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De Pasquale P, Bonanno M, Mojdehdehbaher S, Quartarone A, Calabrò RS. The Use of Head-Mounted Display Systems for Upper Limb Kinematic Analysis in Post-Stroke Patients: A Perspective Review on Benefits, Challenges and Other Solutions. Bioengineering (Basel) 2024; 11:538. [PMID: 38927774 DOI: 10.3390/bioengineering11060538] [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: 04/11/2024] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
In recent years, there has been a notable increase in the clinical adoption of instrumental upper limb kinematic assessment. This trend aligns with the rising prevalence of cerebrovascular impairments, one of the most prevalent neurological disorders. Indeed, there is a growing need for more objective outcomes to facilitate tailored rehabilitation interventions following stroke. Emerging technologies, like head-mounted virtual reality (HMD-VR) platforms, have responded to this demand by integrating diverse tracking methodologies. Specifically, HMD-VR technology enables the comprehensive tracking of body posture, encompassing hand position and gesture, facilitated either through specific tracker placements or via integrated cameras coupled with sophisticated computer graphics algorithms embedded within the helmet. This review aims to present the state-of-the-art applications of HMD-VR platforms for kinematic analysis of the upper limb in post-stroke patients, comparing them with conventional tracking systems. Additionally, we address the potential benefits and challenges associated with these platforms. These systems might represent a promising avenue for safe, cost-effective, and portable objective motor assessment within the field of neurorehabilitation, although other systems, including robots, should be taken into consideration.
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Affiliation(s)
- Paolo De Pasquale
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy
| | - Mirjam Bonanno
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy
| | - Sepehr Mojdehdehbaher
- Department of Mathematics, Computer Science, Physics and Earth Sciences (MIFT), University of Messina, Viale Ferdinando Stagno d'Alcontres, 31, 98166 Messina, Italy
| | - Angelo Quartarone
- IRCCS Centro Neurolesi Bonino-Pulejo, Cda Casazza, SS 113, 98124 Messina, Italy
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Sakamoto D, Hamaguchi T, Kanemura N, Yasojima T, Kubota K, Suwabe R, Nakayama Y, Abo M. Feature analysis of joint motion in paralyzed and non-paralyzed upper limbs while reaching the occiput: A cross-sectional study in patients with mild hemiplegia. PLoS One 2024; 19:e0295101. [PMID: 38781257 PMCID: PMC11115294 DOI: 10.1371/journal.pone.0295101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
The reaching motion to the back of the head with the hand is an important movement for daily living. The scores of upper limb function tests used in clinical practice alone are difficult to use as a reference when planning exercises for movement improvements. This cross-sectional study aimed to clarify in patients with mild hemiplegia the kinematic characteristics of paralyzed and non-paralyzed upper limbs reaching the occiput. Ten patients with post-stroke hemiplegia who attended the Department of Rehabilitation Medicine of the Jikei University Hospital and met the eligibility criteria were included. Reaching motion to the back of the head by the participants' paralyzed and non-paralyzed upper limbs was measured using three-dimensional motion analysis, and the motor time, joint angles, and angular velocities were calculated. Repeated measures multivariate analysis of covariance was performed on these data. After confirming the fit to the binomial logistic regression model, the cutoff values were calculated using receiver operating characteristic curves. Pattern identification using random forest clustering was performed to analyze the pattern of motor time and joint angles. The cutoff values for the movement until the hand reached the back of the head were 1.6 s for the motor time, 55° for the maximum shoulder joint flexion angle, and 145° for the maximum elbow joint flexion angle. The cutoff values for the movement from the back of the head to the hand being returned to its original position were 1.6 s for the motor time, 145° for the maximum elbow joint flexion angle, 53°/s for the maximum angular velocity of shoulder joint abduction, and 62°/s for the maximum angular velocity of elbow joint flexion. The numbers of clusters were three, four, and four for the outward non-paralyzed side, outward and return paralyzed side, and return non-paralyzed side, respectively. The findings obtained by this study can be used for practice planning in patients with mild hemiplegia who aim to improve the reaching motion to the occiput.
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Affiliation(s)
- Daigo Sakamoto
- Department of Rehabilitation Medicine, The Jikei University School of Medicine Hospital, Tokyo, Japan
- Department of Rehabilitation, Graduate School of Health Science, Saitama Prefectural University, Saitama, Japan
| | - Toyohiro Hamaguchi
- Department of Rehabilitation, Graduate School of Health Science, Saitama Prefectural University, Saitama, Japan
| | - Naohiko Kanemura
- Department of Rehabilitation, Graduate School of Health Science, Saitama Prefectural University, Saitama, Japan
| | - Takashi Yasojima
- Department of Rehabilitation, Graduate School of Health Science, Saitama Prefectural University, Saitama, Japan
| | - Keisuke Kubota
- Research Development Center, Saitama Prefectural University, Saitama, Japan
| | - Ryota Suwabe
- Department of Rehabilitation Medicine, The Jikei University School of Medicine Hospital, Tokyo, Japan
| | - Yasuhide Nakayama
- Department of Rehabilitation Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Masahiro Abo
- Department of Rehabilitation Medicine, The Jikei University School of Medicine, Tokyo, Japan
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Lee G, Hayakawa Y, Watanabe T, Bonkobara Y. Shoulder Movement-Centered Measurement and Estimation Scheme for Underarm-Throwing Motions. SENSORS (BASEL, SWITZERLAND) 2024; 24:2972. [PMID: 38793826 PMCID: PMC11126128 DOI: 10.3390/s24102972] [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: 03/26/2024] [Revised: 04/28/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024]
Abstract
Underarm throwing motions are crucial in various sports, including boccia. Unlike healthy players, people with profound weakness, spasticity, athetosis, or deformity in the upper limbs may struggle or find it difficult to control their hands to hold or release a ball using their fingers at the proper timing. To help them, our study aims to understand underarm throwing motions. We start by defining the throwing intention in terms of the launch angle of a ball, which goes hand-in-hand with the timing for releasing the ball. Then, an appropriate part of the body is determined in order to estimate ball-throwing intention based on the swinging motion. Furthermore, the geometric relationship between the movements of the body part and the release angle is investigated by involving multiple subjects. Based on the confirmed correlation, a calibration-and-estimation model that considers individual differences is proposed. The proposed model consists of calibration and estimation modules. To begin, as the calibration module is performed, individual prediction states for each subject are updated online. Then, in the estimation module, the throwing intention is estimated employing the updated prediction. To verify the effectiveness of the model, extensive experiments were conducted with seven subjects. In detail, two evaluation directions were set: (1) how many balls need to be thrown in advance to achieve sufficient accuracy; and (2) whether the model can reach sufficient accuracy despite individual differences. From the evaluation tests, by throwing 20 balls in advance, the model could account for individual differences in the throwing estimation. Consequently, the effectiveness of the model was confirmed when focusing on the movements of the shoulder in the human body during underarm throwing. In the near future, we expect the model to expand the means of supporting disabled people with ball-throwing disabilities.
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Casile A, Fregna G, Boarini V, Paoluzzi C, Manfredini F, Lamberti N, Baroni A, Straudi S. Quantitative Comparison of Hand Kinematics Measured with a Markerless Commercial Head-Mounted Display and a Marker-Based Motion Capture System in Stroke Survivors. SENSORS (BASEL, SWITZERLAND) 2023; 23:7906. [PMID: 37765963 PMCID: PMC10535006 DOI: 10.3390/s23187906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/25/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
Upper-limb paresis is common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients' movements in ecological scenarios. These systems are, however, very expensive and not readily available for many rehabilitation units. Here, we explored whether the markerless hand motion capabilities of the cost-effective Oculus Quest head-mounted display could be used to provide clinically meaningful measures. A total of 14 stroke patients executed ecologically relevant upper-limb tasks in an immersive virtual environment. During task execution, we recorded their hand movements simultaneously by means of the Oculus Quest's and a marker-based motion capture system. Our results showed that the markerless estimates of the hand position and peak velocity provided by the Oculus Quest were in very close agreement with those provided by a marker-based commercial system with their regression line having a slope close to 1 (maximum distance: mean slope = 0.94 ± 0.1; peak velocity: mean slope = 1.06 ± 0.12). Furthermore, the Oculus Quest had virtually the same sensitivity as that of a commercial system in distinguishing healthy from pathological kinematic measures. The Oculus Quest was as accurate as a commercial marker-based system in measuring clinically meaningful upper-limb kinematic parameters in stroke patients.
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Affiliation(s)
- Antonino Casile
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98122 Messina, Italy
- Center of Translational Neurophysiology of Speech and Communication (CTNSC), Istituto Italiano di Tecnologia (IIT), 44121 Ferrara, Italy;
| | - Giulia Fregna
- Doctoral Program in Translational Neurosciences and Neurotechnologies, University of Ferrara, 44121 Ferrara, Italy;
| | - Vittorio Boarini
- Center of Translational Neurophysiology of Speech and Communication (CTNSC), Istituto Italiano di Tecnologia (IIT), 44121 Ferrara, Italy;
- Department of Mathematics and Computer Science, University of Ferrara, 44121 Ferrara, Italy
| | - Chiara Paoluzzi
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
| | - Fabio Manfredini
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
- Department of Neuroscience, Ferrara University Hospital, 44124 Ferrara, Italy
| | - Nicola Lamberti
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
| | - Andrea Baroni
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
- Department of Neuroscience, Ferrara University Hospital, 44124 Ferrara, Italy
| | - Sofia Straudi
- Department of Neuroscience and Rehabilitation, University of Ferrara, 44121 Ferrara, Italy; (C.P.); (N.L.); (A.B.); (S.S.)
- Department of Neuroscience, Ferrara University Hospital, 44124 Ferrara, Italy
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Razfar N, Kashef R, Mohammadi F. Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets. SENSORS (BASEL, SWITZERLAND) 2023; 23:5513. [PMID: 37420682 DOI: 10.3390/s23125513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 07/09/2023]
Abstract
Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets-the camera-based method (Vicon) and wearable sensor-based technology (Xsens)-were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.
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Affiliation(s)
- Najmeh Razfar
- Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Rasha Kashef
- Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Farah Mohammadi
- Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
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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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Schwarz A, Bhagubai MMC, Nies SHG, Held JPO, Veltink PH, Buurke JH, Luft AR. Correction to: Characterization of stroke-related upper limb motor impairments across various upper limb activities by use of kinematic core set measures. J Neuroeng Rehabil 2022; 19:70. [PMID: 35820923 PMCID: PMC9277818 DOI: 10.1186/s12984-022-01048-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2022] [Indexed: 11/10/2022] Open
Affiliation(s)
- Anne Schwarz
- Vascular Neurology and Neurorehabilitation, Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. .,Biomedical Signals and Systems (BSS), University of Twente, Enschede, The Netherlands.
| | - Miguel M C Bhagubai
- Biomedical Signals and Systems (BSS), University of Twente, Enschede, The Netherlands
| | - Saskia H G Nies
- Cereneo, Center for Neurology and Rehabilitation, Vitznau, Switzerland
| | - Jeremia P O Held
- Vascular Neurology and Neurorehabilitation, Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Peter H Veltink
- Biomedical Signals and Systems (BSS), University of Twente, Enschede, The Netherlands
| | - Jaap H Buurke
- Biomedical Signals and Systems (BSS), University of Twente, Enschede, The Netherlands.,Roessingh Research and Development B.V., Enschede, The Netherlands
| | - Andreas R Luft
- Vascular Neurology and Neurorehabilitation, Department of Neurology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Cereneo, Center for Neurology and Rehabilitation, Vitznau, Switzerland
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