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Session: Poster session I
Session starts: Thursday 24 January, 15:00

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Joan Lobo-Prat (Laboratory of Biomechanical Engineering, University of Twente)
Peter Kooren (Dept. of Physics and Medical Technology, VU University Medical Centre)
Arno Stienen (Laboratory of Biomechanical Engineering, University of Twente)
Bart Koopman (Laboratory of Biomechanical Engineering, University of Twente)

Abstract:
Active movement-assistive devices can increase independence and quality of life of patients with severe neuromusculoskeletal disorders [1]. These systems should not operate autonomously [2] and therefore require interaction between the patient and the device for its control. A large variety of physiological signals (control inputs) can be measured from the human body, which can be used for the control of the active movement-assistive device: neural signals from the central nervous system (e.g. electroencephalography (EEG)), neural activation of the muscle (e.g. electromyography (EMG)), muscle interaction forces, body movements, neural signals from the physiological sensors (e.g. electroneuronography (ENG)) among others. In addition, signals can be measured from systems parallel to the assisted motor system (e.g. eye, head or tongue), which can also provide valuable input for the control of the active movement-assistive device. The selection of the body signal to control the assistive device according to each user needs and capabilities (which may change over time) is crucial for the usability and proper function of the assistive system. A large variety of control strategies have been proposed and it is unknown which strategy is the most suitable for each type of impairment and task. This study presents a method based on the early work of Duane T. McRuer and co-workers [3] to evaluate and compare the performance of healthy subjects during a screen based one-dimensional tracking task using several control inputs. The subject is asked to follow with a cursor a reference point (target) that moves according to an unpredictable multi-sine signal. The reference signal is composed of 10 sinusoidal signals between 0.1 and 3Hz with amplitudes that decrease logarithmically with frequency. In addition, the cursor has dynamic properties that resemble the ones of the human arm. The measured input signals from the different devices/sensors are normalized and implemented using three different control strategies (i.e. position, speed and force) to control the cursor. With the proposed method we will be able to carry out an objective and quantitative evaluation and comparison of the performance of healthy subjects during a screen based one-dimensional tracking task using several control inputs. The final objective of this evaluation is the selection of the most suitable control inputs (or combination of them) to operate an active upper extremity orthosis that is being developed within the Flextension project. REFERENCES [1] Dollar, A.M., and H. Herr (2008) IEEE Transactions on Robotics 24-1, pp. 144 –158. [2] Veltink, P.H., et al. (2001) Archives of Physiology and Biochemistry 109-1, pp. 1–9. [3] McRuer, Duane T. (1965) Air Force Flight Dynamics Laboratory, Research and Technology Division, Air Force Systems Command.