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Bai J, Li G, Lu X, Wen X. Automatic rehabilitation assessment method of upper limb motor function based on posture and distribution force. Front Neurosci 2024; 18:1362495. [PMID: 38440394 PMCID: PMC10909926 DOI: 10.3389/fnins.2024.1362495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 01/30/2024] [Indexed: 03/06/2024] Open
Abstract
The clinical rehabilitation assessment methods for hemiplegic upper limb motor function are often subjective, time-consuming, and non-uniform. This study proposes an automatic rehabilitation assessment method for upper limb motor function based on posture and distributed force measurements. Azure Kinect combined with MediaPipe was used to detect upper limb and hand movements, and the array distributed flexible thin film pressure sensor was employed to measure the distributed force of hand. This allowed for the automated measurement of 30 items within the Fugl-Meyer scale. Feature information was extracted separately from the affected and healthy sides, the feature ratios or deviation were then fed into a single/multiple fuzzy logic assessment model to determine the assessment score of each item. Finally, the total score of the hemiplegic upper limb motor function assessment was derived. Experiments were performed to evaluate the motor function of the subjects' upper extremities. Bland-Altman plots of physician and system scores showed good agreement. The results of the automated assessment system were highly correlated with the clinical Fugl-Meyer total score (r = 0.99, p < 0.001). The experimental results state that this system can automatically assess the motor function of the affected upper limb by measuring the posture and force distribution.
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Affiliation(s)
- Jing Bai
- Industrial Technology Research Institute of Intelligent Equipment, Nanjing Institute of Technology, Nanjing, China
- Jiangsu Provincial Engineering Laboratory of Intelligent Manufacturing Equipment, Nanjing, China
| | - Guocheng Li
- Automation Department, Nanjing Institute of Technology, Nanjing, China
| | - Xuanming Lu
- Industrial Technology Research Institute of Intelligent Equipment, Nanjing Institute of Technology, Nanjing, China
- Jiangsu Provincial Engineering Laboratory of Intelligent Manufacturing Equipment, Nanjing, China
| | - Xiulan Wen
- Automation Department, Nanjing Institute of Technology, Nanjing, China
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2
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Kushnir A, Kachmar O, Bonnechère B. STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring. SENSORS (BASEL, SWITZERLAND) 2024; 24:351. [PMID: 38257442 PMCID: PMC10818392 DOI: 10.3390/s24020351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/21/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024]
Abstract
Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient's progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings.
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Affiliation(s)
- Anna Kushnir
- Elita Rehabilitation Center, 79000 Lviv, Ukraine;
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
| | - Oleh Kachmar
- Elita Rehabilitation Center, 79000 Lviv, Ukraine;
| | - Bruno Bonnechère
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, 3590 Diepenbeek, Belgium
- Department of PXL-Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium
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Pedersini P, Picciolini S, Di Salvo F, Toccafondi A, Novembre G, Gualerzi A, Cusmano I, Garascia A, Tavanelli M, Verde A, Masciocco G, Ricci C, Mannini A, Bedoni M, Morici N. The Exercise aNd hEArt transplant (ENEA) trial - a registry-based randomized controlled trial evaluating the safety and efficacy of cardiac telerehabilitation after heart transplant. Contemp Clin Trials 2024; 136:107415. [PMID: 38114046 DOI: 10.1016/j.cct.2023.107415] [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: 11/06/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND Heart transplant (HTx) is gold-standard therapy for patients with end-stage heart failure. Cardiac rehabilitation (CR) is a multidisciplinary intervention shown to improve cardiovascular prognosis and quality of life. The aim in this randomized controlled trial is to explore the safety and efficacy of cardiac telerehabilitation after HTx. In addition, biomarkers of rehabilitation outcomes will be identified, as data that will enable treatment to be tailored to patient phenotype. METHODS Patients after HTx will be recruited at IRCCS S. Maria Nascente - Fondazione Don Gnocchi, Milan, Italy (n = 40). Consenting participants will be randomly allocated to either of two groups (1:1): an intervention group who will receive on-site CR followed by 12 weeks of telerehabilitation, or a control group who will receive on-site CR followed by standard homecare and exercise programme. Recruitment began on 20th May 2023 and is expected to continue until 20th May 2025. Socio-demographic characteristics, lifestyle, health status, cardiovascular events, cognitive function, anxiety and depression symptoms, and quality of life will be assessed, as well as exercise capacity and muscular endurance. Participants will be evaluated before the intervention, post-CR and after 6 months. In addition, analysis of circulating extracellular vesicles using Surface Plasmon Resonance imaging (SPRi), based on a rehabilomic approach, will be applied to both groups pre- and post-CR. CONCLUSION This study will explore the safety and efficacy of cardiac telerehabilitation after HTx. In addition, a rehabilomic approach will be used to investigate biomolecular phenotypization in HTx patients. TRIAL REGISTRATION NUMBER ClinicalTrials.gov Identifier: NCT05824364.
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Affiliation(s)
| | | | | | | | | | | | | | - Andrea Garascia
- Cardiology Department and De Gasperis Cardio Center, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | | | - Alessandro Verde
- Cardiology Department and De Gasperis Cardio Center, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Gabriella Masciocco
- Cardiology Department and De Gasperis Cardio Center, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Cristian Ricci
- Africa Unit for transdisciplinary Health Research, North-West University, Potchefstroom, South Africa
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Bonanno M, Calabrò RS. Bridging the Gap between Basic Research and Clinical Practice: The Growing Role of Translational Neurorehabilitation. MEDICINES (BASEL, SWITZERLAND) 2023; 10:45. [PMID: 37623809 PMCID: PMC10456256 DOI: 10.3390/medicines10080045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/25/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023]
Abstract
Translational neuroscience is intended as a holistic approach in the field of brain disorders, starting from the basic research of cerebral morphology and with the function of implementing it into clinical practice. This concept can be applied to the rehabilitation field to promote promising results that positively influence the patient's quality of life. The last decades have seen great scientific and technological improvements in the field of neurorehabilitation. In this paper, we discuss the main issues related to translational neurorehabilitation, from basic research to current clinical practice, and we also suggest possible future scenarios.
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Affiliation(s)
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi “Bonino-Pulejox”, Via Palermo, SS 113, C. da Casazza, 98124 Messina, Italy;
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Garro F, Fenoglio E, Forsiuk I, Canepa M, Mozzon M, De Michieli L, Buccelli S, Chiappalone M, Semprini M. NeBULA: A Standardized Protocol for the Benchmarking of Robotic-based Upper Limb Neurorehabilitation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083145 DOI: 10.1109/embc40787.2023.10340242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The use of robotic technologies in neurorehabilitation is growing, because they allow highly repeatable exercise protocols and patient-tailored therapies. However, there is a lack of objective methods for assessing these technologies, which makes it difficult to determine their value in rehabilitation settings. While there exist many outcome measurements for motor assessment from a clinical standpoint (such as the Fugl-Meyer scale), the evaluation of performance and clinical benefits of technology for rehabilitation still lacks a standardized approach from a technical standpoint.In this work, we describe NeBULA (Neuromechanical Biomarkers for Upper Limb Assessment), a benchmarking platform for evaluating robotic technology for upper limb neurorehabilitation. By utilizing standardized neuromechanical biomarkers, NeBULA aims at providing a groundwork for assessing and comparing neurorehabilitation robots. We describe its implementation and preliminary results assessing a novel upper limb exoskeleton.Clinical Relevance- Standardized evaluation of neurorehabilitation robots can lead to better patient outcomes, optimizing resources by identifying the most effective technology and by boosting their use in clinical practice. This would provide quantitative and objective information to complement clinical motor evaluation - preventing suboptimal treatments and ensuring that patients receive personalized care. It can also facilitate the transfer of technologyto clinics, identifying the most promising ones for further investment and research.
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Lopez-Castellanos JM, Ramon JL, Pomares J, Garcia GJ, Ubeda A. Multisensory Evaluation of Muscle Activity and Human Manipulability during Upper Limb Motor Tasks. BIOSENSORS 2023; 13:697. [PMID: 37504097 PMCID: PMC10377320 DOI: 10.3390/bios13070697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/22/2023] [Accepted: 06/27/2023] [Indexed: 07/29/2023]
Abstract
In this work, we evaluate the relationship between human manipulability indices obtained from motion sensing cameras and a variety of muscular factors extracted from surface electromyography (sEMG) signals from the upper limb during specific movements that include the shoulder, elbow and wrist joints. The results show specific links between upper limb movements and manipulability, revealing that extreme poses show less manipulability, i.e., when the arms are fully extended or fully flexed. However, there is not a clear correlation between the sEMG signals' average activity and manipulability factors, which suggests that muscular activity is, at least, only indirectly related to human pose singularities. A possible means to infer these correlations, if any, would be the use of advanced deep learning techniques. We also analyze a set of EMG metrics that give insights into how muscular effort is distributed during the exercises. This set of metrics could be used to obtain good indicators for the quantitative evaluation of sequences of movements according to the milestones of a rehabilitation therapy or to plan more ergonomic and bearable movement phases in a working task.
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Affiliation(s)
- Jose M Lopez-Castellanos
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
- Department of Systems Engineering, National Autonomous University of Honduras, Tegucigalpa 11101, Honduras
| | - Jose L Ramon
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
| | - Jorge Pomares
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
| | - Gabriel J Garcia
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
| | - Andres Ubeda
- Human Robotics Group, University of Alicante, 03690 San Vicente del Raspeig, Spain
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Wei W, Tan F, Zhang H, Mao H, Fu M, Samuel OW, Li G. Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition. Sci Data 2023; 10:358. [PMID: 37280249 DOI: 10.1038/s41597-023-02263-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/23/2023] [Indexed: 06/08/2023] Open
Abstract
Surface electromyogram (sEMG) offers a rich set of motor information for decoding limb motion intention that serves as a control input to Intelligent human-machine synergy systems (IHMSS). Despite growing interest in IHMSS, the current publicly available datasets are limited and can hardly meet the growing demands of researchers. This study presents a novel lower limb motion dataset (designated as SIAT-LLMD), comprising sEMG, kinematic, and kinetic data with corresponding labels acquired from 40 healthy humans during 16 movements. The kinematic and kinetic data were collected using a motion capture system and six-dimensional force platforms and processed using OpenSim software. The sEMG data were recorded using nine wireless sensors placed on the subjects' thigh and calf muscles on the left limb. Besides, SIAT-LLMD provides labels to classify the different movements and different gait phases. Analysis of the dataset verified the synchronization and reproducibility, and codes for effective data processing are provided. The proposed dataset can serve as a new resource for exploring novel algorithms and models for characterizing lower limb movements.
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Affiliation(s)
- Wenhao Wei
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), and the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518055, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Fangning Tan
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), and the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518055, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Hang Zhang
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - He Mao
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), and the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518055, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Menglong Fu
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Oluwarotimi Williams Samuel
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), and the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518055, China.
- School of Computing and Engineering, University of Derby, Derby, DE22 3AW, UK.
- Data Science Research Center, University of Derby, Derby, DE22 3AW, UK.
| | - Guanglin Li
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), and the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518055, China.
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.
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Bonnechère B, Timmermans A, Michiels S. Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020875. [PMID: 36679672 PMCID: PMC9866361 DOI: 10.3390/s23020875] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/19/2022] [Accepted: 01/05/2023] [Indexed: 06/01/2023]
Abstract
The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot-or can only partially-be applied in rehabilitation. Well-powered, high-quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drug validation studies, limited funding since not sponsored by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high levels of evidence are sparse. The current limitations of EBP in rehabilitation are presented in this narrative review, and innovative solutions are suggested, such as technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the current limitations. The development and implementation of new technologies can increase the quality of research and the level of evidence supporting rehabilitation, provided some adaptations are made to our research methodology.
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Affiliation(s)
- Bruno Bonnechère
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
- Technology-Supported and Data-Driven Rehabilitation, Data Science Institute, Hasselt University, 3590 Diepenbeek, Belgium
| | - Annick Timmermans
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
| | - Sarah Michiels
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
- Department of Otorhinolaryngology, Antwerp University Hospital, 2650 Edegem, Belgium
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Bonnechère B. Integrating Rehabilomics into the Multi-Omics Approach in the Management of Multiple Sclerosis: The Way for Precision Medicine? Genes (Basel) 2022; 14:63. [PMID: 36672802 PMCID: PMC9858788 DOI: 10.3390/genes14010063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/05/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Over recent years, significant improvements have been made in the understanding of (epi)genetics and neuropathophysiological mechanisms driving the different forms of multiple sclerosis (MS). For example, the role and importance of the bidirectional communications between the brain and the gut-also referred to as the gut-brain axis-in the pathogenesis of MS is receiving increasing interest in recent years and is probably one of the most promising areas of research for the management of people with MS. However, despite these important advances, it must be noted that these data are not-yet-used in rehabilitation. Neurorehabilitation is a cornerstone of MS patient management, and there are many techniques available to clinicians and patients, including technology-supported rehabilitation. In this paper, we will discuss how new findings on the gut microbiome could help us to better understand how rehabilitation can improve motor and cognitive functions. We will also see how the data gathered during the rehabilitation can help to get a better diagnosis of the patients. Finally, we will discuss how these new techniques can better guide rehabilitation to lead to precision rehabilitation and ultimately increase the quality of patient care.
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Affiliation(s)
- Bruno Bonnechère
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Technology-Supported and Data-Driven Rehabilitation, Data Science Institute, Hasselt University, 3590 Diepenbeek, Belgium
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Longatelli V, Torricelli D, Tornero J, Pedrocchi A, Molteni F, Pons JL, Gandolla M. A unified scheme for the benchmarking of upper limb functions in neurological disorders. J Neuroeng Rehabil 2022; 19:102. [PMID: 36167552 PMCID: PMC9513990 DOI: 10.1186/s12984-022-01082-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 09/08/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In neurorehabilitation, we are witnessing a growing awareness of the importance of standardized quantitative assessment of limb functions. Detailed assessments of the sensorimotor deficits following neurological disorders are crucial. So far, this assessment has relied mainly on clinical scales, which showed several drawbacks. Different technologies could provide more objective and repeatable measurements. However, the current literature lacks practical guidelines for this purpose. Nowadays, the integration of available metrics, protocols, and algorithms into one harmonized benchmarking ecosystem for clinical and research practice is necessary. METHODS This work presents a benchmarking framework for upper limb capacity. The scheme resulted from a multidisciplinary and iterative discussion among several partners with previous experience in benchmarking methodology, robotics, and clinical neurorehabilitation. We merged previous knowledge in benchmarking methodologies for human locomotion and direct clinical and engineering experience in upper limb rehabilitation. The scheme was designed to enable an instrumented evaluation of arm capacity and to assess the effectiveness of rehabilitative interventions with high reproducibility and resolution. It includes four elements: (1) a taxonomy for motor skills and abilities, (2) a list of performance indicators, (3) a list of required sensor modalities, and (4) a set of reproducible experimental protocols. RESULTS We proposed six motor primitives as building blocks of most upper-limb daily-life activities and combined them into a set of functional motor skills. We identified the main aspects to be considered during clinical evaluation, and grouped them into ten motor abilities categories. For each ability, we proposed a set of performance indicators to quantify the proposed ability on a quantitative and high-resolution scale. Finally, we defined the procedures to be followed to perform the benchmarking assessment in a reproducible and reliable way, including the definition of the kinematic models and the target muscles. CONCLUSIONS This work represents the first unified scheme for the benchmarking of upper limb capacity. To reach a consensus, this scheme should be validated with real experiments across clinical conditions and motor skills. This validation phase is expected to create a shared database of human performance, necessary to have realistic comparisons of treatments and drive the development of new personalized technologies.
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Affiliation(s)
- Valeria Longatelli
- Neuroengineering and Medical Robotics Laboratory and WE-COBOT Laboratory, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Diego Torricelli
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), Madrid, Spain
| | - Jesús Tornero
- Advanced Neurorehabilitation Unit, Hospital Los Madroños, Madrid, Spain
| | - Alessandra Pedrocchi
- Neuroengineering and Medical Robotics Laboratory and WE-COBOT Laboratory, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Franco Molteni
- Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Italy
| | | | - Marta Gandolla
- WE-COBOT Laboratory, Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy
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After 55 Years of Neurorehabilitation, What Is the Plan? Brain Sci 2022; 12:brainsci12080982. [PMID: 35892423 PMCID: PMC9330852 DOI: 10.3390/brainsci12080982] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/17/2022] [Accepted: 07/22/2022] [Indexed: 02/05/2023] Open
Abstract
Neurological disorders often cause severe long-term disabilities with substantial activity limitations and participation restrictions such as community integration, family functioning, employment, social interaction and participation. Increasing understanding of brain functioning has opened new perspectives for more integrative interventions, boosting the intrinsic central nervous system neuroplastic capabilities in order to achieve efficient behavioral restitution. Neurorehabilitation must take into account the many aspects of the individual through a comprehensive analysis of actual and potential cognitive, behavioral, emotional and physical skills, while increasing awareness and understanding of the new self of the person being dealt with. The exclusive adoption by the rehabilitator of objective functional measures often overlooks the values and goals of the disabled person. Indeed, each individual has their own rhythm, unique life history and personality construct. In this challenging context, it is essential to deepen the assessment through subjective measures, which more adequately reflect the patient’s perspective in order to shape genuinely tailored instead of standardized neurorehabilitation approaches. In this overly complex panorama, where confounding and prognostic factors also strongly influence potential functional recovery, the healthcare community needs to rethink neurorehabilitation formats.
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Barresi G, Pacchierotti C, Laffranchi M, De Michieli L. Beyond Digital Twins: Phygital Twins for Neuroergonomics in Human-Robot Interaction. Front Neurorobot 2022; 16:913605. [PMID: 35845760 PMCID: PMC9277562 DOI: 10.3389/fnbot.2022.913605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/23/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Giacinto Barresi
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
- *Correspondence: Giacinto Barresi
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Labruyère R. Robot-assisted gait training: more randomized controlled trials are needed! Or maybe not? J Neuroeng Rehabil 2022; 19:58. [PMID: 35676742 PMCID: PMC9178806 DOI: 10.1186/s12984-022-01037-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/26/2022] [Indexed: 11/30/2022] Open
Abstract
I was encouraged by the recent article by Kuo et al. entitled “Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders” to write an opinion piece on the possible further development of stationary robot-assisted gait training research. Randomized clinical trials investigating stationary gait robots have not shown the superiority of these devices over comparable interventions regarding clinical effectiveness, and there are clinical practice guidelines that even recommend against their use. Nevertheless, these devices are still widely used, and our field needs to find ways to apply these devices more effectively. The authors of the article mentioned above feed different machine learning algorithms with patients’ data from the beginning of a robot-assisted gait training intervention using the robot Lokomat. The output of these algorithms allows predictions of the clinical outcome (i.e., functional ambulation categories) while the patients are still participating in the intervention. Such an analysis based on the collection of the device’s data could optimize the application of these devices. The article provides an example of how our field of research could make progress as we advance, and in this opinion piece, I would like to present my view on the prioritization of upcoming research on robot-assisted gait training. Furthermore, I briefly speculate on some drawbacks of randomized clinical trials in the field of robot-assisted gait training and how the quality and thus the effectiveness of robot-assisted gait training could potentially be improved based on the collection and analysis of clinical training data, a better patient selection and by giving greater weight to the motivational aspects for the participants.
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Affiliation(s)
- Rob Labruyère
- Swiss Children's Rehab, University Children's Hospital Zurich, Mühlebergstrasse 104, 8910, Affoltern am Albis, Switzerland. .,Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
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