title: 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.
authors.affiliation: Department of Rehabilitation and Movement Sciences, University of Medicine and Dentistry of New Jersey, Newark, NJ, USA. merians@umdnj.edu
authors.forename: Alma S
authors.initials: AS
authors.lastname: Merians
authors.forename: Gerard G
authors.initials: GG
authors.lastname: Fluet
authors.forename: Qinyin
authors.initials: Q
authors.lastname: Qiu
authors.forename: Soha
authors.initials: S
authors.lastname: Saleh
authors.forename: Ian
authors.initials: I
authors.lastname: Lafond
authors.forename: Amy
authors.initials: A
authors.lastname: Davidow
authors.forename: Sergei V
authors.initials: SV
authors.lastname: Adamovich
mesh_terms: D000328:Adult; D000368:Aged; D001132:Arm / Q000502:physiology*; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D005385:Fingers / Q000502:physiology*; D006225:Hand / Q000502:physiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D010291:Paresis / Q000209:etiology / Q000534:rehabilitation*; D012371:Robotics; D020521:Stroke / Q000150:complications; D000071939:Stroke Rehabilitation; D016896:Treatment Outcome; D014584:User-Computer Interface; D018910:Video Games