The goal of this study was to validate a series elastic actuator (SEA)-based robotic arm that can mimic three abnormal muscle behaviors, namely lead-pipe rigidity, cogwheel rigidity, and spasticity for medical education training purposes. Key characteristics of each muscle behavior were first modeled mathematically based on clinically-observed data across severity levels. A controller that incorporated feedback, feedforward, and disturbance observer schemes was implemented to deliver haptic target muscle resistive torques to the trainee during passive stretch assessments of the robotic arm. A series of benchtop tests across all behaviors and severity levels were conducted to validate the torque estimation accuracy of the custom SEA (RMSE: ~ 0.16 Nm) and the torque tracking performance of the controller (torque error percentage: < 2.8 %). A clinical validation study was performed with seven experienced clinicians to collect feedback on the task trainer’s simulation realism via a Classification Test (CT) and a Disclosed Assessment Test (DAT). In the CT, subjects were able to classify different muscle behaviors with a mean accuracy > 87 % and could further distinguish severity level within each behavior satisfactorily. In the DAT, subjects generally agreed with the simulation realism and provided suggestions on haptic behaviors for future iterations. Overall, subjects scored 4.9 out of 5 for the potential usefulness of this device as a medical education tool for students to learn spasticity and rigidity assessment.

Inertial measurement units (IMUs) are used in biomechanical and clinical applications for quantifying joint kinematics. This study aimed to assist researchers new to IMUs and wanting to develop inexpensive IMU system to estimate the relative angle between IMUs, while understanding the different algorithms for estimating angular kinematics. Thus, there were three sub-goals: 1) to present a low-cost and convenient IMU system utilizing two 6-axis IMUs for computing the relative angle between the IMUs, 2) to examine seven methods for estimating the angular kinematics of an IMU, and 3) to provide open-source code and working principles of these methods. The raw gyroscopic and accelerometer data were pre-processed. The seven methods included gyroscopic integration (GI), accelerometer inclination (AC), Basic Complementary filter (BCF), Kalman filter (KF), Digital Motion Processor (DMPTM, a proprietary algorithm)), Madgwick filter (MW), and Mahony filter (MH). An apparatus was designed to test nine conditions that computed angles for rotation about three axes (roll, pitch, yaw) and three movement speeds (50˚/s, 150˚/s, 300˚/s). Each trial lasted 25 minutes. The root mean squared error (RMSE) between the gold-standard value measured from the apparatus’ encoder and the value calculated from each of the seven method was determined. For roll and pitch, all methods accurately quantified angles (RMSE < 6˚) at all speeds. For yaw, all methods except AC and DMP displayed RMSE < 6˚ at all speeds. AC could not be used for yaw angle computation, and DMP displayed RMSE > 6˚. Researchers can utilize appropriate methods based on their study’s application.