Performance evaluation of 3D reaching tasks using a low-cost haptic device and virtual reality

In this paper we propose a new protocol based on Virtual Reality and a low-cost haptic device for evaluating motion performance during perturbed 3D reaching tasks. The protocol presented herein was designed to assess how different force amplitudes and different reaching directions influence motor performance of healthy subjects. We developed a novel gaming scenario using Unity 3D and the Novint Falcon, a low-cost haptic joystick. The protocol consisted of six 3D point-to-point reaching tasks, which were performed by means of the Falcon while six different force fields were applied. Five subjects were enrolled in the study. During each task, subjects were asked to reach 80 targets. The trajectories of the end-effector, during each task, were recorded to calculate the following kinematic indices: duration of movement, length ratio, lateral deviation, aiming angle, speed metric and normalized jerk. The coefficient of variation was calculated to study the intra-subject variability to establish which indices better assessed the accuracy and the smoothness of the trajectories. Subsequently, two-way repeated measurement ANOVA tests were performed for all indices in order to ascertain effects of the 6 levels of force and the 8 directions of the reaching task. Length ratio and speed metric have proven the highest intra-subject repeatability as accuracy and smoothness indices, respectively. Statistical analysis demonstrated that all the accuracy indices are not sensitive to amplitude variation of the applied force field, nor to different target directions. Conversely, the smoothness indices showed statistical differences in both forces and directions. In particular, the speed metric is sensitive to the applied force, and the normalized jerk depends on the target directions.

[1]  M. Walker,et al.  Virtual reality system for upper extremity rehabilitation of chronic stroke patients living in the community , 2008 .

[2]  Carlos Vaz de Carvalho,et al.  Serious gaming for experiential learning , 2011, 2011 Frontiers in Education Conference (FIE).

[3]  R. Scheidt,et al.  Reach adaptation and final position control amid environmental uncertainty after stroke. , 2007, Journal of neurophysiology.

[4]  Antonio Frisoli,et al.  A 3-RSR Haptic Wearable Device for Rendering Fingertip Contact Forces , 2017, IEEE Transactions on Haptics.

[5]  Oded Nov,et al.  Can Force Feedback and Science Learning Enhance the Effectiveness of Neuro-Rehabilitation? An Experimental Study on Using a Low-Cost 3D Joystick and a Virtual Visit to a Zoo , 2013, PloS one.

[6]  G. Stelmach,et al.  Parkinsonism Reduces Coordination of Fingers, Wrist, and Arm in Fine Motor Control , 1997, Experimental Neurology.

[7]  Steven Martin,et al.  Characterisation of the Novint Falcon Haptic Device for Application as a Robot Manipulator , 2009 .

[8]  Antonio Frisoli,et al.  A Fingertip Haptic Display for Improving Curvature Discrimination , 2008, PRESENCE: Teleoperators and Virtual Environments.

[9]  Stefano Rossi,et al.  Robotic and clinical evaluation of upper limb motor performance in patients with Friedreich’s Ataxia: an observational study , 2015, Journal of NeuroEngineering and Rehabilitation.

[10]  Xavier Rodet,et al.  Study of haptic and visual interaction for sound and music control in the phase project , 2005 .

[11]  M. Hallett,et al.  Virtual Reality–Induced Cortical Reorganization and Associated Locomotor Recovery in Chronic Stroke: An Experimenter-Blind Randomized Study , 2005, Stroke.

[12]  Antoine Ferreira,et al.  Virtual reality and haptics for nanorobotics , 2006, IEEE Robotics & Automation Magazine.

[13]  Antonio Frisoli,et al.  Integration of serious games and wearable haptic interfaces for Neuro Rehabilitation of children with movement disorders: A feasibility study , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

[14]  Yuan-Shin Lee,et al.  Haptic-based Virtual Environment Design and Modeling of Motor Skill Assessment for Brain Injury PatientsRehabilitation , 2011 .

[15]  Alejandro Jarillo Silva,et al.  PHANToM OMNI Haptic Device: Kinematic and Manipulability , 2009, 2009 Electronics, Robotics and Automotive Mechanics Conference (CERMA).

[16]  Susan E Palsbo,et al.  Towards a modified consumer haptic device for robotic-assisted fine-motor repetitive motion training , 2011, Disability and rehabilitation. Assistive technology.

[17]  Stefano Rossi,et al.  Quantification of Age-Related Differences in Reaching and Circle-Drawing using a Robotic Rehabilitation Device , 2014 .