Validation of the Leap Motion Controller using markered motion capture technology

Smeragliuolo, Anna H.; Hill, N. Jeremy; Disla, Luis; Putrino, David

doi:10.1016/j.jbiomech.2016.04.006

Cited by 101 publications

(62 citation statements)

References 24 publications

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“…It's dual platform (Macintosh/Windows), connects to a computer via USB 3.0 connection. It streams data at a variable acquisition rate that varies between mean value of less than 40 Hz [24] to up to 120 Hz [25], under both static and dynamic conditions [24]. It has a full-functioning Software Developer Kit (SDK) that allows to rebuild the 3D scene, and to track the positions of the hands and fingers.…”

Section: B Leap Motion Controllermentioning

confidence: 99%

“…The activated sensor sends data on position of hands and finger during movements. The millimeter accuracy of the LMC when the object is placed bellow 250 mm distance from the sensors was already demonstrated [23,24,25]. To improve the repeatability of the system detection the participants were informed about the optimal position of the hands above the LMC device.…”

Section: B Leap Motion Controllermentioning

confidence: 99%

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Tailored virtual reality and mobile application for motor rehabilitation

Lourenço

Postolache

2018

2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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The present work presents a measurement system and methodology for hand and finger motor rehabilitation. The interaction with serious games developed in Unity 3D game engine is performed using a natural user interface based on Leap Motion Controller. The storage and management of data related to patient identification, established training plans and training results in LeaPhysio system, is realized in a client server architecture. The stored information can be accessed through a developed LeaPhysio App for Android OS platform, which also allows configuration of training plan by a therapist. Different metrics were included in the measurement system to provide to users the possibility to evaluate in an objective way the motor rehabilitation. The tests have shown that the developed system can provide accurate data on hand and finger movements in a meaningful and motivating exercise environment.

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Section: B Leap Motion Controllermentioning

confidence: 99%

Section: B Leap Motion Controllermentioning

confidence: 99%

Tailored virtual reality and mobile application for motor rehabilitation

Lourenço

Postolache

2018

2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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“…Therefore, it is inevitable that gesture recognition by 2D image is replaced by 3D with depth information. In general, 3D information can be acquired by binocular cameras [17], Kinect sensor [18][19][20], Leap Motion [21], and other devices. Those devices can be usually utilized to obtain depth information by spatial relationship of different direction [17] or infrared reflection [22], which can conveniently acquire noncontact image for recognition and classification instead of wearing the complicated equipment.…”

Section: Introductionmentioning

confidence: 99%

Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information

Peng

2018

Journal of Sensors

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Gesture recognition is an important part of human-robot interaction. In order to achieve fast and stable gesture recognition in real time without distance restrictions, this paper presents an improved threshold segmentation method. The improved method combines the depth information and color information of a target scene with hand position by the spatial hierarchical scanning method; the ROI in the scene is thus extracted by the local neighbor method. In this way, the hand can be identified quickly and accurately in complex scenes and different distances. Furthermore, the convex hull detection algorithm is used to identify the positioning of fingertips in ROI, so that the fingertips can be identified and located accurately. The experimental results show that the hand position can be obtained quickly and accurately in the complex background by using the improved method, the real-time recognition distance interval can be reached by 0.5 m to 2.0 m, and the fingertip detection rates can be reached 98.5% in average. Moreover, the gesture recognition rates are more than 96% by the convex hull detection algorithm. It can be thus concluded that the proposed method achieves good performance of hand detection and positioning at different distances.

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“…This behavior is recognized by it and analyzes the AR system. For this purpose, markers [6,7], 9 DOF sensors [8] or camera systems including 3D cameras [9,10] are used. In the augmented reality system that is based on tags, the player is "stuck" with markers and then observed by an optical system consisting of multiple cameras.…”

Section: Introductionmentioning

confidence: 99%

Optical Driving for a Computer System with Augmented Reality Features

Pałys

Murawski

Arciuch

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-This article proposes a laser beam encoding method that is used to control an augmented reality system. Experiments were performed using a red laser emitting a wavelength of λ = 650 nm and a power of P = 3 mW. The purpose of the study was to investigate the methods of modulation and demodulation of the encoded laser signal, and to examine the influence of parameters such as laser pulse duration, camera image resolution, the number of recorded frames per second on the demodulation result of the optical signal.The results show that the proposed coding method provides the transmission of the necessary information in a single laser beam (no less than 36 codes with a decoding efficiency of 99.9%). The developed coding method enables, based on the sequence analysis of video images, the influence on the course of the simulation performed in augmented reality, including distinguishing players and actions taken by them. This is an important advancement in relation to interaction systems used to influence augmented reality.

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Validation of the Leap Motion Controller using markered motion capture technology

Cited by 101 publications

References 24 publications

Tailored virtual reality and mobile application for motor rehabilitation

Tailored virtual reality and mobile application for motor rehabilitation

Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information

Optical Driving for a Computer System with Augmented Reality Features

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