Validation of a Wearable Position, Velocity, and Resistance Meter for Assessing Spasticity and Rigidity

Song, Seung Yun; Pei, Yinan; Tippett, Steven R.; Lamichhane, Dronacharya; Zallek, Christopher M.; Hsiao-Wecksler, Elizabeth T.

doi:10.1115/dmd2018-6906

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Second, the test apparatus did not have a moment arm to understand the effect of acceleration artifacts due to offset between the rotation axis and IMU introduced to the system. While our previous studies displayed a similar level of IMU accuracy even when a moment arm was introduced [3], more rigorous testing may be needed to fully evaluate the performances of the computational methods. In cases where the IMUs experience significant motion related disturbances that introduce a large amount of noise to IMUs, KF, MW, MH may perform better than other methods since KF, MW, MH not only fuse multiple sensor readings, but also removes the effect of these external accelerations by updating the gains of the filter every time step (KF) or using a gradient descent-based algorithm (MW) or a PI compensator (MH).…”

Section: E Other Limitations and Future Workmentioning

confidence: 84%

“…A total of nine test trials (3 rotation axes × 3 movement speeds) with each trial length of 25 minutes and range of motion of 180° was performed. 25 minutes was chosen as the test length since previous studies involving clinical research of neuro-rehabilitation lasted approximately 25 minutes for each test subject [3], [23]- [25]. The range of motion was set to 180° since most anatomical joints do not exceed 180°.…”

Section: B Testing Protocolmentioning

confidence: 99%

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Estimating Relative Angles Using Two Inertial Measurement Units Without Magnetometers

2022

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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.

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Section: E Other Limitations and Future Workmentioning

confidence: 84%

Section: B Testing Protocolmentioning

confidence: 99%

Estimating Relative Angles Using Two Inertial Measurement Units Without Magnetometers

2022

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“…In terms of form factors, prior measurement schemes strapped sensors on the patient ( Le-Ngoc and Jansse, 2012 ; Li et al., 2017 ; Ferreira et al., 2013 ; Lee et al., 2017 ; Seth et al., 2015 ; Wu et al., 2018 ; Song et al., 2018 ; Bar-On et al., 2013 ); in contrast, our design puts the equipment on the evaluator instead. Here, the sensor glove can be used to assess both arms and legs as shown in Figures 1 B and 1C.…”

Section: Resultsmentioning

confidence: 99%

“…Yet this method does not address the motion-dependent aspects of spasticity. Alternatively, biomechanical measurement tools including myometer and dynamometer were demonstrated to quantify the resistance torque of spastic limbs ( Le-Ngoc and Jansse, 2012 ; Li et al., 2017 ; Ferreira et al., 2013 ; Lee et al., 2017 ; Seth et al., 2015 ; Wu et al., 2018 ; Song et al., 2018 ). Multimodal approaches that combine movement and muscle resistance measurements have been developed to examine the velocity-dependent characteristics of spastic muscles ( Wu et al., 2018 ; Bar-On et al., 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Multimodal assessment of spasticity using a point-of-care instrumented glove to separate neural and biomechanical contributions

Amit¹,

Yalçın²,

Liu³

et al. 2022

iScience

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Flexible Pressure Sensors for Objective Assessment of Motor Disorders

Amit

Chukoskie

Skalsky

et al. 2019

Adv Funct Materials

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Monitoring body motion is relevant to motor control disorders as well as assessment of fine motor skills in child development. Furthermore, motion tracking is necessary for rehabilitation monitoring and injury prevention and benefits both sick and healthy individuals. Flexible pressure sensors based on resistors, capacitors, inductors, or transistors are reviewed in the context of healthcare measurements, ranging from physiological signals to body movement characteristics such as grip and gait. To demonstrate the use of flexible pressure sensors for motor assessment, a touch sensing glove that evaluates fine motor skills in autism research is developed. The results show that autistic children perform fewer taps per minute compared to typically developing children, with larger variations in tap durations. In a second example, a force and motion sensing glove is developed to assess spasticity, a neuromuscular disorder that causes muscle stiffness/resistance and jerky movement. Analyses of force versus velocity show movement-dependent muscle resistance in a patient with spasticity. Through these flexible sensor systems, the shift from subjective scores to objective measurement will promote better diagnosis and dramatically improve the accuracy in tracking patient response to therapy.patients doing certain movements, and then clinicians rank each patient's level according to qualitative descriptions in benchmark classification scales. [12,13] The subjective scores can be inconsistent between raters and do not capture finelevel changes in a patient's progress in response to therapy. Therefore, there is a critical need to tackle the issue of imprecise assessment of motor disorders.With recent advances in flexible sensors and innovations in tactile sensing, [14][15][16][17][18][19] we have new low-cost technologies that are prime to facilitate quantitative evaluation of motor control. This progress report presents current developments in wearable sensor systems applicable to motor disorders, so that consistent, objective metrics become available to accurately track whether a treatment effectively relieves symptoms. The wearable sensors are not limited to placement on patients but can also be worn by clinicians or caregivers to assist them in taking measurements during patient interactions. [20,21] The point-of-care sensor systems will allow frequent monitoring, which is highly desirable to offer a better understanding of the patient's short and long-term response to therapies, to tailor treatment and improve outcome and quality of life for patients.Other reviews [14][15][16][17][18][19] have already extensively covered the flexible materials and devices used in tracking vital signs and electronic skin applications. In light of that, this progress report focuses on the applications in monitoring motor skills, in particular to implement pressure sensor designs for wearable systems with diverse form factors, for instance, gloves, [20,22,23] epidermal tags tags, [24,25] and shoe insoles. [26,27] We will discuss the mechanis...

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Validation of a Wearable Position, Velocity, and Resistance Meter for Assessing Spasticity and Rigidity

Cited by 7 publications

References 10 publications

Estimating Relative Angles Using Two Inertial Measurement Units Without Magnetometers

Estimating Relative Angles Using Two Inertial Measurement Units Without Magnetometers

Multimodal assessment of spasticity using a point-of-care instrumented glove to separate neural and biomechanical contributions

Flexible Pressure Sensors for Objective Assessment of Motor Disorders

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