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Posts Tagged Passive
[ARTICLE] Design of a robot-assisted exoskeleton for passive wrist and forearm rehabilitation – Full Text
This paper presents a new exoskeleton design for wrist and forearm rehabilitation. The contribution of this study is to offer a methodology which shows how to adapt a serial manipulator that reduces the number of actuators used on exoskeleton design for the rehabilitation. The system offered is a combination of end-effector- and exoskeleton-based devices. The passive exoskeleton is attached to the end effector of the manipulator, which provides motion for the purpose of rehabilitation process. The Denso VP 6-Axis Articulated Robot is used to control motion of the exoskeleton during the rehabilitation process. The exoskeleton is designed to be used for both wrist and forearm motions. The desired moving capabilities of the exoskeleton are flexion–extension (FE) and adduction–abduction (AA) motions for the wrist and pronation–supination (PS) motion for the forearm. The anatomical structure of a human limb is taken as a constraint during the design. The joints on the exoskeleton can be locked or unlocked manually in order to restrict or enable the movements. The parts of the exoskeleton include mechanical stoppers to prevent the excessive motion. One passive degree of freedom (DOF) is added in order to prevent misalignment problems between the axes of FE and AA motions. Kinematic feedback of the experiments is performed by using a wireless motion tracker assembled on the exoskeleton. The results proved that motion transmission from robot to exoskeleton is satisfactorily achieved. Instead of different exoskeletons in which each axis is driven and controlled separately, one serial robot with adaptable passive exoskeletons is adequate to facilitate rehabilitation exercises.
Deficiencies in the upper extremities restrain a person’s ability to go about daily life, consequently limiting one’s independence. Therefore, robots are used to perform task-oriented repetitive movements in order to improve motor recovery, muscle strength and movement coordination. Stroke is one of the primary reasons for a decrease in motor function of the upper limbs of human beings. It restricts the daily, social and household activities of the patients. Therefore, rehabilitation therapy is required to recover some of the movement lost (Bayona et al., 2005; Bonita and Beaglehole, 1988; Cramer and Riley, 2008). This is accomplished by a long-term intensive and repetitive rehabilitation period. Traditional therapies not only require great effort but also require the manual assistance of physiotherapists. The one-to-one contact of the therapists with their patients leaves the therapists exhausted. Moreover, therapists have limited abilities with regard to speed, senses, strength, and repeatability.
Robot-aided therapy is a developing part of post-stroke rehabilitation care (Reinkensmeyer et al., 2004). Robotic rehabilitation systems ensure compact therapy which can be applied in repetitive, controllable and accurate manner (Kahn et al., 2006; Marchal-Crespo and Reinkensmeyer, 2009). Robotic devices can provide limitless repeatability for patients thus decreasing the effort that therapists have to make (Kwakkel et al., 2008; Lum et al., 2002). Additionally, patient performance evaluation can easily be monitored and assessed by the therapists to adjust the rest of the required therapy (Celik et al., 2010; Ponomarenko et al., 2014).
The types of exercises are grouped into two branches: active and passive exercises. The subjects move their limbs actively and apply torque and/or force in active exercises. Passive exercises are in contrast to active exercises, in which the subjects remain passive during the exercise while an active device moves the limb. Continuous passive motion (CPM) is generated in this way (Maciejasz et al., 2014).
There is a broad range of robotic systems presented for upper-extremity rehabilitation. The mechanical structure of the rehabilitation robots can be mainly grouped into two parts: “end-effector-based” and “exoskeletons”. MIT-MANUS (Krebs et al., 1998) and MIME (Lum et al., 2002) are included in the first part. End-effector-type robots cover a large workspace without having the capability to apply torques to specific joints of the arm. Having simpler control structure than exoskeletons is an advantage of end-effector-type devices. The most distal part of the robot is in contact with the patient limb. The segments of the upper extremities can be regarded as a mechanical chain. Therefore, motion in the end effector of the robot will automatically move other segments of the patient. They may cause redundant configurations of the patient’s upper extremities and may risk injury. Exoskeletons are the external structural mechanisms that have joints and links that can collaborate with the human body. They transmit motion exerted by the links to the human joints, thus making them suitable for the human anatomy. Exoskeletons must be able to carry out movements within the natural limitations of a human wrist for an ergonomic design. Mechanical and control issues are more complex than end-effector-type devices. The 5 degrees of freedom (DOF) MAHI (Gupta and O’Malley, 2006), 6 DOF ARMin (Nef et al., 2008) and 7 DOF CADEN-7 (Perry et al., 2007) are some examples of exoskeletons used in upper-extremity rehabilitation. LIMPACT (Otten et al., 2015), MIT-Manus (Krebs et al., 1998) and MIME (Lum et al., 2005) are prime examples of systems designed for assisting upper-limb proximal joints (the shoulder and the elbow). On the other hand, CR-2 Haptic (Khor et al., 2014) has one rotational DOF. There are manual reconfigurations for any specific wrist movement. Systems called Universal Haptic Drive (Oblak et al., 2010), Bi-Manu-Track (Lum et al., 1993) and Supinator Extender (Allington et al., 2011) have 2 DOF. The closest configuration resembling a human wrist and a rehabilitation robot can be employed by a 3 DOF system with three revolute joints. This configuration type enhances the functionality of devices providing rehabilitation services as it allows independence for specific motions of the wrist. RiceWrist (Gupta et al., 2008) and CRAMER (Spencer et al., 2008) use parallel mechanisms for wrist and forearm rehabilitation. RiceWrist-S (Pehlivan et al., 2012) is a 3 DOF exoskeleton system which is the developed version of RiceWrist (Gupta et al., 2008). A three-axis gimbal called WristGimbal (Martinez et al., 2013) offers flexibility to adjust rotation centers of the axes in order to match the wrist center of the patient. A 3 DOF self-aligning exoskeleton given in Beekhuis et al. (2013) compensates for misalignment of the wrist and forearm. Parallelogram linkages are used for this purpose. Nu-Wrist (Omarkulov et al., 2016) is a novel self-aligning 3 DOF system allowing passive adaptation in the wrist joint.
This paper presents the design of an exoskeleton for human wrist and forearm rehabilitation. Specific wrist and forearm therapies are performed. An issue with the angular displacement limit of a robot axis was experienced. The solution method obtained by changing the design is given herein. Adapting a 6 DOF Denso robot for wrist and forearm rehabilitation is proposed. The novelty of the study is the use of an exoskeleton driven by a serial robot, which is a method that has not yet been tackled in the literature. The proposed system hybridized the end-effector-type and exoskeleton-type rehabilitation systems in order to utilize advantages and to avoid disadvantages. Precise movement transmission from robot to patient limb can be provided by using an exoskeleton which plays a guide role in the exercises. This adaptation makes the system feasible to apply torques to specific joints of the wrist and allow independent, concurrent and precise movement control. This technique offers flexibility to the users. If the user wants wrist and forearm rehabilitation, a 3-D model of the exoskeleton is designed, manufactured with 3-D printing technology and interfaced with the robot. The exoskeleton may be designed for ankle, shoulder and/or elbow applications. Therefore, a serial robot can be used as a motion provider for different types of rehabilitation. Instead of different exoskeletons having a motor for each axis, the combination of a serial robot and passive exoskeleton is enough to perform the rehabilitation exercises.
2 Wrist and forearm motion and exoskeleton design
A human uses the distal parts of his/her arm (i.e., wrist, forearm) in coordination with proximal parts (i.e., elbow, shoulder) in order to carry out movements required in daily life, e.g., wrist and forearm motions such as eating, writing, opening a door, driving an automobile and so on. The wrist joint has got 2 DOF; flexion and extension (FE) and radial–ulnar deviation. Radial–ulnar deviations can also be called adduction and abduction (AA), respectively. Flexion is the bending of the wrist so that the palm approaches the anterior surface of the forearm. The extension is the reverse of flexion. Abduction (radial deviation) is the bending of the wrist towards to the thumb side. The reverse of this motion is called adduction (ulnar deviation). Pronation and supination (PS) are the movements for the forearm. Pronation is applied to a hand such that the palm turns backward or downward. Supination is the rotation of the forearm such that the palm of the hand faces anteriorly to the anatomic position (Omarkulov et al., 2016). These motions are given in Fig. 1.
[Abstract] A Preliminary Study: Mobile Device for Hand and Wrist Rehabilitation – IEEE Conference Publication
[Abstract + References] Design of MobIle Digit Assistive System (MIDAS): A Passive Hand Extension Exoskeleton for Post Stroke Rehabilitation – Conference paper
Stroke often causes flexor hypertonia as well as weakness of finger extension. This limits functionality of the hand degrading independent ability to perform upper limb activities of daily living (ADL’s). Hand rehabilitation post stroke is vital to regaining functionality in the affected limb, leading to improved independence and quality of living. In this paper the development of DigEx and MIDAS passive arm orthoses are detailed. A quick-change cam system is implemented featuring one-handed cam swapping. This provides the ability to vary assistance levels to improve usability and independence for the user. Pulleys and bearings are added to reduce friction caused by mechanical contacts and material failure. Initial tests with the prototype are promising.
[Abstract] A Portable Passive Rehabilitation Robot for Upper-Extremity Functional Resistance Training
[ARTICLE] Elasticity improves handgrip performance and user experience during visuomotor control – Full Text
Passive rehabilitation devices, providing motivation and feedback, potentially offer an automated and low-cost therapy method, and can be used as simple human–machine interfaces. Here, we ask whether there is any advantage for a hand-training device to be elastic, as opposed to rigid, in terms of performance and preference. To address this question, we have developed a highly sensitive and portable digital handgrip, promoting independent and repetitive rehabilitation of grasp function based around a novel elastic force and position sensing structure. A usability study was performed on 66 healthy subjects to assess the effect of elastic versus rigid handgrip control during various visuomotor tracking tasks. The results indicate that, for tasks relying either on feedforward or on feedback control, novice users perform significantly better with the elastic handgrip, compared with the rigid equivalent (11% relative improvement, 9–14% mean range; p < 0.01). Furthermore, there was a threefold increase in the number of subjects who preferred elastic compared with rigid handgrip interaction. Our results suggest that device compliance is an important design consideration for grip training devices.
Interaction with the environment involves the exchange of forces while manipulation requires skillful force control and is a sensitive measure of motor condition [1,2]. For hand and finger training, this motivates isometric training based on force control without the need to support overt movements, for example using a force-sensing handle such as Tyromotion’s Pablo device (www.tyromotion.com). Grip force control can also be used for human–machine interfaces and teleoperation applications, e.g. control of surgical robotics , and as a tool to study ergonomics and handgrip design . Furthermore, grip strength is a pervasive clinical outcome supported by dynamometry-based isometric measurements (using the Jamar handgrip) [5,6]. Isometric training has been shown to enable the learning of force fields applied on virtual movements associated with the exerted isometric force and that this learning transferred to real (isotonic) movements [7,8]. However, such systems for isometric control or strength do not support the kinematic aspect of training which is an intrinsic part of manipulation and activities of daily living (ADLs) .
Grasping of objects involves grip aperture modulation and shaping of the hand, and often involves interaction with soft objects or manipulation . This suggests that grip training should involve learning to shape one’s hand across a range of joint angles similar to natural grasping tasks. Moreover, allowing the stretching of muscles can reduce collagen build-up in the joints and prevent further biomechanical issues such as contractures . The MusicGlove system promotes finger individuation through finger tapping , while Neofect’s Smartglove can measure overt movements of the digits using bend sensors , with both interfacing to virtual environments for training. A recent study in 12 chronic stroke patients with moderate hemiparesis comparing two weeks of movement-based training using the MusicGlove system to both isometric grip training and conventional therapy showed superior functional outcomes .
While skilful force control is critical to efficient manipulation, it may be helped by using additional joint position sensing. Indeed, proprioception can be divided into both static and dynamic components, and relies on various types of mechanoreceptors and skin afferents, including muscle spindles, Golgi tendon organs and skin stretch senses . The different afferents respond in a variety of ways to different stimuli, for example muscle spindle receptors signal both the length and rate of change of muscles hence contributing to both the static and dynamic components . The static component senses the stationary limb while the dynamic component involves the estimation of limb position and velocity during either volitionally generated active movements or passively induced motions. In fact, active movement itself as opposed to endpoint postures is thought to provide the greatest acuity for localization . Therefore, elastic as opposed to isometric interaction will provide additional coordinated kinaesthetic information facilitating control and learning by playing a vital role during the planning and execution of voluntary movements [17,18]. A recent study comparing virtual learning based on isometric force information demonstrated the beneficial effect of additional elastic deformation on control and learning . Damage to the neural circuits mediating proprioceptive function, e.g. due to an infarction in thalamic or parietal brain areas, can impair a patient’s ability during goal-directed movement, prehension, accurate aiming, reaching and tracking movements [20,21]. This can occur in up to half of stroke patients and therefore technology that can stimulate proprioceptive feedback during active training are essential.
The vast majority of ADLs require a functioning hand. This explains why individuals with complete loss of movement capabilities select recovering arm and hand function as their number one priority for improving their quality of life . Unfortunately, 77% of stroke survivors are affected by arm–hand weakness and poor control , while impaired hand function is also common in other neurological diseases such as cerebral palsy and multiple sclerosis. Hand function is also commonly impaired as a consequence of rheumatological and orthopaedic conditions such as symptomatic hand arthritis which is estimated to affect over 300 million worldwide . The only intervention shown to improve arm function is repetitive, task-specific exercise, but this is limited by the cost and availability of physiotherapists [25,26]. To address this issue, we are developing affordable devices to promote independent training of hand function from the ward to the home. These simple devices provide accessible functional rehabilitation by working on improving hand function through the use of engaging virtual therapy games controlled via sensors. With such devices, it is possible to train hand functions through individuated finger movements or whole hand grip force control .
So how can one train using both force control and hand kinaesthesia with a passive device using no actuators? To manipulate objects such as a soft ball, one has to control the force which is coupled to motion through the object’s elasticity. Similarly, we have created an elastic handle with a spring mechanism in series with a force transducer yielding force-sensing coupled with movement deformation. In a recent study, we showed that this sensitive mechanism enables even severely impaired patients to interact with a mobile tablet PC who would otherwise be unable to use such technology by conventional means, i.e. swiping, tapping and tilting .
This device has enabled us to study the effect of elasticity and resulting proprioceptive information on grip control. We have carried out a usability study with 66 healthy individuals, contrasting the elastic behaviour that this handgrip affords to isometric-equivalent interaction during visuomotor tracking tasks. We used two types of tasks, namely, one relying predominantly on feedforward information while the other relies on continuous sensory feedback. The digital handgrip and mobile-based virtual therapy platform used for this experiment are described in the next section, followed by the description of the visuomotor tasks and experimental protocols. The results presented in the following section reveal advantages of the elastic interaction over pure isometric information for grip control, alongside the influence of different factors on performance and preferences during the different interaction modalities.