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      Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation

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          Abstract

          Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, can be really effective when used in virtual reality (VR) environments. Mechanical devices are often expensive, cumbersome, patient specific and hand specific, while tracking-based devices are not affected by these limitations but, especially if based on a single tracking sensor, could suffer from occlusions. In this paper, the implementation of a multi-sensors approach, the Virtual Glove (VG), based on the simultaneous use of two orthogonal LEAP motion controllers, is described. The VG is calibrated and static positioning measurements are compared with those collected with an accurate spatial positioning system. The positioning error is lower than 6 mm in a cylindrical region of interest of radius 10 cm and height 21 cm. Real-time hand tracking measurements are also performed, analysed and reported. Hand tracking measurements show that VG operated in real-time (60 fps), reduced occlusions, and managed two LEAP sensors correctly, without any temporal and spatial discontinuity when skipping from one sensor to the other. A video demonstrating the good performance of VG is also collected and presented in the Supplementary Materials. Results are promising but further work must be done to allow the calculation of the forces exerted by each finger when constrained by mechanical tools (e.g., peg-boards) and for reducing occlusions when grasping these tools. Although the VG is proposed for rehabilitation purposes, it could also be used for tele-operation of tools and robots, and for other VR applications.

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          Most cited references25

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          A survey on robotic devices for upper limb rehabilitation

          The existing shortage of therapists and caregivers assisting physically disabled individuals at home is expected to increase and become serious problem in the near future. The patient population needing physical rehabilitation of the upper extremity is also constantly increasing. Robotic devices have the potential to address this problem as noted by the results of recent research studies. However, the availability of these devices in clinical settings is limited, leaving plenty of room for improvement. The purpose of this paper is to document a review of robotic devices for upper limb rehabilitation including those in developing phase in order to provide a comprehensive reference about existing solutions and facilitate the development of new and improved devices. In particular the following issues are discussed: application field, target group, type of assistance, mechanical design, control strategy and clinical evaluation. This paper also includes a comprehensive, tabulated comparison of technical solutions implemented in various systems.
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            Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians.

            Approximately two thirds of stroke survivors continue to experience motor deficits of the arm resulting in diminished quality of life. Conventional rehabilitation provides modest and sometimes delayed effects. Virtual reality (VR) technology is a novel adjunctive therapy that could be applied in neurorehabilitation. We performed a meta-analysis to determine the added benefit of VR technology on arm motor recovery after stroke. We searched Medline, EMBASE, and Cochrane literature from 1966 to July 2010 with the terms "stroke," "virtual reality," and "upper arm/extremity." We evaluated the effect of VR on motor function improvement after stroke. From the 35 studies identified, 12 met the inclusion/exclusion criteria totaling 195 participants. Among them, there were 5 randomized clinical trials and 7 observational studies with a pre-/postintervention design. Interventions were delivered within 4 to 6 weeks in 9 of the studies and within 2 to 3 weeks in the remaining 3. Eleven of 12 studies showed a significant benefit toward VR for the selected outcomes. In the pooled analysis of all 5 randomized controlled trials, the effect of VR on motor impairment (Fugl-Meyer) was OR=4.89 (95% CI, 1.31 to 18.3). No significant difference was observed for Box and Block Test or motor function. Among observational studies, there was a 14.7% (95% CI, 8.7%-23.6%) improvement in motor impairment and a 20.1% (95% CI, 11.0%-33.8%) improvement in motor function after VR. VR and video game applications are novel and potentially useful technologies that can be combined with conventional rehabilitation for upper arm improvement after stroke.
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              Analysis of the Accuracy and Robustness of the Leap Motion Controller

              The Leap Motion Controller is a new device for hand gesture controlled user interfaces with declared sub-millimeter accuracy. However, up to this point its capabilities in real environments have not been analyzed. Therefore, this paper presents a first study of a Leap Motion Controller. The main focus of attention is on the evaluation of the accuracy and repeatability. For an appropriate evaluation, a novel experimental setup was developed making use of an industrial robot with a reference pen allowing a position accuracy of 0.2 mm. Thereby, a deviation between a desired 3D position and the average measured positions below 0.2 mm has been obtained for static setups and of 1.2 mm for dynamic setups. Using the conclusion of this analysis can improve the development of applications for the Leap Motion controller in the field of Human-Computer Interaction.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                10 March 2018
                March 2018
                : 18
                : 3
                : 834
                Affiliations
                [1 ]A 2VI_Lab, Department of Life, Health & Environmental Sciences, University of L’Aquila, Via Vetoio 1, 67100 Coppito, L’Aquila, Italy; matteo.polsinelli@ 123456student.univaq.it (M.P.); matteo.spezialetti@ 123456graduate.univaq.it (M.S.)
                [2 ]Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00198, Italy; cinque@ 123456di.uniroma1.it
                Author notes
                [* ]Correspondence: giuseppe.placidi@ 123456univaq.it ; Tel.: +39-0862-433493
                Author information
                https://orcid.org/0000-0002-4790-4029
                Article
                sensors-18-00834
                10.3390/s18030834
                5877317
                29534448
                405548f1-f495-4304-9b3d-70371ae45c34
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 06 February 2018
                : 09 March 2018
                Categories
                Article

                Biomedical engineering
                virtual glove,leap motion controller,hand tracking,hand rehabilitation
                Biomedical engineering
                virtual glove, leap motion controller, hand tracking, hand rehabilitation

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