Spasticity is a common consequence of the upper motor neuron syndrome and usually associated with brain lesion, stroke, cerebral palsy, spinal cord injury, and etc. On the other hand, rigidity is a neuromuscular disorder often found in Parkinson’s disease patients. Both of spasticity and rigidity are characterized by abnormal hypertonic muscle behaviors that will cause discomfort and hinder daily activities. Worldwide, the estimated affected population of spasticity is around 12 million [1], and rigidity affects more than 10 million people [2]. Clinical evaluation of spasticity or rigidity involves personal assessment using qualitative scales, such as the Modified Ashworth Scale (MAS) or Modified Tardieu Scale (MTS) for spasticity and Unified Parkinson’s Disease Rating Scale (UPDRS) for rigidity. However, this evaluation method heavily relies on the rater’s personal experience/interpretation and usually results in poor consistency and low reliability. The goal of this design was to develop a quantitative measurement device that can be used to assist clinical evaluation of spasticity or rigidity. This portable device, the Position, Velocity, and Resistance Meter (PVRM), can be strapped around a patient’s limb to measure angular position, angular velocity and muscle resistance of a given joint while the patient’s limb is passively stretched by the clinician. Acquiring this quantitative data from patients will not only allow clinicians to make more reliable assessments but also help researchers gain additional insights into the quantification of spasticity and rigidity.

Patients with neuromuscular disorders such as Parkinson’s disease (PD), traumatic brain or spinal cord injury, or multiple sclerosis (MS) can develop different levels of abnormal muscle behavior (hypertonia) such as rigidity and spasticity [1], [2]. Hypertonia can affect different parts of the body such as upper or lower extremities. Symptoms include pain, increased muscle tone, spasms, and decreased functional abilities. Hypertonia can interfere with many activities of daily living, greatly affecting the quality of life in patients and causing anxiety, depression, and social isolation [2].

<p>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).</p> <p>Potential applications of the TES include physical human-robot interaction (pHRI) for navigating riding or remote robots.</p> <p>The data and data processing code from this study are open source and can be found via the following links:</p> <ul> <li>IEEE DataPort with data: https://ieee-dataport.org/documents/validation-study-torso-dynamics-estimation-system-tes-hands-free-physical-human-robot </li> <li>GitHub repository with code: https://github.com/ssong47/TorsodynamicsEstimationSystem </li> </ul> <p><br></p>

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 (DMP TM , 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.

<p>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).</p> <p>Potential applications of the TES include physical human-robot interaction (pHRI) for navigating riding or remote robots.</p> <p>The data and data processing code from this study are open source and can be found via the following links:</p> <ul> <li>IEEE DataPort with data: https://ieee-dataport.org/documents/validation-study-torso-dynamics-estimation-system-tes-hands-free-physical-human-robot </li> <li>GitHub repository with code: https://github.com/ssong47/TorsodynamicsEstimationSystem </li> </ul> <p><br></p>