Stroke is a major concern in countries with aging populations like Japan, where the risk of stroke can be as high as 20% in middle-aged people. It is one of the leading causes of motor function loss and disability. Appropriate rehabilitation can help restore function to the affected areas, but it can be expensive, often requiring several trained therapists for a single patient. Robotic rehabilitation offers a solution to this issue.
An important concern in the design of robots is the ‘inverse kinematics’ problem. A rehabilitation robot mimics the human limb and has the same number of joints. When it comes to upper limb rehabilitation robots, that includes the shoulder joint, the elbow, and the wrist. Extending these joints to a particular angle makes the arm move a certain way. But imagine the possible number of angles that the joints could bend in just to do something simple like picking up an object. This is where inverse kinematics comes in. It looks at where the arm needs to go (its end position and direction) and then calculates backwards to determine what angles the various joints would need to be bent at to achieve this. It seems like an obvious solution, but the mathematics behind it is incredibly complicated. In an article published in Artificial Intelligence Review¸ a group of scientists led by Dr. Tam Bui of Shibaura Institute of Technology, Japan, propose an improved algorithm for solving the inverse kinematics problem. “The advantage of the proposed algorithm is the reduced complexity and volume of calculations in comparison with other methods,” says Dr. Bui.
In recent years, optimization-based approaches to solving the inverse kinematics problem have gained popularity. Optimization deals with the minimization or maximization of an objective function (such as execution time, or energy of execution). But most current approaches to solving the inverse kinematics problem do not look at the feasibility of the optimized joint angles, i.e., if the shoulder, elbow, and wrist of the human arm on which the robot is going to be used can move in the same way that the robot arm is moving based on the back calculations. Dr. Bui and team accounted for this variable when they developed their algorithm, titled ‘self-adaptive control parameters in Differential Evolution with search space improvement (Pro-ISADE).’ Like the name suggests, this uses an optimization approach called Differential Evolution (where the calculation is iterated until the best or ‘optimal’ solution is found) with a reduced search space, which is the range of angles the joints of the robot can bend in. Reducing the search space improves the calculation speed of the algorithm and ensures that the calculated joint angles for the robot are not unnatural.
Dr. Bui and his team—Dr. Trung Nguyen and Dr. Ha Pham from HUST, Hanoi, Vietnam—used their Pro-ISADE approach to solve the inverse kinematic problem for the human arm in two activities essential to daily life, drinking a glass of water and brushing one’s teeth. They also studied throwing and catching a ball. First, they performed the activities using a functional human arm and captured the angles of the arm using a measurement device they had created, called an Exoskeleton type human motion capture system (E-HMCS). The E-HMCS is strapped onto the arm and converts its motion into an electronic signal using sensors called potentiometers.
The researchers used the E-HMCS to record the path taken by the arm to perform the tasks, as well as the arm’s final position and direction. They then put these recorded values into the Pro-ISADE algorithm to see if it could accurately predict the angles the robotic joints had to make to accomplish the tasks. They found that the Pro-ISADE algorithm predicted values that were very close to the measured angles, proving its effectiveness at solving the inverse kinematics problem for natural human movements.
The Pro-ISADE algorithm is, thus, a great tool for rehabilitation robots, where mimicking the natural movement of the human body is essential to avoid injury to patients. According to Dr. Bui, “Robot-assisted rehabilitation allows for higher intensity training, longer duration, and more repetition. Upper limb rehabilitation robots could help post-stroke patients quickly reintegrate into their daily lives.”
The Pro-ISADE algorithm could also be used in industrial and server robots, showing the wide applicability and enormous potential of this approach.
About Shibaura Institute of Technology (SIT), Japan
Shibaura Institute of Technology (SIT) is a private university with campuses in Tokyo and Saitama. Since the establishment of its predecessor, Tokyo Higher School of Industry and Commerce, in 1927, it has maintained “learning through practice” as its philosophy in the education of engineers. SIT was the only private science and engineering university selected for the Top Global University Project sponsored by the Ministry of Education, Culture, Sports, Science and Technology and will receive support from the ministry for 10 years starting from the 2014 academic year. Its motto, “Nurturing engineers who learn from society and contribute to society,” reflects its mission of fostering scientists and engineers who can contribute to the sustainable growth of the world by exposing their over 8,000 students to culturally diverse environments, where they learn to cope, collaborate, and relate with fellow students from around the world.
About Dr. Tam Bui from SIT, Japan
Dr. Tam Bui obtained his Ph.D. in Mechanical Engineering in 2015 from SIT. He currently works as an Assistant Professor in the Department of Machinery and Control Systems at SIT. His research interests include engineering optimization, optimization design, robotics, and manufacturing technology. He has published 33 articles and one book chapter. Dr. Bui also serves as a lecturer at Hanoi University of Science and Technology, Vietnam.
This study was also supported by the Centennial SIT Action for the 100th anniversary of Shibaura Institute of Technology entering the top 10 at the Asian Institute of Technology.
Artificial Intelligence Review
Method of Research
Subject of Research
Using proposed optimization algorithm for solving inverse kinematics of human upper limb applying in rehabilitation robotic
Article Publication Date
The authors declare that they have no conflict of interest.