Neurorehabilitation Devices: Engineering Design, Measurement and Control
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Click on the cover image above to read some pages of this book! Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Debilitating neuromuscular disorders and traumatic brain, spinal cord or peripheral injuries have a devastating effect on those who suffer from them.
Written from an engineering perspective, and based on a course taught by the American Society of Mechanical Engineers, Neurorehabilitation Devices first helps the designer to better understand and formulate design, measurement and control systems for biomedical devices used in the treatment and recovery of people suffering from these disorders.
Just some of the topics covered in this book are: methods to allow an amputee to control a powered artificial arm by means of electrical signals generated by contractions of muscles of the residual limb in combination with motor nerve activity from peripheral nerves, as well as the development of new technologies to use electrical stimulation to treat the hyperactive bladder, to electrically induce bowel movement and defecation, and to develop methods for selective stimulation of nerve fibres.
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The development of robotic devices for rehabilitation is a fast-growing field. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen based on motor control paradigms or with non-invasive brain machine interfaces. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios.
We also review assessment techniques for the evaluation of robotic therapy.
Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease. Introduction Motor and sensory loss or dysfunction, caused by brain injuries or neurological disorders, severely affects the quality of life and may culminate in the inability to perform simple activities of daily living.
These impairments can also affect the lower limb, compromising, with different degrees of severity, the sensorimotor strategies used by the brain during gait and balance control. In order to understand how to recover from these pathological conditions, it is necessary to highlight how the patient behavior is affected by a specific impairment. For example, proprioceptive impairments affect movement planning and inter-limb coordination [ 3 , 4 ]; paresis affects movements in accuracy, temporal efficiency, and efficacy [ 5 ]; and abnormal muscle tone turns into a lack of movement smoothness and intra-limb coordination [ 6 ].
In the last decades, innovative robotic technologies have been developed in order to effectively help clinicians during the neurorehabilitation process. However, most of the studies in this field have been focused more on the development of the devices, whereas less effort was made on maximizing their efficacy for promoting recovery. The main challenge consists of designing effective training modalities, supported by appropriate control strategies. Thus, each robotic device supports a pre-defined training modality depending on the low-level control strategy implemented and also on the residual abilities of each patient.
However, among all the different training modalities, the most relevant is the assistive one. Assistive controllers help participants to move their impaired limbs according to the desired postures during grasping, reaching, or walking, reflecting the strategy adopted by conventional physical and occupational therapy active assistive training mode. Specifically, among the assistive strategies, the assistance-as-needed is widely employed because it reduces the patient risk of relying only on the robot to accomplish the rehabilitative task.
Indeed, over-assistance could decrease the level of participation and, as a consequence, also the chance to induce neuroplastic changes [ 9 ]. In addition to the assistance-as-needed strategy, to avoid the slacking effect, challenge-based controllers are used to make tasks more difficult or stimulating. Another challenge-based approach is the constraint-induced strategy.
This requires specific patterns of force generation to avoid compensatory movements and ensure the right postures [ 13 ].
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Corrective strategies have the same aim: Through the creation of virtual haptic channels for the end-effector or the joints of the exoskeleton tunneling , users are allowed to move only in delimited tunnels. Once they go out from the correct path, adopting compensatory movements, they are forced to go back into the channel [ 14 , 15 ]. Moreover, error enhancement strategies that amplify movement errors have been proposed since kinematic errors generated during movement are a fundamental neural signal that drives motor adaptation [ 16 , 17 ] for a detailed review of the available control strategies and their implications, see Marchal-Crespo et al.
In order to improve the potentiality of neurorehabilitation, it is then crucial to combine robotic therapy with other disciplines, such as computational neuroscience, motor learning and control, and bio-signal processing, among others. Based on the previous considerations, here, we highlight recent findings in different fields that could be or are already applied to robotic neurorehabilitation. In the first section, we present an overview of state-of-the-art robotic devices for the upper and lower limb, with two specific case studies.
The second section exploits complementing knowledge of other domains, focusing on rehabilitative training and assessment of behavioral and neural changes induced by robotic therapy. We conclude by providing a general perspective on the research in the robotic neurorehabilitation field for the near future, illustrating the limitations of current systems and perspectives for further improvement. Robotic devices for neurorehabilitation can be classified into two main categories based on the different types of physical human—robot interaction: end-effector devices and exoskeletons.
End-effector-based systems are robotic devices provided with a specific interface that mechanically constrains the distal part of the human limb e. These systems do not control the whole kinematic chain and the human limb is free to completely adapt either to external disturbances or to movements applied by the end-effector robot.
Neurorehabilitation Devices: Engineering Design, Measurement and Control
Exoskeletons, on the contrary, exactly reproduce the kinematics of the human limb and support its movements through the control of the position and the orientation of each joint. The devices are designed with the specific purpose of coupling and aligning the mechanical joints to the human ones.
In addition, the range of motion ROM and the number of the actuated joints are appropriately chosen to optimize the control. In the following section, an overview of the available robotic devices for the upper and lower limb is provided, including a detailed description of two newly developed systems treated as case studies. Various robotic systems for the upper limb have been developed, and protocols based on task-oriented repetitive movements have been proposed to improve ROM [ 19 , 20 ], muscle strength [ 21 ], movement coordination [ 22 ], and to promote motor learning [ 23 ].
Depending on the type and severity of the motor dysfunction and related impairment, one type of device could be more effective than the other. Specifically, if the residual sensorimotor functionalities of the patient are extremely low, exoskeletons could be more appropriate to apply forces to each joint [ 24 ].
Moreover, end-effector devices could be more effective to deliver complex patterns of forces e.
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One of the first end-effector robots developed for upper limb rehabilitation, the MIT Manus [ 26 ], belongs to the laboratories of the Massachusetts Institute of Technology MIT and was designed for the shoulder and elbow joints. Additionally, the Mirror Image Motion Enabler [ 31 ] and the Bi-Manu-Track [ 32 ] are two examples of upper limb robotic devices designed to implement bimanual training protocols. A summary of the most common upper limb end-effector robots and their main features is provided in Table 1. It is worth mentioning that the exoskeletons currently developed differ in terms of mechanical structure.
In detail, regarding the upper limb, most of them do not provide actuation for all the degrees of freedom see [ 53 ] for a complete review , as they are only equipped with motors for the movements of the shoulder L-Exos [ 54 ], the Pneu-Wrex [ 55 ] and elbow joints, while additional actuation for the wrist is not available. The design of the exoskeleton for the hand is indeed more difficult.
Table 2 summarizes the exoskeletons for the upper limb. Another interesting classification of exoskeleton and end-effector devices relates to their actuation system. Available possibilities are actuation by a motor, actuation by pneumatic muscle, and non-motorized actuation such as hydraulic or springs [ 53 ]. In the first category, i. Upper limb exoskeletons for rehabilitation have been developed only recently compared to the end-effector devices.
This is due to different reasons [ 70 ]: i The complex interaction between the mechanical structure of the exoskeletons and the different joints of the human body; ii the complex control schemes to be adopted to deal with back-drivability and transparency; and iii the need to promote sensorimotor recovery of the patient not passively moving their joints but using assistive training modalities able to respond to any pathological movement [ 71 ].
The low-level implementation of these assistive training modalities is more challenging in exoskeletons than in end-effector devices. In particular, with the goal to exploit exoskeletons to also improve inter-joint coordination in the neurological population [ 72 ], several innovative control schemes have been designed. Starting from the common control schemes implemented in end-effector devices and exoskeletons, there is closed-loop feedback control with feedforward components.
To obtain the error signal of the feedback loop, the ability to sense some kinematic variable positions, velocities or interaction forces and then compare them with a pre-determined reference trajectory is needed [ 58 ]. Instead, the feedforward components can be computed by the robot-model or can be learnt with iterative techniques [ 52 ].