Design and development of rehabilitation robotic system for rats with spinal cord injury

• Main Author: Anopas, Dollaporn
• Other Authors: Ang Wei Tech
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Mechanical engineering::Robots
• Online Access: https://hdl.handle.net/10356/146516
• Institution: Nanyang Technological University

Spinal cord injury (SCI) is mostly caused by an impairment of impulse conduction. At present, rehabilitation plays an important role to enhance motor recovery and to minimize the impairment of movement function. In addition, it has been found that coupling rehabilitation with assisted neuronal regrowth via regenerative medicine at the injury site can further improve recovery progress. From our preliminary experiment, the use of our rehabilitation robotic system with rats in a complete transection SCI model can facilitate more axon regeneration in scaffold compared to without using the robotic system. However, the Basso, Beattie, and Bresnahan (BBB) score which is popularly used to evaluate the motor recovery cannot show a significant difference between rehabilitated group and non-rehabilitated group. Therefore, a new method to assess the locomotor function is proposed using a force sensing system. This system is used to measure the vertical force exerted by a rat’s hindlimbs and it shows that the vertical force exerted by rehabilitated rats is significantly greater than non-rehabilitated rats. This experiment proves that rehabilitation is important for axon regeneration and motor recovery. Since the initial robotic system could only provide a fixed trajectory, the robotic system is improved to provide a more natural walking trajectory by synchronizing the gait between forelimbs and hindlimbs. A phase extraction method which consists of an unsupervised neural network learning, a phase synchronization algorithm, and a control strategy are developed to control the movement trajectories of a rat’s hindlimbs according to the forelimbs phase. To prove the performance of our phase extraction that can be used in real time, our phase extraction is compared against the Hilbert transform with principal component analysis (PCA) which is an analytical method for offine process. Results show that a percentage of root-mean-square time error between goal phase and our phase extraction method (7.94%) is comparable to that of Hilbert transform with PCA (7.44%). Moreover, the phase differences from our robotic system (0.80 rad) are less than the variability of phase difference (0.97 rad) from 14 healthy rats. This result shows that our system has the potential to synchronize forelimbs movement with hindlimbs movement in real time. In summary, the contribution of our research is the rehabilitation robotic system with the phase synchronization algorithm that is suitable for real-time application. Furthermore, the new method based on force sensing to evaluate the enhancement of locomotor performance is developed in this study.