Author Topic: Haptic Training Method Using a Nonlinear Joint Control  (Read 3260 times)

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Haptic Training Method Using a Nonlinear Joint Control
« on: April 23, 2011, 08:41:04 am »
Author : Jarillo-Silva Alejandro, Domínguez-Ramírez Omar A., Parra-Vega Vicente
International Journal of Scientific & Engineering Research, IJSER - Volume 2, Issue 4, April-2011
ISSN 2229-5518
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Abstract— There are many research works on robotic devices to assist in movement training following neurologic injuries such as stroke with effects on upper limbs. Conventional neurorehabilitation appears to have little impact on spontaneous biological recovery; to this end robotic neurorehabilitation has the potential for a greater impact. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. This paper consider the contribution on a haptic training method based on kinesthetic guidance scheme with a non linear control law (proxybased second order sliding mode control) with the human in the loop, and with purpose to guide a human user´s movement to move a tool (pen in this case) along a predetermined smooth trajectory with finite time tracking, the task is a real maze. The path planning can compensate for the inertial dynamics of changes in direction, minimizing the consumed energy and increasing the manipulability of the haptic device with the human in the loop. The Phantom haptic device is used as experimental platform, and the experimental results demonstrate.

Index Terms—Diagnosis and rehabilitation, haptic guidance, sliding mode control, path planning, haptic interface, passivity and control design.

IN  the least decade the number of patients who have suffere accidents stroke or traumatic brain injuries have increase considerably [1]. The central nervous system damage can lea to impaired movement control upper extremities, which ar facing major difficulties in relation to the activities of daily life. Several studies showed that the rehabilitation therapy which is based on motion-oriented tasks repetitive, it help to improve the move-ment disorder of these patients [2], [3]. Unfortunately repeatability therapy requires consistency, time for physicians and therefore money.
Conventional neurorehabilitation appears to have little impact on impairment on spontaneous biological recovery. Robotic neurorehabilitation has potential for a greater impact on impairment due to an easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, the capacity to deliver high dosage of training protocols and high intensity in the exercises. This situation economic rehabilitation. With the purpose of enhance the relationship between outcome and the costs of rehabilitation robotic devices are that being introduced in clinical rehabilitation [4], [5]. Rehabilitation using robotic devices not only has had an important contribution in this area but also has introduced greater accuracy and repeatability of rehabilitation exercises. Accurate measurement quantitative parameter using robotic instrumentation is a tool that accomplishes the goal of the patient’s recovery.

As care is decentralized and moves away the inpatient settings to homes, the availability of technologies can provide effective treatment outside, the acute care of the hospital would be critical to achieve sustainable man-agement of such diseases. In the field of neuromotor rehabilitation, skilled clinical managers and therapists can achieve remarkable results without using technological tools or with rudimentary equipment, but such precious human capital is in very short supply and, in an case, is totally insufficient to sustain the current demographic.
Robots are particularly suitable for both rigorous testing and application of motor learning principles to neurorehabilitation. Computational motor control and learning principles derived from studies in healthy subjects are introduced in the context of robotic neurorehabilitation. There is an increasing interest in using robotic devices to provide rehabilitation therapy following neurologic injuries such as stroke and spinal cord injury. The general paradigm to be explored is to use a robotic device to physically interact with the participant’s limbs during movement training, although there is also work that uses robot that do not physically contact the patients [6].

The Problem
Some of the problems presented by a motor rehabilitation of patients who have suffered a brain injury, is the time rehabilitation takes place, the cost that this entails, poor information that the specialist has to determine the diagnosis of a patient in rehabilitation, and the lack of platforms for rehabilitating patients who cannot assist to the hospital and require continued rehabilitation.

Our Proposal
One technique used to solve the problem of generating rehabilitation platforms using robotic systems is haptic guidance, this technique is based on the use of haptic devices, which allow human-machine interaction. These haptic devices are programmed under certain considerations, such as considering the safety of the patient during the interaction, the physiology of the patient in order to generate tracking trajectories that allow proper rehabilitation, and so on. In this paper is proposed to generate a trajectory based on the solution of mazes in 2D and the haptic device guides the patient under a control law, which is designed with certain characteristics that allow coupling among the patient, the device and the trajectory. The haptic device is equipped with optical encoders, this allows obtaining data such as position, velocity, acceleration, force and torque, these data in combination with other data allow to the specialist to generate a clinical diagnosis, which has therapeutic support based on the patient’s movements in eac exercise performed on the platform.

In section 2, we introduce the human-haptic interaction, including the haptic scheme, the dynamic model of the haptic device and the guidance control law implemented in the experimental platform. The description task and the path planning for guiding to the experiments are given in section 3; experimental results are discussed in 4. Finally, we presen the conclusions in section 5.

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