1
|
Sarwat H, Alkhashab A, Song X, Jiang S, Jia J, Shull PB. Post-stroke hand gesture recognition via one-shot transfer learning using prototypical networks. J Neuroeng Rehabil 2024; 21:100. [PMID: 38867287 PMCID: PMC11167772 DOI: 10.1186/s12984-024-01398-7] [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: 01/10/2024] [Accepted: 05/31/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND In-home rehabilitation systems are a promising, potential alternative to conventional therapy for stroke survivors. Unfortunately, physiological differences between participants and sensor displacement in wearable sensors pose a significant challenge to classifier performance, particularly for people with stroke who may encounter difficulties repeatedly performing trials. This makes it challenging to create reliable in-home rehabilitation systems that can accurately classify gestures. METHODS Twenty individuals who suffered a stroke performed seven different gestures (mass flexion, mass extension, wrist volar flexion, wrist dorsiflexion, forearm pronation, forearm supination, and rest) related to activities of daily living. They performed these gestures while wearing EMG sensors on the forearm, as well as FMG sensors and an IMU on the wrist. We developed a model based on prototypical networks for one-shot transfer learning, K-Best feature selection, and increased window size to improve model accuracy. Our model was evaluated against conventional transfer learning with neural networks, as well as subject-dependent and subject-independent classifiers: neural networks, LGBM, LDA, and SVM. RESULTS Our proposed model achieved 82.2% hand-gesture classification accuracy, which was better (P<0.05) than one-shot transfer learning with neural networks (63.17%), neural networks (59.72%), LGBM (65.09%), LDA (63.35%), and SVM (54.5%). In addition, our model performed similarly to subject-dependent classifiers, slightly lower than SVM (83.84%) but higher than neural networks (81.62%), LGBM (80.79%), and LDA (74.89%). Using K-Best features improved the accuracy in 3 of the 6 classifiers used for evaluation, while not affecting the accuracy in the other classifiers. Increasing the window size improved the accuracy of all the classifiers by an average of 4.28%. CONCLUSION Our proposed model showed significant improvements in hand-gesture recognition accuracy in individuals who have had a stroke as compared with conventional transfer learning, neural networks and traditional machine learning approaches. In addition, K-Best feature selection and increased window size can further improve the accuracy. This approach could help to alleviate the impact of physiological differences and create a subject-independent model for stroke survivors that improves the classification accuracy of wearable sensors. TRIAL REGISTRATION NUMBER The study was registered in Chinese Clinical Trial Registry with registration number CHiCTR1800017568 in 2018/08/04.
Collapse
Affiliation(s)
- Hussein Sarwat
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China
| | - Amr Alkhashab
- Robot Offline Programming, Visual Components, Vänrikinkuja, Espoo, 02600, Finland
| | - Xinyu Song
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China
| | - Shuo Jiang
- College of Electronics and Information Engineering, Tongji University, Cao'an Highway, Shanghai, 201804, China
| | - Jie Jia
- The Department of Rehabilitation Medicine, The National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
| | - Peter B Shull
- School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China.
| |
Collapse
|
2
|
Soleimani M, Ghazisaeedi M, Heydari S. The efficacy of virtual reality for upper limb rehabilitation in stroke patients: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2024; 24:135. [PMID: 38790042 PMCID: PMC11127427 DOI: 10.1186/s12911-024-02534-y] [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: 11/14/2023] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Stroke frequently gives rise to incapacitating motor impairments in the upper limb. Virtual reality (VR) rehabilitation has exhibited potential for augmenting upper extremity recovery; nonetheless, the optimal techniques for such interventions remain a topic of uncertainty. The present systematic review and meta-analysis were undertaken to comprehensively compare VR-based rehabilitation with conventional occupational therapy across a spectrum of immersion levels and outcome domains. METHODS A systematic search was conducted in PubMed, IEEE, Scopus, Web of Science, and PsycNET databases to identify randomized controlled trials about upper limb rehabilitation in stroke patients utilizing VR interventions. The search encompassed studies published in the English language up to March 2023. The identified studies were stratified into different categories based on the degree of immersion employed: non-immersive, semi-immersive, and fully-immersive settings. Subsequent meta-analyses were executed to assess the impact of VR interventions on various outcome measures. RESULTS Of the 11,834 studies screened, 55 studies with 2142 patients met the predefined inclusion criteria. VR conferred benefits over conventional therapy for upper limb motor function, functional independence, Quality of life, Spasticity, and dexterity. Fully immersive VR showed the greatest gains in gross motor function, while non-immersive approaches enhanced fine dexterity. Interventions exceeding six weeks elicited superior results, and initiating VR within six months post-stroke optimized outcomes. CONCLUSIONS This systematic review and meta-analysis demonstrates that adjunctive VR-based rehabilitation enhances upper limb motor recovery across multiple functional domains compared to conventional occupational therapy alone after stroke. Optimal paradigms likely integrate VR's immersive capacity with conventional techniques. TRIAL REGISTRATION This systematic review and meta-analysis retrospectively registered in the OSF registry under the identifier [ https://doi.org/10.17605/OSF.IO/YK2RJ ].
Collapse
Affiliation(s)
- Mohsen Soleimani
- Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Marjan Ghazisaeedi
- Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Soroush Heydari
- Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
3
|
Papagiannis G, Triantafyllou Α, Yiannopoulou KG, Georgoudis G, Kyriakidou M, Gkrilias P, Skouras AZ, Bega X, Stasinopoulos D, Matsopoulos G, Syringas P, Tselikas N, Zestas O, Potsika V, Pardalis A, Papaioannou C, Protopappas V, Malizos N, Tachos N, Fotiadis DI. Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test. Sci Rep 2024; 14:10598. [PMID: 38719940 PMCID: PMC11079036 DOI: 10.1038/s41598-024-61070-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 04/30/2024] [Indexed: 05/12/2024] Open
Abstract
A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.
Collapse
Grants
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- Τ2ΕΔΚ04333 European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE- INNOVATE
- European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH–CREATE– INNOVATE
Collapse
Affiliation(s)
- Georgios Papagiannis
- Biomechanics Laboratory, Physiotherapy Department, University of the Peloponnese, 23100, Sparta, Greece.
- Physioloft, Physiotherapy Center, 14562, Kifisia, Greece.
| | - Αthanasios Triantafyllou
- Biomechanics Laboratory, Physiotherapy Department, University of the Peloponnese, 23100, Sparta, Greece
- Physioloft, Physiotherapy Center, 14562, Kifisia, Greece
| | | | - George Georgoudis
- Department of Physiotherapy, University of West Attica, 12243, Athens, Greece
| | - Maria Kyriakidou
- Biomechanics Laboratory, Physiotherapy Department, University of the Peloponnese, 23100, Sparta, Greece
| | - Panagiotis Gkrilias
- Biomechanics Laboratory, Physiotherapy Department, University of the Peloponnese, 23100, Sparta, Greece
| | - Apostolos Z Skouras
- Sports Excellence, 1St Department of Orthopaedic Surgery, National and Kapodistrian University of Athens, 12462, Athens, Greece
| | - Xhoi Bega
- Physioloft, Physiotherapy Center, 14562, Kifisia, Greece
| | | | - George Matsopoulos
- Biomedical Engineering Laboratory, National Technical University of Athens, 9, Herοon Polytechniou Str., Zografou, 15773, Athens, Greece
| | - Pantelis Syringas
- Biomedical Engineering Laboratory, National Technical University of Athens, 9, Herοon Polytechniou Str., Zografou, 15773, Athens, Greece
| | - Nikolaos Tselikas
- CNA Lab, Department of Informatics, Telecommunications University of Peloponnese, 22100, Tripoli, Greece
| | - Orestis Zestas
- CNA Lab, Department of Informatics, Telecommunications University of Peloponnese, 22100, Tripoli, Greece
| | - Vassiliki Potsika
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110, Ioannina, Greece
| | - Athanasios Pardalis
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110, Ioannina, Greece
| | - Christoforos Papaioannou
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110, Ioannina, Greece
| | | | | | - Nikolaos Tachos
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110, Ioannina, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, 45110, Ioannina, Greece
- Biomedical Research Institute, Foundation for Research and Technology-Hellas (FORTH), 70013, Heraklion, Greece
| |
Collapse
|
4
|
Figeys M, Koubasi F, Hwang D, Hunder A, Miguel-Cruz A, Ríos Rincón A. Challenges and promises of mixed-reality interventions in acquired brain injury rehabilitation: A scoping review. Int J Med Inform 2023; 179:105235. [PMID: 37806176 DOI: 10.1016/j.ijmedinf.2023.105235] [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: 05/16/2023] [Revised: 08/02/2023] [Accepted: 09/24/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND Acquired brain injury (ABI) can lead to significant impairments and difficulties in everyday life, necessitating the need for rehabilitation. Mixed-reality (MR) technologies have revolutionized the delivery of neurorehabilitation therapies. However, inconsistencies in research methodology, diverse study populations and designs, and exaggerated claims in the research, media, and private consumer sectors have impacted the knowledge base of the field, including within the context of ABI rehabilitation. OBJECTIVE This scoping review aims to explore MR-systems in ABI rehabilitation, while assessing the evidence base and technology readiness levels of these systems. METHODS Seven databases were searched for studies, which were screened and analyzed by two independent raters. The types of MR systems, levels of evidence, and technology readiness levels were extracted and analyzed using descriptive analyses. RESULTS Twenty-six studies were included in the review, all of which focused on ABI etiologies stemming from strokes. Across studies, upper-limb motor rehabilitation was the most common rehabilitation target of MR interventions, followed by gait, cognition, and lower-extremity functioning. At present, overall results indicate low evidence for MR-applications in ABI rehabilitation, with a median technology readiness level of 6, corresponding to system prototypes being tested in relevant environments. CONCLUSION Although challenges regarding system usability and design were reported, results appear promising with ongoing research. With variability across studies, technologies, and populations, determining the effectiveness of MR interventions in ABI remains a challenge, necessitating the need for ongoing innovation, research, and development of these systems.
Collapse
Affiliation(s)
- Mathieu Figeys
- Department of Occupational Therapy, Faculty of Rehabilitation Medicine, College of Health Sciences, University of Alberta, Edmonton, Canada; Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, Canada.
| | - Farnaz Koubasi
- Department of Occupational Therapy, Faculty of Rehabilitation Medicine, College of Health Sciences, University of Alberta, Edmonton, Canada
| | - Doyeon Hwang
- School of Public Health, University of Alberta, Edmonton, Canada
| | - Allison Hunder
- Faculty of Kinesiology, Sport, & Recreation, University of Alberta, Edmonton, Canada
| | - Antonio Miguel-Cruz
- Department of Occupational Therapy, Faculty of Rehabilitation Medicine, College of Health Sciences, University of Alberta, Edmonton, Canada; Glenrose Rehabilitation Hospital, Alberta Health Services, Edmonton, Canada; Faculty of Health, University of Waterloo, Waterloo, Canada
| | - Adriana Ríos Rincón
- Department of Occupational Therapy, Faculty of Rehabilitation Medicine, College of Health Sciences, University of Alberta, Edmonton, Canada
| |
Collapse
|
5
|
Howard MC, Davis MM. A meta-analysis and systematic literature review of mixed reality rehabilitation programs: Investigating design characteristics of augmented reality and augmented virtuality. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2022.107197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|