The quality and ability of a person's reaching motion is important as it fundamental for many activities a person needs to be able to perform if s/he is to be independent, such as dressing, eating, and getting into/out of a chair. Additionally, the ability to reach enables support and anchoring to increase an individual's safety and mobility [
1]. Having a stroke can reduce a person's ability to reach because of the resulting death of associated brain cells. Fortunately, due to the plasticity of the brain, at least partial recovery is usually possible [
2]. Furthermore, recovery can be greatly enhanced by rehabilitation therapy [
3].
The use of haptic-robotics in therapy
A haptic interface is a human-computer interface that uses the sense of touch. The sense of touch is unique in that it can allow for simultaneous exploration and manipulation of a particular interface [
11]. By applying forces on the operator, a haptic device gives the tactile sensation of interacting dynamically with physical objects. Motor skills recovery is dependent on both afferent and efferent stimulation [
12], thus the capability of a haptic feedback system for simultaneous exploration and manipulation makes it ideal to use with stroke rehabilitation therapy. Consequently, there has been a recent rise in popularity of haptic feedback in therapy, and the devices that have been used are yielding encouraging results. Lum
et al. designed a novel therapy and assessment device that passively and actively guided users through upper-limb movements and recorded their performance [
13]. Krebs, Volpe,
et al. have contributed a large amount of data from clinical trials with MIT-MANUS and other robots that show improvements in patient outcomes when upper-limb training is present [
14,
15]. Loureiro
et al. strove to achieve a low cost modular home based system through GENTLE/s, a haptic and virtual reality system for upper-limb stroke rehabilitation [
16]. Reinkensmeyer
et al. used a different approach by exploring the simplicity of reaching motion therapy constrained to a straight line through the implementation of their Assisted Rehabilitation and Measurement Guide (ARM Guide) [
17]. Rosati
et al. devised MariBot, a 5 degree of freedom (DoF) system for bed-side therapy with acute period stroke patients [
18]. Nef and Riener developed ARMin, a large semi-exoskeleton with 6 DoF [
19]. For further details and a comparison of robitc-aided upper-limb rehabilitation, the reader is referred to [
20].
Compared to the robots mentioned above, the ARM Guide is the one that is most similar to this project [
17]. First of all, most of the systems above are quite large (many of them hospital based), operate as an exoskeleton to the user's arm, and/or require constant therapist supervision to ensure absolute user safety. Secondly, the reaching motion supported motion of the ARM guide is quite similar to the device created in this researh. The ARM Guide constrains the user to one simple 3 DoF reaching motion (a passive, linear reaching motion with adjustable yaw and pitch using locking mechanisms), however, this is coupled with sensors such as the 6 DoF force/torque sensor on the splint bearing to monitor abnormal tone, spasticity, and lack of coordination. In fact, Reinkensmeyer
et al. stated that one of the first objectives of the ARM Guide is to provide an improved diagnostic tool for assessing arm movement tone, spasticity, and coordination after brain injury [
17]. Although assessment and client performance are important factors, the primary focus of the research below is to construct a tool for (possibly long-term) post-stroke, upper-limb rehabilitation training.
The new robotic system described in this paper will provide several advantages over the current state-of-the-art. Firstly, the system will be lighter and more compact, allowing it to be used in various contexts and locations, such as at the patient's bedside, anywhere in a clinic, or at home. It will also be more intuitive and simpler to use as it does not require the user to have to learn how to "interact" with complex hardware. Finally, it will be capable of autonomous guidance through the use of a artificial intelligence based controller, which will allow the system to make decisions with respect the type of exercise automatically based on real-time feedback from the system and operator. This last advantage and the algorithms that have been developed will be the basis of a future publication. It is expected that the combination of the advantages above will result in a system that is versatile and accessible in a variety of settings.
Patients usually start with about 60 to 70 degrees of flexion in the elbow. The movement takes place in the saggital plane with the hand in alignment with the shoulder. The hand is pushed forward until it reaches the final desired position and then follows the reverse path until the hand and arm return to their initial positions. It is important to note that the motions should be smooth and controlled while the person performing the exercise maintains an upright posture. There are variations to this movement that are progressively implemented as the patient begins to regain use of his/her limb. One variation of this forward movement is to direct the path laterally outward at approximately 45 degrees using shoulder abduction and rotation on the horizontal plane. In the event that the patient requires assistance extending the elbow while exercising, gentle cueing is provided by the therapist using his/her fingertips to gently touch the patient between the ulna and radius (two long forearm bones) just below the olecranon (elbow), as well as portions of the triceps brachii tendon just above the olecranon. The therapist moves his/her touch away from the elbow to provide as much stimulation as possible. This touch is for directional cue and stimulation, not actual movement assistance, and therefore should be barely pushing the limb.