Evidence-Based Neurorehabilitation Technology
InMotion ARM™ : clinical version of the MIT-Manus
The InMotion ARM™ Robot is evidence based, intelligent, interactive technology that is capable of continuously adapting to and challenging each patient’s ability. This allows the clinician to efficiently deliver personalized intensive sensorimotor therapy to neurologic patients.
Robotic arm with two active degrees of freedom –
- Elbow flexion/extension
- Shoulder protraction/retraction
- Shoulder internal/external rotation
The most thoroughly researched device for upper extremity neurorehabilitation
- 1000+ patients
- Large multi-site randomized controlled clinical trials
- Easy to use technology allows for high repetition
- 400-1000 reps/session
- Task specific to reduce impairments
- in the affected limb(s) focusing on improving patient’s:
- Range of Motion
- Movement Speed
- Movement Smoothness
- Easy-to-use, grab and go set up
- Direct wheel chair access
- Print patient progress reports directly from the robot
Broad clinical application shown to improve functional outcomes across the continuum of care.1
The InMotion HAND™ Robot senses patient forces and assists the patient as needed, continuously adapting to each patients abilities allowing the clinician to deliver optimum intensive sensorimotor grasp and release hand therapy.
The InMotion HAND™ is an “add-on” optional module that attaches to the InMotion ARM™ Robot.
InMotion ARM™ software
Intensive — 1024 movements per therapy session
Evidence-based treatment protocols.
Therapy protocols allowing clinicians to
Therapeutic exercise games for:
- Motor planning
- Eye-hand coordination
- Attention, visual field deficits/neglect
- Massed practice
2D Gravity compensated therapy is more effective than 3D Spatial therapy
Bionik’s modular, “gym-of-robots” systems approach to neurorehabilitation is the only system designed to optimize the use of robotics for neurorehabilitation in a manner that is consistent with the latest clinical research and neuroscience, taking into account the latest understandings on motor learning interference and motor memory consolidation. For instance, training planar and vertical (anti-gravity) movements in alternate days leads to better outcomes than training during the same session2.
By measuring patient kinematic and kinetic data objectively, Bionik’s robots have shown that for severe to moderate brain injury the effectiveness of therapy is optimized by allowing the robots to focus on reducing impairment and allowing the therapist to assist on translating the gains in impairment into function.
Quantifies upper extremity motor control and movement recovery allowing clinicians to distinguish true recovery from compensation
Establishes a baseline and measures progress to:
- Determine medical necessity
- Justify continuation of treatment based upon measurable gains
Quantifiable measures for:
- Shoulder stabilization
- Smoothness of Arm movement
- Arms ability to move against resistance
- Mean and Maximum arm speed
- Arm Reaching error
- Joint independence
Correlated with traditional assessment scales:
Fugl-meyer, Motor-Power and NIH stroke scale performance*
Maximum Shoulder Force
Optional InMotion Eval module.
Allows clinicians to measure a patient’s ability to generate maximum shoulder flexion/extension, adduction/abduction force.
Custom Designed Technology
6 degree-of-freedom force-torque sensor monolithic aluminum device containing analog and digital electronics systems.
Module attaches to the InMotion ARM™ Robot.
Performance feedback metrics
InMotion ARM™ Dimensions
Workstation: 44”(1.12m)(W) x 73” (1.85m)(D) x 45” (1.2m)(H) at lowest position. Allow 32″(.81m) for chair.
185 lbs. (83kg)
100 – 240VAC, 50/60Hz, automatic <1250VA.
* Bosecker Caittlyn MS, Dipietro Laura, Volpe BT, Krebs HI “Kinematic Robot-Based Evaluation Scales and Clinical Counterparts to Measure Upper Limb Motor Performance in Patients With Chronic Stroke” Neurorehabilitation and Neural Repair 24(1) 62-69 , 2010.
1 Robot training enhanced motor outcome in patients with stroke maintained over 3 years. Neurology. Volpe BT, Krebs HI, Hogan N, Edelsteinn L, Diels CM, Aisen ML. 1999;53:1874 –1876.
2 Krebs, H.I., et al., “Rehabilitation Robotics: Pilot Trial of a Spatial Extension for MIT-MANUS,” Journal of NeruoEngineering and Rehabilitation, Biomedcentral, 1:5 (2004)
Klein Julius, Spencer Steven J,Reinkensmeyer David J. “Breaking It Down Is Better: Haptic Decomposition of Complex Movements Aids in Robot-Assisted Motor Learning” ieee Transactions On Neural Systems And Rehabilitation Engineering, VOL. 20, NO. 3, MAY 2012
Krakauer John W, Carmichael Thomas S, Corbett Dale, Wittenberg George F, “Getting neurorehabilitation right: What Can Be Learned From Animal Models?” Neurorehabilitation and Neural Repair, published online March 30 2012
L. Dipietro, H.I. Krebs, B.T. Volpe, J. Stein, C. Bever, S.T. Mernoff, S.E. Fasoli, and N. Hogan “Learning, not Adaptation, Characterizes Stroke Motor Recovery: Evidence from Kinematic Changes Induced by Robot-Assisted Therapy in Trained and Untrained Task in the Same Workspace.” IEEE transactions on neural systems and rehabilitation engineering 2012 Jan:20(1):48-57