted. The values of the acquired speech descriptors were then used to create a vector of the physical features of oesophageal speech. A set of these features will determine a model that should allow us to recognize whether the speech-rehabilitation process is proceeding correctly and also provide a selection of bespoke procedures that we could introduce to each patient. This research is a continuation of the analysis of oesophageal speech published previously. This time, the effectiveness of parameterization was tested using methodologies for analyzing the phonetic segments of each word. Full article

(This article belongs to the Special Issue Computer-Assisted Technologies in Sports Medicine and Rehabilitation)

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