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Cunha B, Ferreira R, Sousa ASP. Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence: An Overview. SENSORS (BASEL, SWITZERLAND) 2023; 23:7100. [PMID: 37631637 PMCID: PMC10459225 DOI: 10.3390/s23167100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.
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Affiliation(s)
- Bruno Cunha
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, Department of Physiotherapy, School of Health-Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal;
| | - Ricardo Ferreira
- Institute for Systems and Computer Engineering, Technology and Science—Telecommunications and Multimedia Centre, FEUP, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal;
| | - Andreia S. P. Sousa
- Center for Rehabilitation Research—Human Movement System (Re)habilitation Area, Department of Physiotherapy, School of Health-Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal;
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Arntz A, Weber F, Handgraaf M, Lällä K, Korniloff K, Murtonen KP, Chichaeva J, Kidritsch A, Heller M, Sakellari E, Athanasopoulou C, Lagiou A, Tzonichaki I, Salinas-Bueno I, Martínez-Bueso P, Velasco-Roldán O, Schulz RJ, Grüneberg C. Technologies in Home-Based Digital Rehabilitation: Scoping Review. JMIR Rehabil Assist Technol 2023; 10:e43615. [PMID: 37253381 PMCID: PMC10415951 DOI: 10.2196/43615] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/10/2023] [Accepted: 05/25/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Due to growing pressure on the health care system, a shift in rehabilitation to home settings is essential. However, efficient support for home-based rehabilitation is lacking. The COVID-19 pandemic has further exacerbated these challenges and has affected individuals and health care professionals during rehabilitation. Digital rehabilitation (DR) could support home-based rehabilitation. To develop and implement DR solutions that meet clients' needs and ease the growing pressure on the health care system, it is necessary to provide an overview of existing, relevant, and future solutions shaping the constantly evolving market of technologies for home-based DR. OBJECTIVE In this scoping review, we aimed to identify digital technologies for home-based DR, predict new or emerging DR trends, and report on the influences of the COVID-19 pandemic on DR. METHODS The scoping review followed the framework of Arksey and O'Malley, with improvements made by Levac et al. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO, and the Cochrane Library. The search spanned January 2015 to January 2022. A bibliometric analysis was performed to provide an overview of the included references, and a co-occurrence analysis identified the technologies for home-based DR. A full-text analysis of all included reviews filtered the trends for home-based DR. A gray literature search supplemented the results of the review analysis and revealed the influences of the COVID-19 pandemic on the development of DR. RESULTS A total of 2437 records were included in the bibliometric analysis and 95 in the full-text analysis, and 40 records were included as a result of the gray literature search. Sensors, robotic devices, gamification, virtual and augmented reality, and digital and mobile apps are already used in home-based DR; however, artificial intelligence and machine learning, exoskeletons, and digital and mobile apps represent new and emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches but has not led to the development of new technologies. CONCLUSIONS Multiple tools are available and implemented for home-based DR; however, some technologies face limitations in the application of home-based rehabilitation. However, artificial intelligence and machine learning could be instrumental in redesigning rehabilitation and addressing future challenges of the health care system, and the rehabilitation sector in particular. The results show the need for feasible and effective approaches to implement DR that meet clients' needs and adhere to framework conditions, regardless of exceptional situations such as the COVID-19 pandemic.
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Affiliation(s)
- Angela Arntz
- Division of Physiotherapy, Department of Applied Health Sciences, University of Applied Health Sciences Bochum, Bochum, Germany
- Faculty of Human Sciences, University of Cologne, Cologne, Germany
| | - Franziska Weber
- Division of Physiotherapy, Department of Applied Health Sciences, University of Applied Health Sciences Bochum, Bochum, Germany
- Department of Rehabilitation, Physiotherapy Science & Sports, University Medical Center Utrecht, Utrecht, Netherlands
| | - Marietta Handgraaf
- Division of Physiotherapy, Department of Applied Health Sciences, University of Applied Health Sciences Bochum, Bochum, Germany
| | - Kaisa Lällä
- Institute of Rehabilitation, Jamk University of Applied Sciences, Jyväskylä, Finland
| | - Katariina Korniloff
- Institute of Rehabilitation, Jamk University of Applied Sciences, Jyväskylä, Finland
| | - Kari-Pekka Murtonen
- Institute of Rehabilitation, Jamk University of Applied Sciences, Jyväskylä, Finland
| | - Julija Chichaeva
- Institute of Rehabilitation, Jamk University of Applied Sciences, Jyväskylä, Finland
| | - Anita Kidritsch
- Institute of Health Sciences, St. Pölten University of Applied Sciences, St. Pölten, Austria
| | - Mario Heller
- Department of Media & Digital Technologies, St. Pölten University of Applied Sciences, St. Pölten, Austria
| | - Evanthia Sakellari
- Department of Public and Community Health, Laboratory of Hygiene and Epidemiology, University of West Attica, Athens, Greece
| | | | - Areti Lagiou
- Department of Public and Community Health, Laboratory of Hygiene and Epidemiology, University of West Attica, Athens, Greece
| | - Ioanna Tzonichaki
- Department of Occupational Therapy, University of West Attica, Athens, Greece
| | - Iosune Salinas-Bueno
- Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
- Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
| | - Pau Martínez-Bueso
- Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
- Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
| | - Olga Velasco-Roldán
- Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
- Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
| | | | - Christian Grüneberg
- Division of Physiotherapy, Department of Applied Health Sciences, University of Applied Health Sciences Bochum, Bochum, Germany
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The telehealth program of occupational therapy among older people: an up-to-date scoping review. Aging Clin Exp Res 2023; 35:23-40. [PMID: 36344805 PMCID: PMC9640899 DOI: 10.1007/s40520-022-02291-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND The average life expectancy of older people is increasing, and most seniors desire to age at home and are capable of living independently. Occupational therapy (OT) is client-centered and uses patients' meaningful activities, or occupations, as treatment methods, thus playing an important role in later adulthood. Telemedicine removes the constraints of time and space, and the combination of OT and telemedicine can greatly improve medical efficiency and clinical effectiveness. AIMS The purpose of this scoping review was to examine the scope and effectiveness of telehealth OT for older people. METHODS This scoping review was conducted following the methodological framework proposed by Arksey and O'Malley. We searched the literature in five databases following the PICOS (Population, Intervention, Comparison, Outcome, Study design) guideline, from inception to April 2022. Two trained reviewers independently retrieved, screened, and extracted data, and used a descriptive synthesizing approach to summarize the results. RESULTS The initial search yielded 1249 studies from databases and manual searches, of which 20 were eligible and were included in the final review. A thematic analysis revealed five main themes related to telehealth OT: occupational assessment, occupational intervention, rehabilitation counseling, caregiver support, and activity monitoring. CONCLUSIONS Telehealth OT has been used widely for older people, focusing primarily on occupational assessment and intervention provided conveniently for occupational therapists and older clients. In addition, telehealth OT can monitor patients' activities and provide rehabilitation counseling and health education for the elderly and their caregivers, thus improving the security of their home life and the efficacy of OT. During the COVID-19 pandemic, telehealth will be an effective alternative to face-to-face modalities.
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Environmental Sound Classification Based on Transfer-Learning Techniques with Multiple Optimizers. ELECTRONICS 2022. [DOI: 10.3390/electronics11152279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The last decade has seen increased interest in environmental sound classification (ESC) due to the increased complexity and rich information of ambient sounds. The state-of-the-art methods for ESC are based on transfer learning paradigms that often utilize learned representations from common image-classification problems. This paper aims to determine the effectiveness of employing pre-trained convolutional neural networks (CNNs) for audio categorization and the feasibility of retraining. This study investigated various hyper-parameters and optimizers, such as optimal learning rate, epochs, and Adam, Adamax, and RMSprop optimizers for several pre-trained models, such as Inception, and VGG, ResNet, etc. Firstly, the raw sound signals were transferred into an image format (log-Mel spectrogram). Then, the selected pre-trained models were applied to the obtained spectrogram data. In addition, the effect of essential retraining factors on classification accuracy and processing time was investigated during CNN training. Various optimizers (such as Adam, Adamax, and RMSprop) and hyperparameters were utilized for evaluating the proposed method on the publicly accessible sound dataset UrbanSound8K. The proposed method achieves 97.25% and 95.5% accuracy on the provided dataset using the pre-trained DenseNet201 and the ResNet50V2 CNN models, respectively.
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Ding K, Zhang B, Ling Z, Chen J, Guo L, Xiong D, Wang J. Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback. SENSORS 2022; 22:s22093368. [PMID: 35591058 PMCID: PMC9101599 DOI: 10.3390/s22093368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/25/2022] [Accepted: 04/26/2022] [Indexed: 02/01/2023]
Abstract
Motor function evaluation is a significant part of post-stroke rehabilitation protocols, and the evaluation of wrist motor function helps provide patients with individualized rehabilitation training programs. However, traditional assessment is coarsely graded, lacks quantitative analysis, and relies heavily on clinical experience. In order to objectively quantify wrist motor dysfunction in stroke patients, a novel quantitative evaluation system based on force feedback and machine learning algorithm was proposed. Sensors embedded in the force-feedback robot record the kinematic and movement data of the subject, and the rehabilitation doctor used an evaluation scale to score the wrist function of the subject. The quantitative evaluation models of wrist motion function based on random forest (RF), support vector machine regression (SVR), k-nearest neighbor (KNN), and back propagation neural network (BPNN) were established, respectively. To verify the effectiveness of the proposed quantitative evaluation system, 25 stroke patients and 10 healthy volunteers were recruited in this study. Experimental results show that the evaluation accuracy of the four models is all above 88%. The accuracy of BPNN model is 94.26%, and the Pearson correlation coefficient between model prediction and clinician scores is 0.964, indicating that the BPNN model can accurately evaluate the wrist motor function for stroke patients. In addition, there was a significant correlation between the prediction score of the quantitative assessment system and the physician scale score (p < 0.05). The proposed system enables quantitative and refined assessment of wrist motor function in stroke patients and has the feasibility of helping rehabilitation physicians in evaluating patients’ motor function clinically.
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Affiliation(s)
- Kangjia Ding
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou 215163, China; (K.D.); (B.Z.); (Z.L.); (J.C.); (L.G.); (D.X.)
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Bochao Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou 215163, China; (K.D.); (B.Z.); (Z.L.); (J.C.); (L.G.); (D.X.)
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Zongquan Ling
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou 215163, China; (K.D.); (B.Z.); (Z.L.); (J.C.); (L.G.); (D.X.)
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jing Chen
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou 215163, China; (K.D.); (B.Z.); (Z.L.); (J.C.); (L.G.); (D.X.)
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Liquan Guo
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou 215163, China; (K.D.); (B.Z.); (Z.L.); (J.C.); (L.G.); (D.X.)
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Daxi Xiong
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou 215163, China; (K.D.); (B.Z.); (Z.L.); (J.C.); (L.G.); (D.X.)
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jiping Wang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou 215163, China; (K.D.); (B.Z.); (Z.L.); (J.C.); (L.G.); (D.X.)
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Correspondence: ; Tel.: +86-177-9859-8015
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An Estimation of the Discharge Exponent of a Drip Irrigation Emitter by Response Surface Methodology and Machine Learning. WATER 2022. [DOI: 10.3390/w14071034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
The discharge exponent is a general index used to evaluate the hydraulic performance of emitters, which is affected by emitters’ structural parameters. Accurately estimating the effect of change in structural parameters on the discharge exponent is critical for the design and optimization of emitters. In this research, the response surface methodology (RSM) and two machine learning models, the artificial neural network (ANN) and support vector regression (SVR), are used to predict the discharge exponent of tooth-shaped labyrinth channel emitters. The input parameters consist of the number of channel units (N), channel depth (D), tooth angle (α), tooth height (H) and channel width (W). The applied models are assessed through the coefficient of determination (R2), root-mean-square error (RMSE) and mean absolute error (MAE). The analysis of variance shows that tooth height had the greatest effect on the discharge exponent. Statistical criteria indicate that among the three models, the SVR model has the highest prediction accuracy and the best robustness with an average R2 of 0.9696, an average RMSE of 0.0037 and an average MAE of 0.0031. The SVR model can quickly and accurately simulate the discharge exponent of emitters, which is conducive to the rapid design of the emitter.
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AIM in Rehabilitation. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Advanced Applications of Industrial Robotics: New Trends and Possibilities. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app12010135] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This review is dedicated to the advanced applications of robotic technologies in the industrial field. Robotic solutions in areas with non-intensive applications are presented, and their implementations are analysed. We also provide an overview of survey publications and technical reports, classified by application criteria, and the development of the structure of existing solutions, and identify recent research gaps. The analysis results reveal the background to the existing obstacles and problems. These issues relate to the areas of psychology, human nature, special artificial intelligence (AI) implementation, and the robot-oriented object design paradigm. Analysis of robot applications shows that the existing emerging applications in robotics face technical and psychological obstacles. The results of this review revealed four directions of required advancement in robotics: development of intelligent companions; improved implementation of AI-based solutions; robot-oriented design of objects; and psychological solutions for robot–human collaboration.
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Atashzar SF, Carriere J, Tavakoli M. Review: How Can Intelligent Robots and Smart Mechatronic Modules Facilitate Remote Assessment, Assistance, and Rehabilitation for Isolated Adults With Neuro-Musculoskeletal Conditions? Front Robot AI 2021; 8:610529. [PMID: 33912593 PMCID: PMC8072151 DOI: 10.3389/frobt.2021.610529] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 02/08/2021] [Indexed: 12/12/2022] Open
Abstract
Worldwide, at the time this article was written, there are over 127 million cases of patients with a confirmed link to COVID-19 and about 2.78 million deaths reported. With limited access to vaccine or strong antiviral treatment for the novel coronavirus, actions in terms of prevention and containment of the virus transmission rely mostly on social distancing among susceptible and high-risk populations. Aside from the direct challenges posed by the novel coronavirus pandemic, there are serious and growing secondary consequences caused by the physical distancing and isolation guidelines, among vulnerable populations. Moreover, the healthcare system's resources and capacity have been focused on addressing the COVID-19 pandemic, causing less urgent care, such as physical neurorehabilitation and assessment, to be paused, canceled, or delayed. Overall, this has left elderly adults, in particular those with neuromusculoskeletal (NMSK) conditions, without the required service support. However, in many cases, such as stroke, the available time window of recovery through rehabilitation is limited since neural plasticity decays quickly with time. Given that future waves of the outbreak are expected in the coming months worldwide, it is important to discuss the possibility of using available technologies to address this issue, as societies have a duty to protect the most vulnerable populations. In this perspective review article, we argue that intelligent robotics and wearable technologies can help with remote delivery of assessment, assistance, and rehabilitation services while physical distancing and isolation measures are in place to curtail the spread of the virus. By supporting patients and medical professionals during this pandemic, robots, and smart digital mechatronic systems can reduce the non-COVID-19 burden on healthcare systems. Digital health and cloud telehealth solutions that can complement remote delivery of assessment and physical rehabilitation services will be the subject of discussion in this article due to their potential in enabling more effective and safer NMSDK rehabilitation, assistance, and assessment service delivery. This article will hopefully lead to an interdisciplinary dialogue between the medical and engineering sectors, stake holders, and policy makers for a better delivery of care for those with NMSK conditions during a global health crisis including future pandemics.
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Affiliation(s)
- S. Farokh Atashzar
- Department of Electrical and Computer Engineering, Department of Mechanical and Aerospace Engineering, New York University, New York, NY, United States
| | - Jay Carriere
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Mahdi Tavakoli
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
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Carriere J, Shafi H, Brehon K, Pohar Manhas K, Churchill K, Ho C, Tavakoli M. Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic. Front Artif Intell 2021; 4:613637. [PMID: 33733232 PMCID: PMC7907599 DOI: 10.3389/frai.2021.613637] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/08/2021] [Indexed: 01/16/2023] Open
Abstract
The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.
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Affiliation(s)
- Jay Carriere
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Hareem Shafi
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Katelyn Brehon
- School of Public Health, University of Alberta, Edmonton, AB, Canada
| | - Kiran Pohar Manhas
- Neurosciences, Rehabilitation, and Vision Strategic Clinical Network, Alberta Health Services, Calgary, AB, Canada
| | - Katie Churchill
- Department of Occupational Therapy, University of Alberta, Edmonton, AB, Canada.,Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Chester Ho
- Neurosciences, Rehabilitation, and Vision Strategic Clinical Network, Alberta Health Services, Calgary, AB, Canada.,Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Mahdi Tavakoli
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
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11
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AIM in Rehabilitation. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_177-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Six Dijkstra MWMC, Siebrand E, Dorrestijn S, Salomons EL, Reneman MF, Oosterveld FGJ, Soer R, Gross DP, Bieleman HJ. Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health: An Example Involving Periodic Workers' Health Assessments. JOURNAL OF OCCUPATIONAL REHABILITATION 2020; 30:343-353. [PMID: 32500471 PMCID: PMC7406529 DOI: 10.1007/s10926-020-09895-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Purpose Computer algorithms and Machine Learning (ML) will be integrated into clinical decision support within occupational health care. This will change the interaction between health care professionals and their clients, with unknown consequences. The aim of this study was to explore ethical considerations and potential consequences of using ML based decision support tools (DSTs) in the context of occupational health. Methods We conducted an ethical deliberation. This was supported by a narrative literature review of publications about ML and DSTs in occupational health and by an assessment of the potential impact of ML-DSTs according to frameworks from medical ethics and philosophy of technology. We introduce a hypothetical clinical scenario from a workers' health assessment to reflect on biomedical ethical principles: respect for autonomy, beneficence, non-maleficence and justice. Results Respect for autonomy is affected by uncertainty about what future consequences the worker is consenting to as a result of the fluctuating nature of ML-DSTs and validity evidence used to inform the worker. A beneficent advisory process is influenced because the three elements of evidence based practice are affected through use of a ML-DST. The principle of non-maleficence is challenged by the balance between group-level benefits and individual harm, the vulnerability of the worker in the occupational context, and the possibility of function creep. Justice might be empowered when the ML-DST is valid, but profiling and discrimination are potential risks. Conclusions Implications of ethical considerations have been described for the socially responsible design of ML-DSTs. Three recommendations were provided to minimize undesirable adverse effects of the development and implementation of ML-DSTs.
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Affiliation(s)
- Marianne W M C Six Dijkstra
- School of Health, Saxion University of Applied Sciences/AGZ, M.H. Tromplaan 28, 7500 KB, Enschede, The Netherlands.
- Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- University of Groningen, Groningen, The Netherlands.
| | - Egbert Siebrand
- Research Group Ethics & Technology, Saxion University of Applied Sciences, Enschede, The Netherlands
| | - Steven Dorrestijn
- Research Group Ethics & Technology, Saxion University of Applied Sciences, Enschede, The Netherlands
| | - Etto L Salomons
- School of Ambient Intelligence, Saxion University of Applied Sciences, Enschede, The Netherlands
| | - Michiel F Reneman
- Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Frits G J Oosterveld
- School of Health, Saxion University of Applied Sciences/AGZ, M.H. Tromplaan 28, 7500 KB, Enschede, The Netherlands
| | - Remko Soer
- School of Health, Saxion University of Applied Sciences/AGZ, M.H. Tromplaan 28, 7500 KB, Enschede, The Netherlands
- University Medical Center Groningen, Pain Centre, University of Groningen, Groningen, The Netherlands
| | - Douglas P Gross
- Department of Physical Therapy, University of Alberta, Edmonton, Canada
| | - Hendrik J Bieleman
- School of Health, Saxion University of Applied Sciences/AGZ, M.H. Tromplaan 28, 7500 KB, Enschede, The Netherlands
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Gross DP, Steenstra IA, Harrell FE, Bellinger C, Zaïane O. Machine Learning for Work Disability Prevention: Introduction to the Special Series. JOURNAL OF OCCUPATIONAL REHABILITATION 2020; 30:303-307. [PMID: 32623556 DOI: 10.1007/s10926-020-09910-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Rapid development in computer technology has led to sophisticated methods of analyzing large datasets with the aim of improving human decision making. Artificial Intelligence and Machine Learning (ML) approaches hold tremendous potential for solving complex real-world problems such as those faced by stakeholders attempting to prevent work disability. These techniques are especially appealing in work disability contexts that collect large amounts of data such as workers' compensation settings, insurance companies, large corporations, and health care organizations, among others. However, the approaches require thorough evaluation to determine if they add value to traditional statistical approaches. In this special series of articles, we examine the role and value of ML in the field of work disability prevention and occupational rehabilitation.
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Affiliation(s)
- Douglas P Gross
- Department of Physical Therapy, University of Alberta, 2-50 Corbett Hall, Edmonton, AB, T6G 2G4, Canada.
| | | | - Frank E Harrell
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | - Osmar Zaïane
- Department of Computing Science, University of Alberta, Edmonton, Canada
- Alberta Machine Intelligence Institute, Edmonton, Canada
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