Neuromechanics and Control of Physical Behavior: from Experimental and Computational Formulations to Bio-inspired Technologies

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Review
18 October 2017

Musculoskeletal tissues respond to optimal mechanical signals (e.g., strains) through anabolic adaptations, while mechanical signals above and below optimal levels cause tissue catabolism. If an individual's physical behavior could be altered to generate optimal mechanical signaling to musculoskeletal tissues, then targeted strengthening and/or repair would be possible. We propose new bioinspired technologies to provide real-time biofeedback of relevant mechanical signals to guide training and rehabilitation. In this review we provide a description of how wearable devices may be used in conjunction with computational rigid-body and continuum models of musculoskeletal tissues to produce real-time estimates of localized tissue stresses and strains. It is proposed that these bioinspired technologies will facilitate a new approach to physical training that promotes tissue strengthening and/or repair through optimal tissue loading.

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52 citations
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27 citations
Original Research
31 August 2017

Muscle synergy analysis (MSA) is a mathematical technique that reduces the dimensionality of electromyographic (EMG) data. Used increasingly in biomechanics research, MSA requires methodological choices at each stage of the analysis. Differences in methodological steps affect the overall outcome, making it difficult to compare results across studies. We applied MSA to EMG data collected from individuals post-stroke identified as either responders (RES) or non-responders (nRES) on the basis of a critical post-treatment increase in walking speed. Importantly, no clinical or functional indicators identified differences between the cohort of RES and nRES at baseline. For this exploratory study, we selected the five highest RES and five lowest nRES available from a larger sample. Our goal was to assess how the methodological choices made before, during, and after MSA affect the ability to differentiate two groups with intrinsic physiologic differences based on MSA results. We investigated 30 variations in MSA methodology to determine which choices allowed differentiation of RES from nRES at baseline. Trial-to-trial variability in time-independent synergy vectors (SVs) and time-varying neural commands (NCs) were measured as a function of: (1) number of synergies computed; (2) EMG normalization method before MSA; (3) whether SVs were held constant across trials or allowed to vary during MSA; and (4) synergy analysis output normalization method after MSA. MSA methodology had a strong effect on our ability to differentiate RES from nRES at baseline. Across all 10 individuals and MSA variations, two synergies were needed to reach an average of 90% variance accounted for (VAF). Based on effect sizes, differences in SV and NC variability between groups were greatest using two synergies with SVs that varied from trial-to-trial. Differences in SV variability were clearest using unit magnitude per trial EMG normalization, while NC variability was less sensitive to EMG normalization method. No outcomes were greatly impacted by output normalization method. MSA variability for some, but not all, methods successfully differentiated intrinsic physiological differences inaccessible to traditional clinical or biomechanical assessments. Our results were sensitive to methodological choices, highlighting the need for disclosure of all aspects of MSA methodology in future studies.

9,435 views
60 citations
Methods
09 August 2017
A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion
Nicholas S. Szczecinski
1 more and 
Roger D. Quinn

A dynamical model of an animal’s nervous system, or synthetic nervous system (SNS), is a potentially transformational control method. Due to increasingly detailed data on the connectivity and dynamics of both mammalian and insect nervous systems, controlling a legged robot with an SNS is largely a problem of parameter tuning. Our approach to this problem is to design functional subnetworks that perform specific operations, and then assemble them into larger models of the nervous system. In this paper, we present networks that perform addition, subtraction, multiplication, division, differentiation, and integration of incoming signals. Parameters are set within each subnetwork to produce the desired output by utilizing the operating range of neural activity, R, the gain of the operation, k, and bounds based on biological values. The assembly of large networks from functional subnetworks underpins our recent results with MantisBot.

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49 citations
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34 citations
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Original Research
06 June 2017

Muscle synergies calculated from electromyography (EMG) data identify weighted groups of muscles activated together during functional tasks. Research has shown that fewer synergies are required to describe EMG data of individuals with neurologic impairments. When considering potential clinical applications of synergies, understanding how EMG data processing impacts results and clinical interpretation is important. The aim of this study was to evaluate how EMG signal processing impacts synergy outputs during gait. We evaluated the impacts of two common processing steps for synergy analyses: low pass (LP) filtering and unit variance scaling. We evaluated EMG data collected during barefoot walking from five muscles of 113 children with cerebral palsy (CP) and 73 typically-developing (TD) children. We applied LP filters to the EMG data with cutoff frequencies ranging from 4 to 40 Hz (reflecting the range reported in prior synergy research). We also evaluated the impact of normalizing EMG amplitude by unit variance. We found that the total variance accounted for (tVAF) by a given number of synergies was sensitive to LP filter choice and decreased in both TD and CP groups with increasing LP cutoff frequency (e.g., 9.3 percentage points change for one synergy between 4 and 40 Hz). This change in tVAF can alter the number of synergies selected for further analyses. Normalizing tVAF to a z-score (e.g., dynamic motor control index during walking, walk-DMC) reduced sensitivity to LP cutoff. Unit variance scaling caused comparatively small changes in tVAF. Synergy weights and activations were impacted less than tVAF by LP filter choice and unit variance normalization. These results demonstrate that EMG signal processing methods impact outputs of synergy analysis and z-score based measures can assist in reporting and comparing results across studies and clinical centers.

9,009 views
95 citations
Displacement, forces, and normalized |k|–position profiles. One representative trial is shown for the dyad from the visual feedback group. The displacement–position profile is shown in panel (A). The force–position profile (quiver plot) is shown in panel (B). The normalized |k|–position profile is shown in panel (C). The selected trial is representative of correct response.
5,431 views
44 citations
Visuomotor force tracking paradigm for a representative subject. (A) A small compliant spring is compressed between the index finger and thumb. Fingertip forces and bipolar surface EMG from the intrinsic hand muscles FDI and APB (not shown) were recorded. (B) Force tracking task showing three random presentations during spring compression at forces of 1.0 and 2.1 N. Each force level (high or low) was presented to the subject for 30 s with an inter-stimulus interval of 5 s. The blue trace represents the recorded compression force. Red dotted lines indicate the ±15% tolerance limits relative to target force. Data that are within the tolerance limits for a minimum of 5 s in duration (shaded regions) were used in the coherence analysis. In the fourth hold, the compression force briefly fell out of the tolerance range. (C) The visuomotor task is repeated with a stable wooden dowel. (D) Force tracking profile with the dowel at the same force levels and tolerance limits as with the spring. Gray areas represent data within tolerance and dowel-low and dowel-high refer to dowel-low and dowel-high, respectively.
6,796 views
75 citations
Original Research
14 March 2017
Response trends. The responses of the model and human subjects in all five disturbance experiments are shown. Human responses (gray lines) are normalized with respect to their maximum value in each experiment and for each muscle. The model responses (black lines) are linearly scaled to place as many of the responses as possible within ±1 s.d. of the human responses (gray shaded area). The % of the model responses within ±1 s.d. of human responses are shown at the top of each graph, and those which are ≤50% are marked with *.
6,841 views
51 citations
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