Neurophysiological validation of simultaneous intrinsic and reflexive joint impedance estimates

Abstract

Background People with brain or neural injuries, such as cerebral palsy or spinal cord injury, commonly have joint hyper-resistance. Diagnosis and treatment of joint hyper-resistance is challenging due to a mix of tonic and phasic contributions. The parallel-cascade (PC) system identification technique offers a potential solution to disentangle the intrinsic (tonic) and reflexive (phasic) contributions to joint impedance, i.e. resistance. However, a simultaneous neurophysiological validation of both intrinsic and reflexive joint impedances is lacking. This simultaneous validation is important given the mix of tonic and phasic contributions to joint hyper-resistance. Therefore, the main goal of this paper is to perform a group-level neurophysiological validation of the PC system identification technique using electromyography (EMG) measurements. Methods Ten healthy people participated in the study. Perturbations were applied to the ankle joint to elicit reflexes and allow for system identification. Participants completed 20 hold periods of 60 seconds, assumed to have constant joint impedance, with varying magnitudes of intrinsic and reflexive joint impedances across periods. Each hold period provided a paired data point between the PC-based estimates and neurophysiological measures, i.e. between intrinsic stiffness and background EMG, and between reflexive gain and reflex EMG. Results The intrinsic paired data points, with all subjects combined, were strongly correlated, with a range of $$r = [0.87\ 0.91]$$ r = [ 0.87 0.91 ] in both ankle plantarflexors and dorsiflexors. The reflexive paired data points were moderately correlated, with $$r = [0.64\ 0.69]$$ r = [ 0.64 0.69 ] in the ankle plantarflexors only. Conclusion An agreement with the neurophysiological basis on which PC algorithms are built is necessary to support its clinical application in people with joint hyper-resistance. Our results show this agreement for the PC system identification technique on group-level. Consequently, these results show the validity of the use of the technique for the integrated assessment and training of people with joint hyper-resistance in clinical practice.

Reducing the Soleus Stretch Reflex With Conditioning: Exploring Game- and Impedance-Based Biofeedback

“…A weak withinblock correlation (r = 0.31) of the EMG-and impedancebased reflexive biofeedback was found based on 6 s data segments. Oppositely, for longer segments a moderate acrossblock correlation was found (r = 0.68; 7.5 min segments) and reported previously (r = 0.69; 60 s segments) (17). This difference between the correlation of short and long segments is likely related to the inherent 15 s risetime of the impedance estimation algorithm (14).…”

“…First, withinblock correlation was investigated via a within-block Z-score standardization of all 75 data pairs for all 99 blocks (33 blocks per participants). The Z-score standardization allows to combine all data across-blocks and across-subjects before computing the correlation (17). Second, the across-block correlation was investigated by using the mean of 75 data pairs per block and using a within-subject Z-score standardization to combine data across-subjects.…”

“…For the Impedance group, continuous dorsiflexion perturbations were used to elicit a stretch reflex (17). These ramp-and-hold perturbations had an 2 • amplitude, 125 • /s max., velocity, 15,800 • /s 2 max., and acceleration and 40 ms duration, see Figure 1C.…”

“…Reflexive joint impedance was estimated using a parallelcascade identification algorithm outlined in van 't Veld et al (17), see Figure 1E. In short, using the recorded torques and kinematics the algorithm first estimates the intrinsic impedance parameters: inertia I, damping B, and stiffness K. These parameters capture the joint resistance in response to the mechanical perturbations from the tissue-related, non-neural origin, and tonic neural origin.…”

“…SOL and TA background activity was defined as the smoothed (moving average, 100 ms window) rectified EMG (6,7). During trials with continuous perturbations, background torque was used instead of SOL EMG, and this background activity was computed using low-pass filters (2nd-order, 0.1 Hz, Butterworth {TA}; critically damped {torque}) to reduce the influence of these perturbations (17).…”