Incorporating haptic effects into three-dimensional virtual environments to train the hemiparetic upper extremity

Abstract

Current neuroscience has identified several constructs to increase the effectiveness of upper extremity rehabilitation. One is the use of progressive, skill acquisition-oriented training. Another approach emphasizes the use of bilateral activities. Building on these principles, this paper describes the design and feasibility testing of a robotic/virtual environment system designed to train the arm of persons who have had strokes. The system provides a variety of assistance modes, scalable workspaces and hand-robot interfaces allowing persons with strokes to train multiple joints in three dimensions. The simulations utilize assistance algorithms that adjust task difficulty both online and offline in relation to subject performance. Several distinctive haptic effects have been incorporated into the simulations. An adaptive master-slave relationship between the unimpaired and impaired arm encourages active movement of the subject's hemiparetic arm during a bimanual task. Adaptive anti-gravity support and damping stabilize the arm during virtual reaching and placement tasks. An adaptive virtual spring provides assistance to complete the movement if the subject is unable to complete the task in time. Finally, haptically rendered virtual objects help to shape the movement trajectory during a virtual placement task. A proof of concept study demonstrated this system to be safe, feasible and worthy of further study.

Figures

Fig. 1

a. Glove attached to the end effector of the Haptic Master. b. For more involved subjects, a commercial resting hand splint was attached to the end-effector of the Haptic Master via ball and socket joint.

Fig. 2

a. Visual presentation of the Reach-Touch simulation. b. Velocity / Assistive Spring Force changes during one trial of Reach-Touch. Four seconds after velocity toward the target approaches zero, the assistive force is initiated. The endpoint velocity toward the target then increases until the repetition is completed. c. Percentage change in kinematic measures following arm training using the Haptic Master during the three dimensional Reach-Touch simulation

Fig. 3

a. Visual presentation of the Cup Placing simulation. b. Percentage change in kinematic measures following arm training using the Haptic Master during the Cup Placing Simulation c. Depiction of a single subject training with and without haptic effects on Day one. Dashed line is training with no added damping or gravity assist. Thick line is training with damping and gravity assist. d. Same subject after eight days of training with haptic assistance, performing cup placing with no damping or gravity assist. Note the up and over trajectory comparable to a real world placing task.

Fig. 4

a. Trajectories produced during five repetitions of Catching Falling Objects simulation demonstrated by a representative subject. The unimpaired arm position (left) was measured by an electromagnetic sensor. The impaired arm position (right) was measured by the Haptic Master. The goal is to touch the target object that falls along the arrow line simultaneously with two hands. Note similar but not identical trajectories and a lack of interaction between the two effectors. b. Relationship between differences in vertical position of motion sensor and Haptic Master endpoint. The Haptic Master's assistive spring exerts greater forces on the hemiplegic arm as it lags further behind the unimpaired arm. c. Daily force production in the hemiparetic arm over the course of the training (increased active force indicates less assistance by the robotic arm). d. Active force exerted by hemiplegic upper extremity of four subjects during a bilateral simulated activity before and after 8 days of training.