Analysis of Precision and Stability of Hand Tracking with Leap Motion Sensor

In this analysis, we present results from measurements performed to determine the stability of a hand tracking system and the accuracy of the detected palm and finger’s position. Measurements were performed for the evaluation of the sensor for an application in an industrial robot-assisted assembly scenario. Human–robot interaction is a relevant topic in collaborative robotics. Intuitive and straightforward control tools for robot navigation and program flow control are essential for effective utilisation in production scenarios without unnecessary slowdowns caused by the operator. For the hand tracking and gesture-based control, it is necessary to know the sensor’s accuracy. For gesture recognition with a moving target, the sensor must provide stable tracking results. This paper evaluates the sensor’s real-world performance by measuring the localisation deviations of the hand being tracked as it moves in the workspace.

Gesture Recognition Based on Kinect v2 and Leap Motion Data Fusion

This study proposed a method for multiple motion-sensitive devices (i.e. one Kinect v2 and two Leap Motions) to integrate gesture data in Unity. Other depth cameras could replace the Kinect. The general steps in integrating gesture data for motion-sensitive devices were introduced as follows. (1) A method was proposed to recognize the fingertip from depth images for the Kinect v2. (2) Coordinates observed by three motion-sensitive devices were aligned in space in three steps. First, preliminary coordinate conversion parameters were obtained through joint calibration of the three devices. Second, two types of devices were approached to the observed value of the standard Leap Motion by the least squares method twice (i.e. one Kinect and one Leap Motion on the first round, then two Leap Motions on the second round). (3) Data of the three devices were aligned with time by using Unity while applying the data plan. On this basis, a human hand interacted with a virtual object in Unity. Experimental results demonstrated that the proposed method had a small recognition error of hand joints and realized the natural interaction between the human hand and virtual objects.

Leap motion evaluation for assessment of upper limb motor skills in Parkinson's disease

The main goal of this study is to investigate the potential of the Leap Motion Controller (LMC) for the objective assessment of motor dysfunctioning in patients with Parkinson's disease (PwPD). The most relevant clinical signs in Parkinson's Disease (PD), such as slowness of movements, frequency variation, amplitude variation, and speed, were extracted from the recorded LMC data. Data were clinically quantified using the LMC software development kit (SDK). In this study, 16 PwPD subjects and 12 control healthy subjects were involved. A neurologist assessed the subjects during the task execution, assigning them a score according to the MDS/UPDRS-Section III items. Features of motor performance from both subject groups (patients and healthy controls) were extracted with dedicated algorithms. Furthermore, to find out the significance of such features from the clinical point of view, machine learning based methods were used. Overall, our findings showed the moderate potential of LMC to extract the motor performance of PwPD.