Robotically facilitated virtual rehabilitation of arm transport integrated with finger movement in persons with hemiparesis

Abstract

Background: Recovery of upper extremity function is particularly recalcitrant to successful rehabilitation. Robotic-assisted arm training devices integrated with virtual targets or complex virtual reality gaming simulations are being developed to deal with this problem. Neural control mechanisms indicate that reaching and hand-object manipulation are interdependent, suggesting that training on tasks requiring coordinated effort of both the upper arm and hand may be a more effective method for improving recovery of real world function. However, most robotic therapies have focused on training the proximal, rather than distal effectors of the upper extremity. This paper describes the effects of robotically-assisted, integrated upper extremity training.

Methods: Twelve subjects post-stroke were trained for eight days on four upper extremity gaming simulations using adaptive robots during 2-3 hour sessions.

Results: The subjects demonstrated improved proximal stability, smoothness and efficiency of the movement path. This was in concert with improvement in the distal kinematic measures of finger individuation and improved speed. Importantly, these changes were accompanied by a robust 16-second decrease in overall time in the Wolf Motor Function Test and a 24-second decrease in the Jebsen Test of Hand Function.

Conclusions: Complex gaming simulations interfaced with adaptive robots requiring integrated control of shoulder, elbow, forearm, wrist and finger movements appear to have a substantial effect on improving hemiparetic hand function. We believe that the magnitude of the changes and the stability of the patient's function prior to training, along with maintenance of several aspects of the gains demonstrated at retention make a compelling argument for this approach to training.

Figures

Figure 1

Simulations. Screen shots for simulations utilized during this training study a. Plasma Pong, b. Hummingbird Hunt, c. Hammer Task, d. Virtual Piano. e. Training setup.

Figure 2

Piano trainer kinematic analyses. a. Daily averages during Virtual Piano training for finger fractionation defined as the difference between the angle of the MCP joint of the cued finger and of the most flexed non-cued finger. Higher scores indicate better performance. Averages for 10 subjects are shown (two subjects who used the CyberGrasp haptic device during virtual piano training are not included in this analysis). 2b. Daily averages for all 12 subjects in the time to press each key during piano training. 2c.Daily averages of number of correct keys pressed divided by total keys pressed for all 12 subjects. Error bars = Standard Error of the Mean.

Figure 3

Hammer simulation kinematic analyses. Daily average for all twelve subjects during Hammer Task training in a. the length of the path required to complete ten targets. b. time required to hammer each virtual cylinder c. in hand trajectory smoothness quantified as normalized integrated jerk (values are dimensionless, lower scores indicate smoother path with fewer subunits). d. peak finger extension. 3e. hand deviation calculated as the cumulative excursion of the hand position in 3D space from the center of the target starting at the time target is acquired until completion of hammering (lower scores indicate more stability). 3f. Individual subjects start measure (average of first two training days), and end measure (average of last two training days) for all twelve subjects in average hand deviation during hammer task training. Error bars = Standard Error of the Mean.

Figure 4

Jebsen test of hand function comparison. The composite time for the Jebsen Test of Hand Function at three testing points for the 12 subjects with strokes (JTHF1 = Pre-test, JTHF2 = Post-test, JTHF3 = Retention, Impaired Hand = open circles, Unimpaired Hand = solid circles), and the seven aged matched controls (Non-Dominant Hand = open triangles, Dominant hand = solid triangles). Error bars = Standard Error of the Mean.