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      Design of an Inertial-Sensor-Based Data Glove for Hand Function Evaluation

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          Abstract

          Capturing hand motions for hand function evaluations is essential in the medical field. Various data gloves have been developed for rehabilitation and manual dexterity assessments. This study proposed a modular data glove with 9-axis inertial measurement units (IMUs) to obtain static and dynamic parameters during hand function evaluation. A sensor fusion algorithm is used to calculate the range of motion of joints. The data glove is designed to have low cost, easy wearability, and high reliability. Owing to the modular design, the IMU board is independent and extensible and can be used with various microcontrollers to realize more medical applications. This design greatly enhances the stability and maintainability of the glove.

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          Most cited references26

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          Interactive wearable systems for upper body rehabilitation: a systematic review

          Background The development of interactive rehabilitation technologies which rely on wearable-sensing for upper body rehabilitation is attracting increasing research interest. This paper reviews related research with the aim: 1) To inventory and classify interactive wearable systems for movement and posture monitoring during upper body rehabilitation, regarding the sensing technology, system measurements and feedback conditions; 2) To gauge the wearability of the wearable systems; 3) To inventory the availability of clinical evidence supporting the effectiveness of related technologies. Method A systematic literature search was conducted in the following search engines: PubMed, ACM, Scopus and IEEE (January 2010–April 2016). Results Forty-five papers were included and discussed in a new cuboid taxonomy which consists of 3 dimensions: sensing technology, feedback modalities and system measurements. Wearable sensor systems were developed for persons in: 1) Neuro-rehabilitation: stroke (n = 21), spinal cord injury (n = 1), cerebral palsy (n = 2), Alzheimer (n = 1); 2) Musculoskeletal impairment: ligament rehabilitation (n = 1), arthritis (n = 1), frozen shoulder (n = 1), bones trauma (n = 1); 3) Others: chronic pulmonary obstructive disease (n = 1), chronic pain rehabilitation (n = 1) and other general rehabilitation (n = 14). Accelerometers and inertial measurement units (IMU) are the most frequently used technologies (84% of the papers). They are mostly used in multiple sensor configurations to measure upper limb kinematics and/or trunk posture. Sensors are placed mostly on the trunk, upper arm, the forearm, the wrist, and the finger. Typically sensors are attachable rather than embedded in wearable devices and garments; although studies that embed and integrate sensors are increasing in the last 4 years. 16 studies applied knowledge of result (KR) feedback, 14 studies applied knowledge of performance (KP) feedback and 15 studies applied both in various modalities. 16 studies have conducted their evaluation with patients and reported usability tests, while only three of them conducted clinical trials including one randomized clinical trial. Conclusions This review has shown that wearable systems are used mostly for the monitoring and provision of feedback on posture and upper extremity movements in stroke rehabilitation. The results indicated that accelerometers and IMUs are the most frequently used sensors, in most cases attached to the body through ad hoc contraptions for the purpose of improving range of motion and movement performance during upper body rehabilitation. Systems featuring sensors embedded in wearable appliances or garments are only beginning to emerge. Similarly, clinical evaluations are scarce and are further needed to provide evidence on effectiveness and pave the path towards implementation in clinical settings.
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            Vision-based hand pose estimation: A review

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              Assessment of hand kinematics using inertial and magnetic sensors

              Background Assessment of hand kinematics is important when evaluating hand functioning. Major drawbacks of current sensing glove systems are lack of rotational observability in particular directions, labour intensive calibration methods which are sensitive to wear and lack of an absolute hand orientation estimate. Methods We propose an ambulatory system using inertial sensors that can be placed on the hand, fingers and thumb. It allows a full 3D reconstruction of all finger and thumb joints as well as the absolute orientation of the hand. The system was experimentally evaluated for the static accuracy, dynamic range and repeatability. Results The RMS position norm difference of the fingertip compared to an optical system was 5±0.5 mm (mean ± standard deviation) for flexion-extension and 12.4±3.0 mm for combined flexion-extension abduction-adduction movements of the index finger. The difference between index and thumb tips during a pinching movement was 6.5±2.1 mm. The dynamic range of the sensing system and filter was adequate to reconstruct full 80 degrees movements of the index finger performed at 116 times per minute, which was limited by the range of the gyroscope. Finally, the reliability study showed a mean range difference over five subjects of 1.1±0.4 degrees for a flat hand test and 1.8±0.6 degrees for a plastic mold clenching test, which is smaller than other reported data gloves. Conclusion Compared to existing data gloves, this research showed that inertial and magnetic sensors are of interest for ambulatory analysis of the human hand and finger kinematics in terms of static accuracy, dynamic range and repeatability. It allows for estimation of multi-degree of freedom joint movements using low-cost sensors.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                13 May 2018
                May 2018
                : 18
                : 5
                : 1545
                Affiliations
                [1 ]Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan; bslin@ 123456mail.ntpu.edu.tw (B.-S.L.); akino_sumiko@ 123456hotmail.com (I.-J.L.)
                [2 ]College of Electrical Engineering and Computer Science, National Taipei University, New Taipei City 23741, Taiwan
                [3 ]Department of Physical Medicine and Rehabilitation, Chi-Mei Medical Center, Tainan 71004, Taiwan; yangshuyu1970@ 123456gmail.com
                [4 ]Department of Electrical Engineering, Yuan Ze University, Taoyuan City 32003, Taiwan; alexlo0703@ 123456gmail.com (Y.-C.L.); eejlee@ 123456saturn.yzu.edu.tw (J.L.)
                [5 ]Department of Physical Medicine & Rehabilitation, Taoyuan Chang Gung Memorial Hospital, Taoyuan City 333, Taiwan
                [6 ]Center for Healthy and Aging Research, Chang Gung University, Taoyuan City 33302, Taiwan
                [7 ]School of Medicine, Medical College, Chang Gung University, Taoyuan City 33302, Taiwan
                Author notes
                [* ]Correspondence: bigmac1479@ 123456gmail.com ; Tel.: +886-3-319-6200 (ext. 2378); Fax: +886-3-319-3700
                Author information
                https://orcid.org/0000-0003-0498-3190
                https://orcid.org/0000-0002-3217-4668
                Article
                sensors-18-01545
                10.3390/s18051545
                5982580
                29757261
                c79c5bb8-6911-4228-ba30-d1739fcfe596
                © 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
                : 27 March 2018
                : 10 May 2018
                Categories
                Article

                Biomedical engineering
                motion capture,data glove,inertial sensor,joint measurement,rehabilitation
                Biomedical engineering
                motion capture, data glove, inertial sensor, joint measurement, rehabilitation

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