1. Archives of Physical Medicine and Rehabilitation
Artificial Neural Network Learns Clinical Assessment of Spasticity in Modified Ashworth Scale 
Jeong-Ho Park, MSc, Yushin Kim, PT, PhD, Kwang-Jae Lee, MD, PhD, Yong-Soon Yoon, MD, PhD, Si Hyun Kang, MD, PhD, Heesang Kim, MD, Hyung-Soon Park, PhD 
Archives of Physical Medicine and Rehabilitation 
DOI: /j.apmr
Copyright © 2019 American Congress of Rehabilitation Medicine Terms and Conditions

2. Archives of Physical Medicine and Rehabilitation DOI: (10.1016/j.apmr.2019.03.016)
Copyright © 2019 American Congress of Rehabilitation Medicine Terms and Conditions

3. Fig 1
Spasticity assessment using the manual spasticity evaluator. The rater extends the subject’s elbow joint while holding the handle of the device. The angle sensor aligned to the subject’s elbow joint measures angle of the joint and the force sensor beneath the handle (area surrounded by a dotted line) measures the force applied to the subject. The device is fastened to the subject’s upper arm, forearm, and hand using fabric straps.
Archives of Physical Medicine and Rehabilitation DOI: ( /j.apmr )
Copyright © 2019 American Congress of Rehabilitation Medicine Terms and Conditions

4. Fig 2
Nine biomechanical parameters to quantify the spastic response. (A) PROM and resistance related parameters (slope of linear regression of the resistance with respect to the joint angle, magnitude of catch, resistance after catch, and maximum resistance during the passive stretching); (B) speed related parameters, maximum stretching speed; (C) acceleration related parameters, angle of catch based on the maximum deceleration; (D) mechanical power related parameters, angle of catch based on the local minimum of the mechanical power and local minimum of mechanical power; (E) detailed explanation on calculation of resistance after catch and magnitude of catch.
Archives of Physical Medicine and Rehabilitation DOI: ( /j.apmr )
Copyright © 2019 American Congress of Rehabilitation Medicine Terms and Conditions

5. Fig 3
Distribution of biomechanical parameters of trials classified as each MAS score. Normalized values are shown. Zero level implies the mean value of each parameter. Dots indicate average values of each MAS score. Vertical lines show 1 SD. (A) angle of catch based on the maximum deceleration; (B) magnitude of catch; (C) resistance after catch; (D) PROM; (E) maximum resistance during the passive stretching; (F) maximum stretching speed; (G) angle of catch based on the local minimum of the mechanical power; (H) local minimum of mechanical power; (I) slope of linear regression of the resistance with respect to the joint angle. ∗For some MAS3 trials that catch was not clearly observed, angle of catch based on the maximum deceleration, angle of catch based on the local minimum of the mechanical power, magnitude of catch, resistance after catch, and local minimum of mechanical power were set to be extreme values (eg, -10 or 10).
Archives of Physical Medicine and Rehabilitation DOI: ( /j.apmr )
Copyright © 2019 American Congress of Rehabilitation Medicine Terms and Conditions

6. Fig 4
The average relevance of biomechanical parameters and biases to the decision of each MAS score. Red and blue colors indicate positive average relevance (relevant for the prediction) and negative average relevance (against the prediction), respectively. Overall relevance is defined as the sum of the average relevance values over the MAS scores. Relevance values of angle of catch based on the maximum deceleration, magnitude of catch, resistance after catch, angle of catch based on the local minimum of the mechanical power, and local minimum of mechanical power for MAS3 (colored in gray) were excluded from the calculation of overall relevance. ∗The average relevance value is not significantly different from 0 according to 1 sample t test (statistical significance: 5%).
Archives of Physical Medicine and Rehabilitation DOI: ( /j.apmr )
Copyright © 2019 American Congress of Rehabilitation Medicine Terms and Conditions