Abstract:Robot based therapy is one of the prevalent therapeutic approaches in motor stroke rehabilitation. It is often used in hospitals in combination with conventional therapy. In order to optimize human-robot interaction, we aim to investigate how a therapist physically supports patients during motor training of the upper extremities. This paper presents the design of a flexible textile sensor matrix, which measures the pressure exerted between therapist and patient during direct haptic interaction as well as the hand position and orientation in space. The matrix contains 144 sensors which enables measuring pressure intensity and localization of areas where the pressure is applied. The measurement matrix was evaluated with four healthy participants.
1 IntroductionYearly approximately 262,000 first-time or repeated strokes occur in Germany  and 700,000 in the United States  respectively, making stroke one of the dominant causes of acquired disability. One of the clinically evaluated and effective therapies is robot based rehabilitation training , , . Greater training time and a high intensity of repeated movements make rehabilitation robot based therapy very effective ,  . However, a crucial factor for the therapeutic benefit of such devices is the implemented control algorithms which define the haptic interaction between the patient and the robot . A large variety of control algorithms has been developed recently , ,  and it is known that different control strategies influence motor recovery in different ways ,  . However, it is not yet clear which algorithm is the best option for motor rehabilitation ,  and how new algorithms should be designed to ensure optimal recovery.
So far the way therapists perform haptic “assist as needed” interaction with a patient during physiotherapy is seen as the gold standard. Hence, our hypothesis is that a control strategy, which supports a patient in the way a therapist would do, would be most beneficial for motor learning. Some studies have already shown that human-human interaction is more efficient for executing motor tasks than working alone ,  and even more efficient than working with a robotic partner, regarding the time needed to accomplish a task ,  . Moreover, achieving a task in haptic connection with another person facilitates motor learning for healthy subjects more than achieving such a task either without any interaction partner  or with a robotic partner . Furthermore, understanding the therapist’s haptic behaviour can improve the mechanic design of future robotic therapeutic devices.
Therefore, we assume that closely investigating human-human interaction during performance of rehabilitation training and subsequent transfer of the haptic behaviour to a rehabilitation robot controller improves interaction between the patient and a robotic device for a similar task. To study direct haptic patient-therapist interaction, we need a sensor system that can measure pressure on the hand surface as well as hand position and orientation. At the same time, the sensor system should not inhibit the therapist-patient interaction. The system should be easy and flexible to apply on either right or left hand (depending on the patient’s affected side). Our goal was to analyse supination/pronation and flexion/extension movements, so the future sensor system needs to cover the hand (excluding fingers), wrist and parts of the forearm. The sensor system should be able to localize the interaction points and should be big enough to measure pressure distribution on inner and outer part of the hand. Finding a low cost solution was also a relevant aspect.
We found one existing solution to investigate comparable research questions for lower extremities: Galvez et al.  developed a lower leg orthosis which comprises two six-axis force/torque sensor and accelerometer units equipped with handles. The measurement units are attached close to knee and ankle and measure the therapeutic motion and interaction force support during gait training. However, the system does not allow measurement of force distribution on hand or leg surface. Other possible solutions is the pliance® sensing glove by Novel GmbH or the medilogic® sensor mat by T&T medilogic Medizintechnik GmbH. The systems allow analysing the interaction pressures with the help of capacitive transducers. However, measurements of the hand position are not possible. Existing solutions, which are also rather expensive, do not meet all of the mentioned requirements. Therefore, it was decided to develop a specific sensor system. In this paper, we present a new soft pressure sensitive matrix for measurement of interaction pressure with integrated inertial sensors for estimating the hand position.
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