Posts Tagged six-axis inertial measurement unit sensor

[ARTICLE] Active rehabilitation training system for upper limb based on virtual reality – Full Text

 

In this article, an active rehabilitation training system based on the virtual reality technology is designed for patients with the upper-limb hemiparesis. The six-axis inertial measurement unit sensors are used to acquire the range of motion of both shoulder and elbow joints. In order to enhance the effect of rehabilitation training, several virtual rehabilitation training games based on the Unity3D engine are designed to complete different tasks from simple level to complicated level. The purpose is to increase the patients’ interest during the rehabilitation training. The basic functions of the virtual rehabilitation task scenes are tested and verified through the single-joint training and the multi-joint compounding training experiments. The experimental results show that the ranges of motion of both shoulder and elbow joints can reach the required ranges of a normal human in the rehabilitation training games. Therefore, the system which is easy to wear, low cost, wireless communication, real-time data acquisition, and interesting virtual rehabilitation task games can provide an effective rehabilitation training process for the upper-limb hemiparesis at home.

The upper limb has many degrees of freedom, and it is also a complex part of the human body by which people can accomplish fine movements during their activities in daily life. With the intensification of the aging problem in the world, the amount of stroke hemiparesis has shown a growing trend, especially in China, which has an enormous population.1 Approximately 30%–50% of these stroke survivors will suffer from chronic hemiparesis, especially involving their hands. In addition, spinal cord injury (SCI) and traffic accident survivors may also find limb movements’ disorder. Injury within the cervical region of the cord leads to tetraplegia, which leads to impairment of all four limbs. An estimated result shows that 55% of new cases will result in tetraplegia, while the other 45% will experience paraplegia due to injury below the cervical level.2Limb hemiparesis which is caused by stroke, SCI, or traffic accidents not only gives the patient’s daily life a great deal of inconvenience and even more makes the patient suffer from great mental pain but also brings a heavy stress and medical burden for the patient’s family and society. Technology has been developed in an effort to facilitate rehabilitation for the patient. Upper-limb rehabilitation is one of the fastest growing areas in modern neurorehabilitation. Quality of life can be improved with efficient therapy.3 At present, rehabilitation therapy of upper limb with traditional rehabilitation therapy is commonly used, that is, rehabilitation therapists perform rehabilitation trainings on individuals. Now with the development of robot technology, the rehabilitation of robot-assisted training is also rising up. The MIT-Manus4 is an example of end-effector-based and arm-rehabilitation robotic device, while the ARMin device5 is an example of arm-rehabilitation exoskeletons which also allows pronation/supination of the lower arm and wrist flexion/extension. It could be operated in three modes: passive mobilization, active game-supported arm therapy, and active training of activities of daily living (ADLs). The end-effector-based robots have practical advantages (usability, simplicity, and cost-effectiveness), and exoskeleton robots have biomechanical advantages (better guidance). Currently, the automatic rehabilitation devices on market as mentioned above are mostly complex and expensive, which are often used in the hospitals and clinics are not affordable to ordinary patients. Therefore, one of the research objectives aims to develop the upper-limb rehabilitation training system with minimal structure and low cost and can be used in patient’s home. But in China, it can be seen that patients with upper-limb orthosis in home is only for fixing the arm and just move autonomously according to the setting angle. The researches on intelligent domestic rehabilitation device just begins, most of which are in the experimental stage and not yet market oriented.6,7

Another problem is that the patients are treated with low initiative and dull training process which does not motivate them, while the treatment effect is not obvious.8,9 Computer games based on virtual reality (VR) are a good way to mobilize the patients’ initiative in the training, so the rehabilitation effect on a particular movement task will be greatly improved.10 VR environments provide an excellent method to manipulate task conditions in training. The effects and the intensity of training can be enhanced and designed more challenging, since the implementation of VR can build a channel both visual and haptic communication can be involved in. The research on VR system which is applied to rehabilitation training was initiated a few years ago. Mazzone et al.11 made a study on the effect of rehabilitation training for patients with shoulder joints training using VR technology. This study aimed to determine whether performance of shoulder exercises in virtual reality gaming (VRG) results in similar muscle activation as non-VRG exercise. The conclusion was drawn that exercise with VRG should be effective to reduce shoulder pain caused by spinal injury. Fischer et al.12 conducted a preliminary study claim that stroke patients could assist themselves in training their hands in the virtual environment. The purpose of this pilot study was to investigate the impact of assisted motor training in a virtual environment on hand function for the stroke survivors. Participants had 6 weeks of training in reach-to-grasp of both virtual and actual objects. After the training period, participants in all three groups demonstrated a decrease in time to perform some of the functional tasks. These designs based on VR have achieved some success and then the second research objective is to add the VR technology to the intelligent domestic rehabilitation device. These studies are mainly designed for the single joint of the upper-limb rehabilitation training. Therefore, it is necessary to carry out the research on multi-joint combined training device for patients who can just stay home by training with VR tasks of adjustable game levels.

Another important element which needs to be considered as an ultimate success using at home is its ease of use. Therefore, simple active rehabilitation device should be developed. The setup time of such device should be fast, besides measurement, treatment approaches, and incorporating gaming, and should provide intuitive interfaces that can be directly utilized by the individuals. This study will introduce an active rehabilitation training system for upper limb based on VR technology, which has some advantages such as simple structure, easy to manipulate, and portable for household. It also mobilizes patients’ initiative with adjustable difficulty level of VR tasks so that the individuals’ rehabilitation effect of the upper limb is obviously improved.

The active rehabilitation training system for upper limb based on VR is designed for the pronation/supination and flexion movement trainings of the elbow joint and the extension/flexion and abduction exercises of the shoulder joint. By adding the games in training processes, the patients may actively participate in rehabilitation trainings, while the efficiency will be greatly improved. The portable and easy-to-use design of this system can effectively reduce the problem of the medical resources shortage in the rehabilitation field.

Overall scheme of the system

The system is composed of two parts: the upper-limb posture detection system and the virtual rehabilitation training task scene, as shown in Figure 1.

figure

Figure 1. Schematic diagram of an active rehabilitation training system for upper limb based on VR.

 

Continue —> Active rehabilitation training system for upper limb based on virtual realityAdvances in Mechanical Engineering – Jianhai Han, Shujun Lian, Bingjing Guo, Xiangpan Li, Aimin You, 2017

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