1
|
Ranzani R, Razzoli M, Sanson P, Song J, Galati S, Ferrarese C, Lambercy O, Kaelin-Lang A, Gassert R. Feasibility of Adjunct Therapy with a Robotic Hand Orthosis after Botulinum Toxin Injections in Persons with Spasticity: A Pilot Study. Toxins (Basel) 2024; 16:346. [PMID: 39195756 PMCID: PMC11360205 DOI: 10.3390/toxins16080346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 07/28/2024] [Accepted: 07/30/2024] [Indexed: 08/29/2024] Open
Abstract
Upper-limb spasticity, frequent after central nervous system lesions, is typically treated with botulinum neurotoxin type A (BoNT-A) injections to reduce muscle tone and increase range of motion. However, performing adjunct physical therapy post-BoNT-A can be challenging due to residual weakness or spasticity. This study evaluates the feasibility of hand therapy using a robotic hand orthosis (RELab tenoexo) with a mobile phone application as an adjunct to BoNT-A injections. Five chronic spastic patients participated in a two-session pilot study. Functional (Box and Block Test (BBT), Action Research Arm Test (ARAT)), and muscle tone (Modified Ashworth Scale (MAS)) assessments were conducted to assess functional abilities and impairment, along with usability evaluations. In the first session, subjects received BoNT-A injections, and then they performed a simulated unsupervised therapy session with the RELab tenoexo in a second session a month later. Results showed that BoNT-A reduced muscle tone (from 12.2 to 7.4 MAS points). The addition of RELab tenoexo therapy was safe, led to functional improvements in four subjects (two-cube increase in BBT as well as 2.8 points in grasp and 1.3 points in grip on ARAT). Usability results indicate that, with minor improvements, adjunct RELab tenoexo therapy could enhance therapy doses and, potentially, long-term outcomes.
Collapse
Affiliation(s)
- Raffaele Ranzani
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
- School of Medicine and Surgery and Milan Center for Neuroscience (NeuroMi), University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy;
- Cereneo, Center for Neurology and Rehabilitation, Seestrasse 18, 6354 Vitznau, Switzerland
| | - Margherita Razzoli
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
| | - Pierre Sanson
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
| | - Jaeyong Song
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
| | - Salvatore Galati
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6962 Lugano, Switzerland; (S.G.); (A.K.-L.)
- Neurology Department, Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
| | - Carlo Ferrarese
- School of Medicine and Surgery and Milan Center for Neuroscience (NeuroMi), University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy;
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
| | - Alain Kaelin-Lang
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6962 Lugano, Switzerland; (S.G.); (A.K.-L.)
- Neurology Department, Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, 6900 Lugano, Switzerland
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Gloriastrasse 37/39, 8092 Zurich, Switzerland; (M.R.); (P.S.); (J.S.); (O.L.); (R.G.)
| |
Collapse
|
2
|
Zhang X, Meesen R, Swinnen SP, Feys H, Woolley DG, Cheng HJ, Wenderoth N. Combining muscle-computer interface guided training with bihemispheric tDCS improves upper limb function in patients with chronic stroke. J Neurophysiol 2024; 131:1286-1298. [PMID: 38716555 DOI: 10.1152/jn.00316.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 02/22/2024] [Accepted: 04/24/2024] [Indexed: 06/21/2024] Open
Abstract
Transcranial direct current stimulation (tDCS) may facilitate neuroplasticity but with a limited effect when administered while patients with stroke are at rest. Muscle-computer interface (MCI) training is a promising approach for training patients with stroke even if they cannot produce overt movements. However, using tDCS to enhance MCI training has not been investigated. We combined bihemispheric tDCS with MCI training of the paretic wrist and examined the effect of this intervention in patients with chronic stroke. A crossover, double-blind, randomized trial was conducted. Twenty-six patients with chronic stroke performed MCI wrist training for three consecutive days at home while receiving either real tDCS or sham tDCS in counterbalanced order and separated by at least 8 mo. The primary outcome measure was the Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE) that was measured 1 wk before training, on the first training day, on the last training day, and 1 wk after training. There was neither a significant difference in the baseline FMA-UE score between groups nor between intervention periods. Patients improved 3.9 ± 0.6 points in FMA-UE score when receiving real tDCS, and 1.0 ± 0.7 points when receiving sham tDCS (P = 0.003). In addition, patients also showed continuous improvement in their motor control of the MCI tasks over the training days. Our study showed that the training paradigm could lead to functional improvement in patients with chronic stroke. We argue that appropriate MCI training in combination with bihemispheric tDCS could be a useful adjuvant for neurorehabilitation in patients with stroke.NEW & NOTEWORTHY Bihemispheric tDCS combined with a novel MCI training for motor control of wrist extensor can improve upper limb function especially a training-specific effect on the wrist movement in patients with chronic stroke. The training regimen can be personalized with adjustments made daily to accommodate the functional change throughout the intervention. This demonstrates that bihemispheric tDCS with MCI training could complement conventional poststroke neurorehabilitation.
Collapse
Affiliation(s)
- Xue Zhang
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Raf Meesen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Faculty of Rehabilitation Sciences, REVAL Rehabilitation Research Center, Hasselt University, Diepenbeek, Belgium
| | - Stephan P Swinnen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Hilde Feys
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Daniel G Woolley
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Hsiao-Ju Cheng
- Singapore-ETH Centre, CREATE campus, Future Health Technologies Programme, Singapore, Singapore
| | - Nicole Wenderoth
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Singapore-ETH Centre, CREATE campus, Future Health Technologies Programme, Singapore, Singapore
| |
Collapse
|
3
|
Zhang Y, Zhang X, Cheng C, Huang S, Hua Y, Hu J, Wang Y, Zhang W, Yang Y, Liu Y, Jia J, Gou P, Zhang P, Zhou F, Wei X, Bai Y. Mirror therapy combined with contralaterally controlled functional electrical stimulation for the upper limb motor function after stroke: a randomized controlled trial. Disabil Rehabil 2024; 46:2528-2534. [PMID: 37341447 DOI: 10.1080/09638288.2023.2225878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 06/10/2023] [Indexed: 06/22/2023]
Abstract
PURPOSE In this study, we investigated the effects of mirror therapy (MT) combined with contralaterally controlled functional electrical stimulation (CCFES) on upper limb motor function, activities of daily life, and corticospinal excitability in post-stroke patients. METHODS Sixty post-stroke patients were randomly divided into four groups: CCFES, MT, MT combined with CCFES, and control. All the patients underwent routine rehabilitation. Those in the MT, CCFES, MT combined with CCFES, and control groups received MT, CCFES, MT combined with CCFES, and routine rehabilitation alone, respectively. Upper limb motor function, activities of daily living, and corticospinal excitability were evaluated before and after a 3-week intervention period. RESULTS MT combined with CCFES demonstrated a significantly greater therapeutic effect on motor function of the paretic wrist than CCFES, MT, or routine rehabilitation alone. However, there was no significant difference in the overall motor function of the affected upper limb, activities of daily life, or corticospinal excitability between the MT combined with CCFES group and the other three groups. CONCLUSION MT combined with CCFES may be a potential adjuvant therapy to promote motor function in paretic wrist after stroke.
Collapse
Affiliation(s)
- Yuqian Zhang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xingnan Zhang
- Department of Rehabilitation Medicine, Baoji Central Hospital, Baoji, China
| | - Cancan Cheng
- Department of Rehabilitation Medicine, Baoji Central Hospital, Baoji, China
| | - Songhua Huang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yan Hua
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jian Hu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuyuan Wang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Weizhou Zhang
- Department of Rehabilitation Medicine, Baoji Central Hospital, Baoji, China
| | - Yi Yang
- Department of Rehabilitation Medicine, Baoji Central Hospital, Baoji, China
| | - Yafeng Liu
- Department of Rehabilitation Medicine, Baoji Central Hospital, Baoji, China
| | - Jian Jia
- Department of Rehabilitation Medicine, Baoji Central Hospital, Baoji, China
| | - Pingping Gou
- Department of Rehabilitation Medicine, Baoji Central Hospital, Baoji, China
| | - Pei Zhang
- Department of Rehabilitation Medicine, Baoji Central Hospital, Baoji, China
| | - Feng Zhou
- Department of Neurosurgery, Baoji Central Hospital, Baoji, China
| | - Xiaoli Wei
- Department of Rehabilitation Medicine, Baoji Central Hospital, Baoji, China
| | - Yulong Bai
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
4
|
Nguyen CM, Uy J, Serrada I, Hordacre B. Quantifying patient experiences with therapeutic neurorehabilitation technologies: a scoping review. Disabil Rehabil 2024; 46:1662-1672. [PMID: 37132669 DOI: 10.1080/09638288.2023.2201514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 04/06/2023] [Indexed: 05/04/2023]
Abstract
PURPOSE Neurorehabilitation technologies are a novel approach to providing rehabilitation for patients with neurological conditions. There is a need to explore patient experiences. This study aimed; 1) To identify available questionnaires that assess patients' experiences with neurorehabilitation technologies, and 2) where reported, to document the psychometric properties of the identified questionnaires. MATERIALS AND METHODS Four databases were searched (Medline, Embase, Emcare and PsycInfo). The inclusion criteria were all types of primary data collection that included neurological patients of all ages who had experienced therapy with neurorehabilitation technologies and completed questionnaires to assess these experiences. RESULTS Eighty-eight publications were included. Fifteen different questionnaires along with many self-developed scales were identified. These were categorised as; 1) self-developed tools, 2) specific questionnaire for a particular technology, and 3) generic questionnaires originally developed for a different purpose. The questionnaires were used to assess various technologies, including virtual reality, robotics, and gaming systems. Most studies did not report any psychometric properties. CONCLUSION Many tools have been used to evaluate patient experiences, but few were specifically developed for neurorehabilitation technologies and psychometric data was limited. A preliminary recommendation would be use of the User Satisfaction Evaluation Questionnaire to evaluate patient experience with virtual reality systems.
Collapse
Affiliation(s)
- Chi Mai Nguyen
- University of South Australia, Allied Health and Human Performance, Adelaide, Australia
| | - Jeric Uy
- University of South Australia, Allied Health and Human Performance, Adelaide, Australia
| | - Ines Serrada
- University of South Australia, Allied Health and Human Performance, Adelaide, Australia
| | - Brenton Hordacre
- University of South Australia, Innovation, Implementation and Clinical Translation (IIMPACT), Health Allied Health and Human Performance, Adelaide, Australia
| |
Collapse
|
5
|
Albanese GA, Bucchieri A, Podda J, Tacchino A, Buccelli S, De Momi E, Laffranchi M, Mannella K, Holmes MWR, Zenzeri J, De Michieli L, Brichetto G, Barresi G. Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments. Front Robot AI 2024; 11:1335147. [PMID: 38638271 PMCID: PMC11025362 DOI: 10.3389/frobt.2024.1335147] [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: 11/08/2023] [Accepted: 01/30/2024] [Indexed: 04/20/2024] Open
Abstract
The robotics discipline is exploring precise and versatile solutions for upper-limb rehabilitation in Multiple Sclerosis (MS). People with MS can greatly benefit from robotic systems to help combat the complexities of this disease, which can impair the ability to perform activities of daily living (ADLs). In order to present the potential and the limitations of smart mechatronic devices in the mentioned clinical domain, this review is structured to propose a concise SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of robotic rehabilitation in MS. Through the SWOT Analysis, a method mostly adopted in business management, this paper addresses both internal and external factors that can promote or hinder the adoption of upper-limb rehabilitation robots in MS. Subsequently, it discusses how the synergy with another category of interaction technologies - the systems underlying virtual and augmented environments - may empower Strengths, overcome Weaknesses, expand Opportunities, and handle Threats in rehabilitation robotics for MS. The impactful adaptability of these digital settings (extensively used in rehabilitation for MS, even to approach ADL-like tasks in safe simulated contexts) is the main reason for presenting this approach to face the critical issues of the aforementioned SWOT Analysis. This methodological proposal aims at paving the way for devising further synergistic strategies based on the integration of medical robotic devices with other promising technologies to help upper-limb functional recovery in MS.
Collapse
Affiliation(s)
| | - Anna Bucchieri
- Rehab Technologies Lab, Istituto Italiano di Tecnologia, Genoa, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Jessica Podda
- Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy
| | - Andrea Tacchino
- Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy
| | - Stefano Buccelli
- Rehab Technologies Lab, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Matteo Laffranchi
- Rehab Technologies Lab, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Kailynn Mannella
- Department of Kinesiology, Brock University, St. Catharines, ON, Canada
| | | | | | | | - Giampaolo Brichetto
- Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Genoa, Italy
- AISM Rehabilitation Center Liguria, Italian Multiple Sclerosis Society (AISM), Genoa, Italy
| | - Giacinto Barresi
- Rehab Technologies Lab, Istituto Italiano di Tecnologia, Genoa, Italy
| |
Collapse
|
6
|
Moulaei K, Moulaei R, Bahaadinbeigy K. The most used questionnaires for evaluating the usability of robots and smart wearables: A scoping review. Digit Health 2024; 10:20552076241237384. [PMID: 38601185 PMCID: PMC11005511 DOI: 10.1177/20552076241237384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 02/27/2024] [Indexed: 04/12/2024] Open
Abstract
Background As the field of robotics and smart wearables continues to advance rapidly, the evaluation of their usability becomes paramount. Researchers may encounter difficulty in finding a suitable questionnaire for evaluating the usability of robotics and smart wearables. Therefore, the aim of this study is to identify the most commonly utilized questionnaires for assessing the usability of robots and smart wearables. Methods A comprehensive search of databases, including PubMed, Web of Science, and Scopus, was conducted for this scoping review. Two authors performed the selection of articles and data extraction using a 10-field data extraction form. In cases of disagreements, a third author was consulted to reach a consensus. The inclusions were English-language original research articles that utilized validated questionnaires to assess the usability of healthcare robots and smart wearables. The exclusions comprised review articles, non-English publications, studies not focused on usability, those assessing clinical outcomes, articles lacking questionnaire details, and those using non-validated or researcher-made questionnaires. Descriptive statistics methods (frequency and percentage), were employed to analyze the data. Results A total of 314 articles were obtained, and after eliminating irrelevant and duplicate articles, a final selection of 50 articles was included in this review. A total of 17 questionnaires were identified to evaluate the usability of robots and smart wearables, with 10 questionnaires specifically for wearables and 7 questionnaires for robots. The System Usability Scale (50%) and Post-Study System Usability Questionnaire (19.44%) were the predominant questionnaires utilized to assess the usability of smart wearables. Moreover, the most commonly used questionnaires for evaluating the usability of robots were the System Usability Scale (56.66%), User Experience Questionnaire (16.66%), and Quebec User Evaluation of Satisfaction with Assistive Technology (10%). Conclusion Commonly employed questionnaires serve as valuable tools in assessing the usability of robots and smart wearables, aiding in the refinement and optimization of these technologies for enhanced user experiences. By incorporating user feedback and insights, designers can strive towards creating more intuitive and effective robotic and wearable solutions.
Collapse
Affiliation(s)
- Khadijeh Moulaei
- Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Reza Moulaei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| |
Collapse
|
7
|
Jamwal PK, Niyetkaliyev A, Hussain S, Sharma A, Van Vliet P. Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home. MethodsX 2023; 11:102312. [PMID: 37593414 PMCID: PMC10428111 DOI: 10.1016/j.mex.2023.102312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/01/2023] [Indexed: 08/19/2023] Open
Abstract
Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.8918 0 , 2.6753 0 and 8.0258 0 , respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.
Collapse
Affiliation(s)
- Prashant K. Jamwal
- Department of Electrical and Computer Engineering, Nazarbayev University, Astana, Kazakhstan
| | - Aibek Niyetkaliyev
- Department of Robotics Engineering, Nazarbayev University, Astana, Kazakhstan
| | - Shahid Hussain
- School of Information Technology and Systems, University of Canberra, Canberra, ACT, Australia
| | - Aditi Sharma
- Department of Electrical and Computer Engineering, Nazarbayev University, Astana, Kazakhstan
| | - Paulette Van Vliet
- Research and Innovation Division, The University of Newcastle, NSW, Australia
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Forbrigger S, DePaul VG, Davies TC, Morin E, Hashtrudi-Zaad K. Home-based upper limb stroke rehabilitation mechatronics: challenges and opportunities. Biomed Eng Online 2023; 22:67. [PMID: 37424017 DOI: 10.1186/s12938-023-01133-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 07/01/2023] [Indexed: 07/11/2023] Open
Abstract
Interest in home-based stroke rehabilitation mechatronics, which includes both robots and sensor mechanisms, has increased over the past 12 years. The COVID-19 pandemic has exacerbated the existing lack of access to rehabilitation for stroke survivors post-discharge. Home-based stroke rehabilitation devices could improve access to rehabilitation for stroke survivors, but the home environment presents unique challenges compared to clinics. The present study undertakes a scoping review of designs for at-home upper limb stroke rehabilitation mechatronic devices to identify important design principles and areas for improvement. Online databases were used to identify papers published 2010-2021 describing novel rehabilitation device designs, from which 59 publications were selected describing 38 unique designs. The devices were categorized and listed according to their target anatomy, possible therapy tasks, structure, and features. Twenty-two devices targeted proximal (shoulder and elbow) anatomy, 13 targeted distal (wrist and hand) anatomy, and three targeted the whole arm and hand. Devices with a greater number of actuators in the design were more expensive, with a small number of devices using a mix of actuated and unactuated degrees of freedom to target more complex anatomy while reducing the cost. Twenty-six of the device designs did not specify their target users' function or impairment, nor did they specify a target therapy activity, task, or exercise. Twenty-three of the devices were capable of reaching tasks, 6 of which included grasping capabilities. Compliant structures were the most common approach of including safety features in the design. Only three devices were designed to detect compensation, or undesirable posture, during therapy activities. Six of the 38 device designs mention consulting stakeholders during the design process, only two of which consulted patients specifically. Without stakeholder involvement, these designs risk being disconnected from user needs and rehabilitation best practices. Devices that combine actuated and unactuated degrees of freedom allow a greater variety and complexity of tasks while not significantly increasing their cost. Future home-based upper limb stroke rehabilitation mechatronic designs should provide information on patient posture during task execution, design with specific patient capabilities and needs in mind, and clearly link the features of the design to users' needs.
Collapse
Affiliation(s)
- Shane Forbrigger
- Department of Electrical and Computer Engineering, Queen's University, Kingston, Canada
| | - Vincent G DePaul
- School of Rehabilitation Therapy, Queen's University, Kingston, Canada
| | - T Claire Davies
- Department of Mechanical and Materials Engineering, Queen's University, Kingston, Canada
| | - Evelyn Morin
- Department of Electrical and Computer Engineering, Queen's University, Kingston, Canada
| | - Keyvan Hashtrudi-Zaad
- Department of Electrical and Computer Engineering, Queen's University, Kingston, Canada.
| |
Collapse
|
10
|
Bu D, Guo S, Guo J, Li H, Wang H. Low-Density sEMG-Based Pattern Recognition of Unrelated Movements Rejection for Wrist Joint Rehabilitation. MICROMACHINES 2023; 14:555. [PMID: 36984962 PMCID: PMC10056026 DOI: 10.3390/mi14030555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/16/2023] [Accepted: 02/25/2023] [Indexed: 06/18/2023]
Abstract
sEMG-based pattern recognition commonly assumes a limited number of target categories, and the classifiers often predict each target category depending on probability. In wrist rehabilitation training, the patients may make movements that do not belong to the target category unconsciously. However, most pattern recognition methods can only identify limited patterns and are prone to be disturbed by abnormal movement, especially for wrist joint movements. To address the above the problem, a sEMG-based rejection method for unrelated movements is proposed to identify wrist joint unrelated movements using center loss. In this paper, the sEMG signal collected by the Myo armband is used as the input of the sEMG control method. First, the sEMG signal is processed by sliding signal window and image coding. Then, the CNN with center loss and softmax loss is used to describe the spatial information from the sEMG image to extract discriminative features and target movement recognition. Finally, the deep spatial information is used to train the AE to reject unrelated movements based on the reconstruction loss. The results show that the proposed method can realize the target movements recognition and reject unrelated movements with an F-score of 93.4% and a rejection accuracy of 95% when the recall is 0.9, which reveals the effectiveness of the proposed method.
Collapse
Affiliation(s)
- Dongdong Bu
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Shuxiang Guo
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Jin Guo
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - He Li
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Hanze Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| |
Collapse
|
11
|
Park CB, Park HS. Portable 3D-printed hand orthosis with spatial stiffness distribution personalized for assisting grasping in daily living. Front Bioeng Biotechnol 2023; 11:895745. [PMID: 36815899 PMCID: PMC9932545 DOI: 10.3389/fbioe.2023.895745] [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: 03/14/2022] [Accepted: 01/20/2023] [Indexed: 02/05/2023] Open
Abstract
Stroke survivors having limited finger coordination require an active hand orthosis to assist them with grasping tasks for daily activities. The orthosis should be portable for constant use; however, portability imposes constraints on the number, size, and weight of the actuators, which increase the difficulty of the design process. Therefore, a tradeoff exists between portability and the assistive force. In this study, a personalized spatial stiffness distribution design is presented for a portable and strengthful hand orthosis. The spatial stiffness distribution of the orthosis was optimized based on measurements of individual hand parameters to satisfy the functional requirements of achieving sufficient grip aperture in the pre-grasping phase and minimal assistive force in the grasping phase. Ten stroke survivors were recruited to evaluate the system. Sufficient grip aperture and high grip strength-to-weight ratio were achieved by the orthosis via a single motor. Moreover, the orthosis significantly restored the range of motion and improved the performance of daily activities. The proposed spatial stiffness distribution can suggest a design solution to make strengthful hand orthoses with reduced weight.
Collapse
Affiliation(s)
| | - Hyung-Soon Park
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| |
Collapse
|
12
|
Su H, Lee KS, Kim Y, Park HS. A Soft, Wearable Skin-Brace for Assisting Forearm Pronation and Supination With a Low-Profile Design. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3211783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Huimin Su
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Kyoung-Soub Lee
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Yusung Kim
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Hyung-Soon Park
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| |
Collapse
|
13
|
The Effect of the Degree of Freedom and Weight of the Hand Exoskeleton on Joint Mobility Function. ROBOTICS 2022. [DOI: 10.3390/robotics11020053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study aims to investigate the effects of the degree of freedom (DOF) and weight of the hand exoskeleton (HE) on hand joint mobility function (ease of movement, movement range) in fine hand use activities. A three-digit passive HE prototype was built to fit each of the 12 participants. Two DOF setups (three DOF, two DOF), two digits’ weight levels (70 g, 140 g), and barehand conditions were tested. A productivity task (performed with Standardized-Nine Hole Peg Test) and motion tasks, both performing the tip pinch and tripod pinch, were conducted to measure the task completion time and the range of motion (ROM) of the digit joints, respectively, using a motion capture system. The perceived ease rating was also measured. The results showed that DOF reduction and weight addition caused a significant task completion time increase and rating drop (p < 0.05). Meanwhile, the DOF reduction increased the ROM reduction of the proximal interphalangeal joints; however, the weight addition caused a correction of the ROM reduction of several joints (p < 0.05) at the tripod pinch. In conclusion, wearing an HE reduces hand joint mobility, especially in lower DOF. However, a certain weight addition may improve joint mobility in terms of the fingers’ movement range.
Collapse
|
14
|
Gantenbein J, Dittli J, Meyer JT, Gassert R, Lambercy O. Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation. Front Neurorobot 2022; 16:815693. [PMID: 35264940 PMCID: PMC8900616 DOI: 10.3389/fnbot.2022.815693] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.
Collapse
Affiliation(s)
- Jessica Gantenbein
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Jan Dittli
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Jan Thomas Meyer
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| |
Collapse
|
15
|
Lau JCL, Mombaur K. Preliminary Study on a Novel Protocol for Improving Familiarity with a Lower-Limb Robotic Exoskeleton in Able-Bodied, First-Time Users. Front Robot AI 2022; 8:785251. [PMID: 35087873 PMCID: PMC8786600 DOI: 10.3389/frobt.2021.785251] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/25/2021] [Indexed: 11/21/2022] Open
Abstract
Lower-limb exoskeletons have been created for different healthcare needs, but no research has been done on developing a proper protocol for users to get accustomed to moving with one. The user manuals provided also do not include such instructions. A pre-test was conducted with the TWIN (IIT), which is a lower-limb exoskeleton made for persons with spinal cord injury. In the pre-test, two healthy, able-bodied graduate students indicated a need for a protocol that can better prepare able-bodied, first-time users to move with an exoskeleton. TWIN was used in this preliminary study and nine users were divided to receive a tutorial or no tutorial before walking with the exoskeleton. Due to COVID-19 regulations, the study could only be performed with healthy, young-to-middle-aged lab members that do not require walking support. The proposed protocol was evaluated with the System Usability Scale, NASA Raw Task Load Index, and two custom surveys. The members who received the tutorial found it easy to follow and helpful, but the tutorial seemed to come at a price of higher perceived mental and physical demands, which could stem from the longer testing duration and the need to constantly recall and apply the things learned from the tutorial. All results presented are preliminary, and it is recommended to include biomechanical analysis and conduct the experiment with more participants in the future. Nonetheless, this proof-of-concept study lays groundwork for future related studies and the protocol will be adjusted, applied, and validated to patients and geriatric users.
Collapse
|
16
|
Marchand C, De Graaf JB, Jarrassé N. Measuring mental workload in assistive wearable devices: a review. J Neuroeng Rehabil 2021; 18:160. [PMID: 34743700 PMCID: PMC8573948 DOI: 10.1186/s12984-021-00953-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/26/2021] [Indexed: 12/21/2022] Open
Abstract
As wearable assistive devices, such as prostheses and exoskeletons, become increasingly sophisticated and effective, the mental workload associated with their use remains high and becomes a major challenge to their ecological use and long-term adoption. Numerous methods of measuring mental workload co-exist, making analysis of this research topic difficult. The aim of this review is to examine how mental workload resulting from the use of wearable assistive devices has been measured, in order to gain insight into the specific possibilities and limitations of this field. Literature searches were conducted in the main scientific databases and 60 articles measuring the mental workload induced by the use of a wearable assistive device were included in this study. Three main families of methods were identified, the most common being 'dual task' and 'subjective assessment' methods, followed by those based on 'physiological measures', which included a wide variety of methods. The variability of the measurements was particularly high, making comparison difficult. There is as yet no evidence that any particular method of measuring mental workload is more appropriate to the field of wearable assistive devices. Each method has intrinsic limitations such as subjectivity, imprecision, robustness or complexity of implementation or interpretation. A promising metric seems to be the measurement of brain activity, as it is the only method that is directly related to mental workload. Finally, regardless of the measurement method chosen, special attention should be paid to the measurement of mental workload in the context of wearable assistive devices. In particular, certain practical considerations, such as ecological situations and environments or the level of expertise of the participants tested, may be essential to ensure the validity of the mental workload assessed.
Collapse
Affiliation(s)
- Charlotte Marchand
- CNRS, UMR 7222, ISIR / INSERM, U1150 Agathe-ISIR, Sorbonne Université, Paris, France
| | | | - Nathanaël Jarrassé
- CNRS, UMR 7222, ISIR / INSERM, U1150 Agathe-ISIR, Sorbonne Université, Paris, France.
| |
Collapse
|
17
|
Akbari A, Haghverd F, Behbahani S. Robotic Home-Based Rehabilitation Systems Design: From a Literature Review to a Conceptual Framework for Community-Based Remote Therapy During COVID-19 Pandemic. Front Robot AI 2021; 8:612331. [PMID: 34239898 PMCID: PMC8258116 DOI: 10.3389/frobt.2021.612331] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 06/01/2021] [Indexed: 01/24/2023] Open
Abstract
During the COVID-19 pandemic, the higher susceptibility of post-stroke patients to infection calls for extra safety precautions. Despite the imposed restrictions, early neurorehabilitation cannot be postponed due to its paramount importance for improving motor and functional recovery chances. Utilizing accessible state-of-the-art technologies, home-based rehabilitation devices are proposed as a sustainable solution in the current crisis. In this paper, a comprehensive review on developed home-based rehabilitation technologies of the last 10 years (2011-2020), categorizing them into upper and lower limb devices and considering both commercialized and state-of-the-art realms. Mechatronic, control, and software aspects of the system are discussed to provide a classified roadmap for home-based systems development. Subsequently, a conceptual framework on the development of smart and intelligent community-based home rehabilitation systems based on novel mechatronic technologies is proposed. In this framework, each rehabilitation device acts as an agent in the network, using the internet of things (IoT) technologies, which facilitates learning from the recorded data of the other agents, as well as the tele-supervision of the treatment by an expert. The presented design paradigm based on the above-mentioned leading technologies could lead to the development of promising home rehabilitation systems, which encourage stroke survivors to engage in under-supervised or unsupervised therapeutic activities.
Collapse
Affiliation(s)
| | | | - Saeed Behbahani
- Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran
| |
Collapse
|
18
|
Lambercy O, Lehner R, Chua K, Wee SK, Rajeswaran DK, Kuah CWK, Ang WT, Liang P, Campolo D, Hussain A, Aguirre-Ollinger G, Guan C, Kanzler CM, Wenderoth N, Gassert R. Neurorehabilitation From a Distance: Can Intelligent Technology Support Decentralized Access to Quality Therapy? Front Robot AI 2021; 8:612415. [PMID: 34026855 PMCID: PMC8132098 DOI: 10.3389/frobt.2021.612415] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 04/20/2021] [Indexed: 12/18/2022] Open
Abstract
Current neurorehabilitation models primarily rely on extended hospital stays and regular therapy sessions requiring close physical interactions between rehabilitation professionals and patients. The current COVID-19 pandemic has challenged this model, as strict physical distancing rules and a shift in the allocation of hospital resources resulted in many neurological patients not receiving essential therapy. Accordingly, a recent survey revealed that the majority of European healthcare professionals involved in stroke care are concerned that this lack of care will have a noticeable negative impact on functional outcomes. COVID-19 highlights an urgent need to rethink conventional neurorehabilitation and develop alternative approaches to provide high-quality therapy while minimizing hospital stays and visits. Technology-based solutions, such as, robotics bear high potential to enable such a paradigm shift. While robot-assisted therapy is already established in clinics, the future challenge is to enable physically assisted therapy and assessments in a minimally supervized and decentralized manner, ideally at the patient’s home. Key enablers are new rehabilitation devices that are portable, scalable and equipped with clinical intelligence, remote monitoring and coaching capabilities. In this perspective article, we discuss clinical and technological requirements for the development and deployment of minimally supervized, robot-assisted neurorehabilitation technologies in patient’s homes. We elaborate on key principles to ensure feasibility and acceptance, and on how artificial intelligence can be leveraged for embedding clinical knowledge for safe use and personalized therapy adaptation. Such new models are likely to impact neurorehabilitation beyond COVID-19, by providing broad access to sustained, high-quality and high-dose therapy maximizing long-term functional outcomes.
Collapse
Affiliation(s)
- Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Switzerland.,Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Rea Lehner
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Switzerland
| | - Karen Chua
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,Centre for Advanced Rehabilitation Therapeutics, Tan Tock Seng Hospital Rehabilitation Centre, Singapore, Singapore.,Rehabilitation Research Institute Singapore, Nanyang Technological University, Singapore, Singapore
| | - Seng Kwee Wee
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,Centre for Advanced Rehabilitation Therapeutics, Tan Tock Seng Hospital Rehabilitation Centre, Singapore, Singapore.,Singapore Institute of Technology (SIT), Singapore, Singapore
| | - Deshan Kumar Rajeswaran
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,Centre for Advanced Rehabilitation Therapeutics, Tan Tock Seng Hospital Rehabilitation Centre, Singapore, Singapore
| | - Christopher Wee Keong Kuah
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,Centre for Advanced Rehabilitation Therapeutics, Tan Tock Seng Hospital Rehabilitation Centre, Singapore, Singapore
| | - Wei Tech Ang
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,Rehabilitation Research Institute Singapore, Nanyang Technological University, Singapore, Singapore.,School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Phyllis Liang
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,Rehabilitation Research Institute Singapore, Nanyang Technological University, Singapore, Singapore
| | - Domenico Campolo
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Asif Hussain
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.,Articares Pte Ltd, Singapore, Singapore
| | | | - Cuntai Guan
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Christoph M Kanzler
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Switzerland.,Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Nicole Wenderoth
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore.,Neural Control of Movement Laboratory, Department of Health Sciences and Technology, ETH Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Switzerland.,Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| |
Collapse
|
19
|
Discussion on the Rehabilitation of Stroke Hemiplegia Based on Interdisciplinary Combination of Medicine and Engineering. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2021; 2021:6631835. [PMID: 33815554 PMCID: PMC7990546 DOI: 10.1155/2021/6631835] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/21/2021] [Accepted: 02/20/2021] [Indexed: 11/25/2022]
Abstract
Interdisciplinary combinations of medicine and engineering are part of the strategic plan of many universities aiming to be world-class institutions. One area in which these interactions have been prominent is rehabilitation of stroke hemiplegia. This article reviews advances in the last five years of stroke hemiplegia rehabilitation via interdisciplinary combination of medicine and engineering. Examples of these technologies include VR, RT, mHealth, BCI, tDCS, rTMS, and TCM rehabilitation. In this article, we will summarize the latest research in these areas and discuss the advantages and disadvantages of each to examine the frontiers of interdisciplinary medicine and engineering advances.
Collapse
|
20
|
Tucan P, Vaida C, Ulinici I, Banica A, Burz A, Pop N, Birlescu I, Gherman B, Plitea N, Antal T, Carbone G, Pisla D. Optimization of the ASPIRE Spherical Parallel Rehabilitation Robot Based on Its Clinical Evaluation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:3281. [PMID: 33810042 PMCID: PMC8004699 DOI: 10.3390/ijerph18063281] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 11/16/2022]
Abstract
The paper presents the design optimization of the ASPIRE spherical parallel robot for shoulder rehabilitation following clinical evaluation and clinicians' feedback. After the development of the robotic structure and the implementation of the control system, ASPIRE was prepared for clinical evaluation. A set of clinical trials was performed on 24 patients with different neurological disorders to obtain the patient and clinician acceptance of the rehabilitation system. During the clinical trials, the behavior of the robotic system was closely monitored and analyzed in order to improve its reliability and overall efficiency. Along with its reliability and efficiency, special attention was given to the safety characteristics during the rehabilitation task.
Collapse
Affiliation(s)
- Paul Tucan
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Calin Vaida
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Ionut Ulinici
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Alexandru Banica
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Alin Burz
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Nicoleta Pop
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Iosif Birlescu
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Bogdan Gherman
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Nicolae Plitea
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | - Tiberiu Antal
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| | | | - Doina Pisla
- CESTER, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania; (P.T.); (I.U.); (A.B.); (A.B.); (N.P.); (I.B.); (B.G.); (N.P.); (T.A.)
| |
Collapse
|