A Biomechanical Model to Aid Robot-Assisted Therapy of Upper Limb Impairment

This paper describes the design, and application of a dynamic bio-mechanical model for assessing and monitoring trajectory, position, orientation, force and torque generated by movements of the whole upper limb during a robot-assisted therapy. The individualised segment inertial parameters are based on anthropometric measurements. The model performs inverse dynamic analysis of the recorded arm's movements to calculate reaction forces and moments acting about the 3-DOF shoulder and 2-DOF elbow joints. A real time fused biofeedback of a 6-DOF force sensor and 3D pose sensors support the model. The model input and output parameters are stored in the patients' database, which is a part of the rehabilitation information system. Our experimental results confirm the nature of the treatment exercise taught to the robotic systems

[1]  D. M. Feeney,et al.  Amphetamine, haloperidol, and experience interact to affect rate of recovery after motor cortex injury. , 1982, Science.

[2]  D. Khalili,et al.  An intelligent robotic system for rehabilitation of joints and estimation of body segment parameters , 1988, IEEE Transactions on Biomedical Engineering.

[3]  F.C.T. van der Helm,et al.  A finite element musculoskeletal model of the shoulder mechanism. , 1994 .

[4]  W. Rymer,et al.  Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide. , 2014, Journal of rehabilitation research and development.

[5]  D Karlsson,et al.  Towards a model for force predictions in the human shoulder. , 1992, Journal of biomechanics.

[6]  John J. Craig Zhu,et al.  Introduction to robotics mechanics and control , 1991 .

[7]  W. T. Dempster,et al.  SPACE REQUIREMENTS OF THE SEATED OPERATOR, GEOMETRICAL, KINEMATIC, AND MECHANICAL ASPECTS OF THE BODY WITH SPECIAL REFERENCE TO THE LIMBS , 1955 .

[8]  Robert Riener,et al.  Inverse dynamics as a tool for motion analysis: arm tracking movements in cerebellar patients , 1997, Journal of Neuroscience Methods.

[9]  M. Ae,et al.  Body segment parameters of Japanese children , 1986 .

[10]  Steven C Cramer,et al.  Robotics, motor learning, and neurologic recovery. , 2004, Annual review of biomedical engineering.

[11]  M Fritz An improved biomechanical model for simulating the strain of the hand-arm system under vibration stress. , 1991, Journal of biomechanics.

[12]  D. Rossi,et al.  Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation , 2005, First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics Conference.

[13]  Garth R Johnson,et al.  A study of the external forces and moments at the shoulder and elbow while performing every day tasks. , 2004, Clinical biomechanics.

[14]  R. Raikova A general approach for modelling and mathematical investigation of the human upper limb. , 1992, Journal of biomechanics.

[15]  E Viikari-Juntura,et al.  Load-sharing patterns in the shoulder during isometric flexion tasks. , 1995, Journal of biomechanics.

[16]  F. Zajac,et al.  Determining Muscle's Force and Action in Multi‐Articular Movement , 1989, Exercise and sport sciences reviews.

[17]  B Peterson,et al.  Biomechanical model of the human shoulder joint--II. The shoulder rhythm. , 1991, Journal of biomechanics.

[18]  A Seireg,et al.  A mathematical model for evaluation of forces in lower extremeties of the musculo-skeletal system. , 1973, Journal of biomechanics.

[19]  Raymond G. Gosine,et al.  A functional task analysis and motion simulation for the development of a powered upper-limb orthosis , 1994 .

[20]  F. C. T. Helm,et al.  Analysis of the kinematic and dynamic behavior of the shoulder mechanism , 1994 .

[21]  K. Mauritz,et al.  Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand , 1995, Journal of the Neurological Sciences.