A low cost instrumented glove for extended monitoring and functional hand assessment

Simone, Lisa K.; Sundarrajan, Nappinnai; Luo, Xun; Jia, Yicheng; Kamper, Derek G.

doi:10.1016/j.jneumeth.2006.09.021

Cited by 129 publications

(111 citation statements)

References 27 publications

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“…We can stress how some measures take time to become. In [6] this was supposed to be due, in an unknown percentage, both to subject's relaxation after gripping the mold and to a time-varying creep in flex sensors when held in a fixed bent position. Because of the attention we paid to train the subject to do not use any gripping force, we believe the time-to-stability was mostly due just to the sensors.…”

Section: Resultsmentioning

confidence: 99%

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LOVETT Scaling with Flex Sensor and MYO Armband for Monitoring Finger Muscles Therapy of Post-Stroke People

Hidayat¹,

Arief²,

Happyanto³

2016

emitter

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LOVETT scale is a common parameter used by the doctor or therapist to determine the muscle strength of the patient's hands, especially patients with post-stroke. As a result of previous work of our group, a sensory glove for monitoring finger muscle therapy for post-stroke people with the name of Electronic Therapy Gloves (ETG) was proposed. With the flex sensor that embedded to the gloves we can measure the LOVETT scale of the post-stroke people. This sensory glove can help the patient doing their rehabilitation fast so that they don't have to go to the hospital every week to check up their progress. In this work, we combine the data of sensory glove and the MYO armband for LOVETT scaling that has never been done before. The output of the Electronic Therapy Gloves can be optimized by 25%. All the LOVETT grade can be identify by the gloves, then it can help the doctor monitor the patient's rehabilitation just by looking the patient's record data with ETG.

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Section: Resultsmentioning

confidence: 99%

“…Later expanded by Dipietro et al [3]. Afterwards used by Simone et al [6]. Gentner and Classen [4] and Li et al [5].…”

Section: Methods and Test Protocolmentioning

confidence: 99%

LOVETT Scaling with Flex Sensor and MYO Armband for Monitoring Finger Muscles Therapy of Post-Stroke People

Hidayat¹,

Arief²,

Happyanto³

2016

emitter

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“…Schettino et al [22] studied hand preshaping before an object is grasped and the effects on limited vision on the modulation of hand kinematics to adjust to object contours. A prevalent and accurate way for obtaining hand pose and articulated motion is the use of data gloves [24]. Data gloves, however, are uncomfortable and prohibitively expensive making their use in everyday activities unappealing.…”

Section: Related Workmentioning

confidence: 99%

Egocentric Object Recognition Leveraging the 3D Shape of the Grasping Hand

Lin

Hua

Mordohai

2015

Computer Vision - ECCV 2014 Workshops

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Abstract. We present a systematic study on the relationship between the 3D shape of a hand that is about to grasp an object and recognition of the object to be grasped. In this paper, we investigate the direction from the shape of the hand to object recognition for unimpaired users. Our work shows that the 3D shape of a grasping hand from an egocentric point of view can help improve recognition of the objects being grasped. Previous work has attempted to exploit hand interactions or gaze information in the egocentric setting to guide object segmentation. However, all such analyses are conducted in 2D. We hypothesize that the 3D shape of a grasping hand is highly correlated to the physical attributes of the object being grasped. Hence, it can provide very beneficial visual information for object recognition. We validate this hypothesis by first building a 3D, egocentric vision pipeline to segment and reconstruct dense 3D point clouds of the grasping hands. Then, visual descriptors are extracted from the point cloud and subsequently fed into an object recognition system to recognize the object being grasped. Our experiments demonstrate that the 3D hand shape can indeed greatly help improve the visual recognition accuracy, when compared with the baseline where only 2D image features are utilized.

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“…By contrast, in academia, most gloves, such as the Wü-Glove [4], the Shadow Monitor Glove [5], and the Sigma Glove [6], have been developed to assess hand function in clinical and rehabilitation settings.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of bend sensor properties as applied in a glove for use in neurorehabilitation settings

Oess

Wanek

Hedel

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Following hand function impairment caused by a neurological disorder, the functional level of the upper extremities has to be assessed in the clinical and rehabilitation settings. Current hand function evaluation tests are somewhat imprecise. Instrumented gloves allow finger motion monitoring during the performance of skilled tasks, such as grasping objects. As a result, they provide an objective tool for evaluating slight changes in the fine motor skills of the hand. Numerous gloves are based on resistive bend sensors, given that this is an easy to handle, low-cost, and reliable sensing element. When bending is not applied homogeneously along such a sensor, as is the case with finger-joint bending, its output response varies with the sensor's longitudinal position. Our goal is to determine the optimal sensor position with respect to the finger-joint in order to enhance the resolution of the sensors embedded in a glove. The validity of the integrated sensors is evaluated and the accuracy values are given. Enhancement of Bend Sensor Properties as Applied in a Glove for Use in Neurorehabilitation SettingsNinja P. Oess, Johann Wanek, and Hubertus J. A. van HedelAbstract-Following hand function impairment caused by a neurological disorder, the functional level of the upper extremities has to be assessed in the clinical and rehabilitation settings. Current hand function evaluation tests are somewhat imprecise. Instrumented gloves allow finger motion monitoring during the performance of skilled tasks, such as grasping objects. As a result, they provide an objective tool for evaluating slight changes in the fine motor skills of the hand. Numerous gloves are based on resistive bend sensors, given that this is an easy to handle, low-cost, and reliable sensing element. When bending is not applied homogeneously along such a sensor, as is the case with finger-joint bending, its output response varies with the sensor's longitudinal position. Our goal is to determine the optimal sensor position with respect to the finger-joint in order to enhance the resolution of the sensors embedded in a glove. The validity of the integrated sensors is evaluated and the accuracy values are given.

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A low cost instrumented glove for extended monitoring and functional hand assessment

Cited by 129 publications

References 27 publications

LOVETT Scaling with Flex Sensor and MYO Armband for Monitoring Finger Muscles Therapy of Post-Stroke People

LOVETT Scaling with Flex Sensor and MYO Armband for Monitoring Finger Muscles Therapy of Post-Stroke People

Egocentric Object Recognition Leveraging the 3D Shape of the Grasping Hand

Enhancement of bend sensor properties as applied in a glove for use in neurorehabilitation settings

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