D2.1 Requirements and Specification - CORBYS

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D2.1 Requirements and Specification function and improved gait (Barbeau and Rossignol, 1994; Dietz et al.,1994; Hesse et al., 1995). In addition, there are several secondary positive effects on the physical and mental condition of these patients (Hidler et al., 2008). Gait training prevents many of the secondary complications that often result from neurological injury and gait impairment (e.g. joint stiffening, muscle atrophy, cardiovascular deterioration, pneumonia, deep venous trombosis). Therefore, treadmill training is a well-established practice nowadays in rehabilitation centres for the neurologically impaired. The driving force behind neural recovery is neural plasticity, the ability of neural circuits, both in the brain and the spinal cord, to reorganise or change function (Elbert et al., 1994). This process was clearly evidenced in prior animal research, revealing the existence of so called “central pattern generators” at the level of the spinal cord that allow to reinstill animal gait through training following spinal cord lesion. However, neural recovery has proven to be much more complex in humans, as human walking involves both spinal control and brain control (Yang and Gorassini, 2006). For rehabilitation to be successful neural plasticity should thus be maximally promoted. Although the mechanisms underlying neural recovery are not yet fully understood, there is a growing consensus about the major enabling principles. Sensory input from the muscles and joints to the central nervous system (afferent input) is crucial (Ridding and Rothwell, 1999; Harkema, 2001). Also, these sensory cues should match as closely as possible with those normally involved in the task to be relearned. Some critical cues of human locomotion have been established, but are subject of ongoing research (Behrman and Harkema, 2000). Another important requirement for recovery is that training should be intensive and that it should be started as early as possible after the injury to maximise outcome (Sinkjaer and Popovic, 2005). The need for intensive training and the aim of relieving therapists from the physical strain induced by manually assisted gait training, triggered the application of robotic assistance to gait rehabilitation. The development and use of gait rehabilitation robots both in rehabilitation practice and in research labs has strengthened the validity of some concepts that are believed to underlie gait retraining in general and also to increase the effectiveness of robot-assisted training itself. A key finding is that assisting movements (too much) may result in reduced effort and decreased motor learning (Marchal-Crespo and Reinkensmeyer, 2008). This is evidenced by motor learning studies in unimpaired subjects involving robotic assistance to learn a movement task, and appears to apply to neural recovery as well. Some studies explored the benefits of amplifying movement errors instead of correcting them, which was found to improve short term motor learning, as reported for instance in Reisman et al., 2007. It was also shown that the training should be adapted to the skills of the subject: similar to providing too much assistance, providing too little is counterproductive (Emken et al., 2007). Another important aspect to training is active participation, which is promoted by motivation. The robotic training environment should trigger the subject to self-initiate and actively contribute to the movements and also to sustain efforts (Lotze et al., 2003). The suggestion that effort may be more important than (robotic) assistance, questions the rationale behind the use of robots in movement therapy (Reinkensmeyer et al., 2007). Nonetheless, many rationales for using robotic assistance in gait rehabilitation can be found in literature (Guglielmelli et al. 2009; Marchal-Crespo and Reinkensmeyer, 2009). Previously unexplored movements provide novel sensory information to the patient, assistance makes gait training more safe and intense, and helping the patient accomplish desired movements is an important motivating factor (an extensive overview can be found in Marchal-Crespo and Reinkensmeyer (2009)). The aforementioned concepts are encompassed by a best practice in assistance-based robotic therapy commonly referred to by “assistance-as-needed”, implying that the robot should assist only as much as needed and only where needed. Hence, the level of assistance should be adaptable and task (or function) specific. Newly developed robot technology for gait rehabilitation is increasingly focused on this paradigm. The following section puts emphasis on general concepts and hardware. 164
D2.1 Requirements and Specification Figure 44: Gait rehabilitation robots: end-effector based (e.g. HapticWalker) and exoskeleton based (e.g. Lokomat®). Related applications supporting the development of exoskeleton based gait rehabilitation robots: human performance augmenting exoskeletons (e.g. HAL), assistive exoskeletons (e.g. ReWalk®), powered prosthetics (e.g. C-leg®) 16.2 Gait rehabilitation robots In the course of merely ten years, the number of rehabilitation devices, and from a broader perspective, the advancements in assistive technology, have grown remarkably. Although common challenges are faced in the development of robots for the upper limbs, this overview is limited to devices for the lower limbs. Similarly to rehabilitation robots for the upper limb, gait rehabilitation robots can be categorised according to their underlying kinematic concept into end-effector based and exoskeleton based robots (Guglielmelli et al., 2009). End-effector based robots interact with the human body in a single point (through their end-effector), whereas exoskeleton based robots interact with the human body in different points across human joints. The latter typically have an anthropomorphic, serial linkage type structure that acts in parallel with the lower limbs. Seldom, there are gait training devices not belonging to any of these two categories. String-man, a device consisting of tensioned wires attached to the body is an example (Surdilovic et al., 2007). 16.2.1 Endeffector type devices Commercially available end-effector type devices are the GT1 Gait Trainer and its successor G-EO (Rehastim, Germany). These are based on a doubled crank and rocker gear system, driving two programmable footplates, generating gait-like movements of the lower limbs (Hesse and Uhlenbrock, 2000). 165
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