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Stroke, Robotics, and Sound: New Approaches in Treating Stroke Patients

Por: | 03 de febrero de 2014

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By Florina Speth and Michael Wahl, Humboldt-Universität zu Berlin

Nine out of ten patients are unable to use their arm following a stroke; for three or four out of ten, the loss is irreversible. We use our hands not merely to push buttons or dial phone numbers but also to grasp the fork, to open doors, to write our name, or to put on clothes. The therapy to overcome this lifelong disability caused by severe damage to nerves involves intensive and repetitive training. Treatment is generally successful if it starts early enough and is intensive, consisting of repetitive movements with a set goal. Robotic systems help in making the training sessions more intense and thus complement conventional therapy.

For most patients this rehabilitation process is prolonged, challenging, and often frustrating, as it demands endurance and patience: progress happens in small steps, and each has to be accepted gratefully. Robotic therapy is ideal for such conditions, and studies show that training with a robotic device to supplement conventional physical therapy gives good results. The results were shown to be dose-dependent: the more, the better. Robotic arm trainers are usually connected to multimedia systems, which offer a playful and encouraging learning environment. A major additional benefit is that a patient’s progress can be tracked and monitored. Patients receive instant feedback so that they know how they are doing, and the ergotherapist treating them can adjust the therapy accordingly.

Although using robotics as an extension of ‘ergo therapy’ is promising, further research is required on several aspects: first, such training has not been successful so far in making the patients better at any of the day-to-day activities. More research is needed to develop training that transforms the drilled movements into natural actions involved in a particular skill, whether it is holding a fork or using a knife or buttoning up a jacket. Secondly, the use of robotic systems needs to become a well-defined part of the conventional therapy. Thirdly, different training modes and algorithms are required that enable the robot to sense the patient’s intention. More specifically, we need to know which mode is effective at what stage of the treatment so that self-guided movements by the robot can be introduced at exactly the right time. Besides, we do not know yet which of the many elements of such a complex training environment, namely the robotic hardware connected to multimedia devices and a therapist sitting next to them, capture the patient’s attention and which are distracting. The individual elements of the process need to be studied in detail.

In treating stroke patients, the effects of the sounds used with the robotic systems sparked our interest. The sounds are either incidental, such as noise from mechanical operations, or intentional, such as the sound effects introduced to make gaming environments more realistic or for motivating the players. The effects of these sounds on motor rehabilitation have never been studied separately. Voices, the chirping of birds, and action-related sounds such as those from colliding objects in virtual scenes can have very strong and varying effects on rehabilitation, depending on the design and purpose. Such sounds can enhance the gaming experience by raising the levels of motivation, making the movements more fluent, or increasing recall. On the other hand, the sounds can make the gaming experience less enjoyable by introducing distractions, instilling fear, introducing stress, interfering with the patient–therapist interaction, or even by making the game boring once the initial novelty wears off. Sounds, and especially music, received a great deal of attention in research on rehabilitation during the last decade. The most significant effects in the context of motor rehabilitation were seen when rhythmical music was applied in improving a patient’s gait or in making the arm movements more fluent. Those suffering from stroke or Parkinson’s disease were introduced to a technique called “rhythmic acoustic stimulation” (RAS). An external timekeeper such as the beat in music makes it easier for patients to initiate and synchronize movements without conscious effort. Tapping along with a beat or shaking the head in rhythm is natural human behaviour, which stems from our ability to process and predict regular patterns: we adjust or adapt our movements automatically to match the pattern. A neural network seems to overlap and interact in areas that shape the time codes of different movements. If we are listening to music while walking, the beat may modulate the pace or bring the steps in sync with the music. This effect is called “audio-motor coupling”.

In aiming to enhance the acoustic design of robotic rehabilitation, our first consideration was to transfer the well-known and promising effects out of the context of motor rehabilitation to robotic devices. This approach offers a scientific starting point for the application of meaningful sound in motor rehabilitation treatments supplemented with robotics. We designed a clinical study involving stroke patients, a study currently in progress. The study combines robotic hand function training with rhythmical music and real-time musical feedback to find out whether rhythmic cues and music influence motivation and function. All patients in this study are treated with a commercial robotic hand function trainer called “Amadeo” (tyromotion GmbH) under different sound settings in addition to the conventional ergo therapy. The patients are divided into three groups: a control group that trains without deployment of any sound, a group that is given rhythmic acoustic cues, and a group that is given rhythmic music and real-time musical feedback. All the patients are tested before, during, and after the treatment to detect changes in function and motivation. We hope that the results of our experiments will inform further technically assisted therapy for rehabilitation.


Florina Speth and Michael Wahl
Humboldt-Universität zu Berlin

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