An Analysis of the Precision and Reliability of the Leap Motion Sensor and Its Suitability for Static and Dynamic Tracking
TLDR
The Leap Motion Controller undoubtedly represents a revolutionary input device for gesture-based human-computer interaction; however, due to its rather limited sensory space and inconsistent sampling frequency, in its current configuration it cannot currently be used as a professional tracking system.Abstract:
We present the results of an evaluation of the performance of the Leap Motion Controller with the aid of a professional, high-precision, fast motion tracking system A set of static and dynamic measurements was performed with different numbers of tracking objects and configurations For the static measurements, a plastic arm model simulating a human arm was used A set of 37 reference locations was selected to cover the controller's sensory space For the dynamic measurements, a special V-shaped tool, consisting of two tracking objects maintaining a constant distance between them, was created to simulate two human fingers In the static scenario, the standard deviation was less than 05 mm The linear correlation revealed a significant increase in the standard deviation when moving away from the controller The results of the dynamic scenario revealed the inconsistent performance of the controller, with a significant drop in accuracy for samples taken more than 250 mm above the controller's surface The Leap Motion Controller undoubtedly represents a revolutionary input device for gesture-based human-computer interaction; however, due to its rather limited sensory space and inconsistent sampling frequency, in its current configuration it cannot currently be used as a professional tracking systemread more
Citations
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Evaluation of the leap motion controller as a new contact-free pointing device.
TL;DR: A Fitts' law-based analysis of the user's performance in selection tasks with the Leap Motion Controller compared with a standard mouse device is presented, at least with regard to the selection recognition provided by the LMC.
Journal ArticleDOI
Exploiting Recurrent Neural Networks and Leap Motion Controller for the Recognition of Sign Language and Semaphoric Hand Gestures
TL;DR: In this article, an RNN is trained by using as features the angles formed by the finger bones of the human hands, acquired by a leap motion controller sensor, and the proposed method, including the effectiveness of the selected angles, was initially tested by creating a very challenging dataset composed by a large number of gestures defined by the American sign language.
Journal ArticleDOI
Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller
TL;DR: The purpose of this paper is to survey the state-of-the-art Human-Computer Interaction techniques with a focus on the special field of three-dimensional interaction, including an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition.
Journal ArticleDOI
Upper Extremity Functional Evaluation by Fugl-Meyer Assessment Scoring Using Depth-Sensing Camera in Hemiplegic Stroke Patients.
TL;DR: FMA using Kinect is a valid way to assess upper extremity function and can provide additional results for movement quality in stroke patients and may be useful in the setting of unsupervised home-based rehabilitation.
Journal ArticleDOI
Validation of the Leap Motion Controller using markered motion capture technology
TL;DR: The LMC is capable of providing data that are clinically meaningful for wrist flexion/extension, and perhaps wrist deviation, but it cannot yet return clinically meaningful data for measuring forearm pronation/supination.
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