A novel wheelchair called PURE (Personalized Unique Rolling Experience) that uses hands-free (HF) torso lean-to-steer control has been developed for manual wheelchair users (mWCUs). PURE addresses limitations of current wheelchairs, such as the inability to use both hands for life experiences instead of propulsion. PURE uses a ball-based robot drivetrain to offer a compact, self-balancing, omnidirectional mobile device. A custom sensor system converts rider torso motions into direction and speed commands to control PURE, which is especially useful if a rider has minimal torso range of motion. We explored whether PURE’s HF control performed as well as a traditional joystick (JS) human-robot interface and mWCUs, performed as well as able-bodied users (ABUs). 10 mWCUs and 10 ABUs were trained and tested to drive PURE through courses replicating indoor settings. Each participant adjusted ride sensitivity settings for both HF and JS control. Repeated-measures MANOVA tests suggested that the number of collisions, completion time, NASA TLX scores except physical demand, and index of performances were similar for HF and JS control and between mWCUs and ABUs for all sections. This suggests that PURE is effective for controlling this new omnidirectional wheelchair by only using torso motion thus leaving both hands to be used for other tasks during propulsion.Link for supplementary material (appendix, short descriptive video): https://uofi.box.com/s/hbrs0bbbxez4hr51potzywy3p9iupryu

This is a submitted (IEEE RO-MAN 2023) draft of a paper on the testing of a hands-free human-robot interaction for navigating a ballbot in a virtual environment. In this study, able-bodied users and manual wheelchair users controlled a virtual ballbot using a hands-free (HF) lean-to-steer control concept that uses torso motions. A custom sensor system (i.e., Torso-dynamics Estimation System (TES)) was utilized to measure and convert the dynamics of the rider’s torso motions into commands to provide HF control of the robot. A simulation study was conducted to explore the efficacy of the HF controller compared to a traditional joystick (JS) controller, and whether there were differences in performance by manual wheelchair users (mWCUs), who may have reduced torso function, compared to able-bodied users (ABUs). Twenty test subjects (10 mWCUs + 10 ABUs) used the subject-specific adjusted TES while wearing a virtual reality headset and were asked to navigate a virtual human rider on the ballbot through obstacle courses replicating seven indoor environment zones. (Link for the published paper: https://ieeexplore.ieee.org/abstract/document/10309327)(Link for a short descriptive video: https://youtu.be/zYZ_Q4DhFKw)

This is a submitted draft of a paper on the design and validation of a Torso-dynamics Estimation System (TES). The TES consisted of a Force Sensing Seat (FSS) and an inertial measurement unit (IMU) that measured the kinetics and kinematics of the subject’s torso motions. The FSS estimated the 3D forces, 3D moments, and 2D COPs while the IMU estimated the 3D torso angles. To validate the TES, the FSS and IMU estimates were compared to gold standard research equipment (AMTI force plate and Qualisys motion capture system, respectively). Potential applications of the TES include physical human-robot interaction (pHRI) for navigating riding or remote robots. The data and data processing code from this study are open source and can be found via the following links: IEEE DataPort with data: https://ieee-dataport.org/documents/validation-study-torso-dynamics-estimation-system-tes-hands-free-physical-human-robot  GitHub repository with code: https://github.com/ssong47/TorsodynamicsEstimationSystem Below is the link for the published article:https://ieeexplore.ieee.org/abstract/document/10309422Below is the link for a short descriptive video: https://youtu.be/qYzhmZ6I47A

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.