Hand rehabilitation - a gaming experience

Panaite, Arun Fabian; Rîsteiu, Marius; Olar, Marius Leonard; Leba, Monica

doi:10.1088/1757-899x/572/1/012092

Cited by 11 publications

(2 citation statements)

References 8 publications

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“…Wearable devices for motion capture have proliferated incredibly during the last few years [7,8] yet, in the future years may become obsolete, as non-invasive technologies are on the rise [9]. Gait (or other motion analysis) is well-established [10] in clinical assessment of movement disabilities.…”

Section: State Of the Artmentioning

confidence: 99%

Human Arm Motion Capture using Gyroscopic Sensors

et al. 2021

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By using the most rudimentary microcontroller chips, that receive data from sensors, and transmit the data to a computer system, thorough a virtual serial port, motion of many objects, bodies and joints can be captured. Capturing the motion and reproducing it live is not the only destination for the data usage. Recording and studying the motion data, can reduce a lot of work in a wide range of domains. Using the simplest methods to capture the data, also means making it so widely accessible for learning, editing and also developing systems that use very little processing power, granting data access for the less efficient computers. We propose using the MPU-6050 MEMS sensor in a dual instance, and the Arduino UNO microcontroller, connected to a computer for data acquisition, to capture the motion of a human arm, and reproduce it in a projected environment. Other experiments, conducted by other researchers and developers have used a higher number of sensors, and the data acquisition and recording systems were much more complex, but our research reduced the number of sensors to just two. One of the high impact innovations brought by this system, in particular, is that we’ve virtually hooked the end of one sensor to the tip of the other, creating a virtual motion chain.

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Section: State Of the Artmentioning

confidence: 99%

Human Arm Motion Capture using Gyroscopic Sensors

et al. 2021

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“…These simple sensors can be attached anywhere on either the hand or a glove covering the hand in order to know the position of the fingers [4] or the turns of the wrist [5], [6]. The corresponding data are taken and then processed for both prosthetics or rehabilitation purposes [7].…”

Section: Introductionmentioning

confidence: 99%

Optical System Based on Multiplexed FBGs to Monitor Hand Movements

et al. 2021

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This contribution reports the development and characterization of an optical system based on parallel Fiber Bragg Gratings (FBGs) to monitor the movements of the wrist and fingers of a hand. The system consisted of a reflective configuration made of FBGs detecting the movements of the fingers and one more located on the wrist as a reference. All FBGs were multiplexed in order to collect the basic movements of the hand. Fibers were embedded in polydimethylsiloxane for protection and to give flexibility to the optical detection setup. Measurements of strain, angle and torsion were performed during the experiments, obtaining sensitivities up to 1.29 pm/µε in strain and 64.23 pm/º in angle. Also, a study on the influence of a single sensor on the performance of the whole system was analyzed for a complete study of this proof of concept. The obtained results present a simple system that can be used to monitor the positions of the hand or for the rehabilitation of patients suffering from neuromotor or post-stroke diseases.

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Right Arm Exoskeleton for Mobility Impaired

Risteiu

Leba

2020

Trends and Innovations in Information Systems and Technologies

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Hand rehabilitation - a gaming experience

Cited by 11 publications

References 8 publications

Human Arm Motion Capture using Gyroscopic Sensors

Human Arm Motion Capture using Gyroscopic Sensors

Optical System Based on Multiplexed FBGs to Monitor Hand Movements

Right Arm Exoskeleton for Mobility Impaired

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