Understanding the capacity of children with congenital unilateral below-elbow deficiency to actuate their affected muscles

doi:10.21203/rs.3.rs-3340949/v1

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Understanding the capacity of children with congenital unilateral below-elbow deficiency to actuate their affected muscles

https://doi.org/10.21203/rs.3.rs-3340949/v1

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published 24 Feb, 2024

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In recent years, commercially available dexterous upper limb prostheses for children have begun to emerge. These devices derive control signals from surface electromyography (measure of affected muscle electrical activity, sEMG) to drive a variety of grasping motions. However, the ability for children with congenital upper limb deficiency to actuate their affected muscles to achieve naturalistic prosthetic control is not well understood, as compared to adults or children with acquired hand loss. To address this gap, we collected sEMG data from 9 congenital one-handed participants ages 8–20 years as they envisioned and attempted to perform 10 different movements with their missing hands. Seven sEMG electrodes were adhered circumferentially around the participant's affected and unaffected limbs and participants mirrored the attempted missing hand motions with their intact side. To analyze the collected sEMG data, we used time and frequency domain analyses. We found that for the majority of participants, attempted hand grasps produced detectable and consistent muscle activity, and the capacity to achieve this was not dissimilar across the affected and unaffected sides. These data suggest that children with congenital hand absence retain a degree of control over their affected muscles, which has important implications for translating and refining advanced prosthetic control technologies for children.

Approximately 1 in 500 live births will present with an upper limb deficiency¹, which is the most common reason for limb absence in children². Among those born with upper limb deficiencies, children with unilateral congenital below-elbow deficiency (UCBED) will most typically present with limb characteristics amenable to prosthesis prescription. Although there are a variety of upper limb prostheses available to these children, they are regularly abandoned with 35–45% of prescribed devices not being used³. In fact, these devices often fall short of meeting the wearers’ needs and typically don’t provide sufficient function and/or improve quality of life⁴. Encouragingly, dexterous prostheses that resemble the form and function of intact hands are becoming widely available for adults and more recently for children^5,6. These devices achieve a variety of grasping movements and as a result, hold the potential to offer additional functional benefits. However, limited research has been performed to address and refine these systems for the unique challenges and demands of children.

With increased prosthetic dexterity comes the need for more sophisticated control systems to manage the newly available function. State-of-the-art control systems for adult prostheses use machine learning to predict the user’s motor intent from patterns of muscle electrical activity (surface electromyography, sEMG) and map these predictions to corresponding prosthetic movements^7–10. Despite promising results in adults with acquired amputation, few studies have been performed in those with congenital limb absence. One study recruited N = 4 adults with congenital limb absence and demonstrated limited success, applying sEMG and machine learning to predict 11 hand movements and finding classification accuracies of 52.1% $\pm$ 15.0%¹¹. Additionally, in a cohort of children (N = 5, < 21 years old) and adults (N = 2) with UCBED, a commercially available control system was used to predict missing limb movements¹². Such systems have been primarily designed for those with acquired limb loss and have yet to be refined for individuals with congenital limb differences. Only 2 of the 5 children demonstrated a classification accuracy greater than 80% for a limited repertoire of only 3 degrees of freedom¹². Of these two publications, one studied and recruited children in a limited setting; however, an adult-specific prosthesis control system was employed which is unlikely to be directly applicable to children¹³. Collectively, these limitations restrict the translation of their findings to a more comprehensive understanding of affected muscle activity in children with UCBED.

The translation of advanced sEMG techniques to decode motor intent and control prostheses for children with UCBED requires a more thorough understanding of the capabilities of their affected muscles. sEMG characteristics such as root mean squared (RMS), moving average, linear envelope, mean frequency (MNF), and median frequency are often used to investigate the biological control an individual has over their limb(s)^14–17. These characteristics have yet to be investigated in children born with limb absence and therefore the capacity for these children to actuate their affected muscles is unknown.

In this work, we investigated RMS and MNF characteristics of children’s affected and unaffected-contralateral muscles while they attempted to perform a variety of hand grasping movements. Measures of within-limb and across-limb consistency and distinguishability were evaluated. We hypothesized that although less pronounced when compared to their unaffected limb, children born with UCBED will exhibit a measurable degree of control over their affected forearm musculature as they attempt to perform movements with their missing hands.

2.1 Participants

Nine participants (8 male, 1 female) with UCBED aged 8 to 20 years old participated in this study (mean = 14 years; SD = 4.4 years). Research protocols were approved by the Institutional Review Board at the Shriners Children’s Hospital – Northern California and were performed in accordance with the relevant guidelines and regulations. Participants provided written informed assent and/or their legal guardians provided written informed consent. Participant details can be found in Table 1.

Table 1

Demographic information for participants with unilateral congenital below-elbow deficiency.
Subject ID	Age	Sex	Affected Limb	Limb Length (cm)		Limb Circumference (cm)		Prosthesis Use
Subject ID	Age	Sex	Affected Limb	Right	Left	Right	Left	Prosthesis Use
SHR-A	20	Male	Left		13		15	PA
SHR-B	8	Male	Right	14	16	20	21.5	PA
SHR-C	11	Male	Right	18	21	18	20	None
SHR-D	9	Male	Left	21.5	12.5	22.5	18.5	None
SHR-E	18	Male	Right	15	28	21	23	BP†
SHR-F	16	Female	Left	26.5	11.5	26.5	23.5	PA
SHR-G	19	Male	Left	26.5	13	25.5	21.5	Myo & PA
SHR-H	14	Male	Left	25	8	26	23	BP
SHR-I	12	Male	Right	10	24	21	24	BP
PA = Passive device.
BP = Body-powered device.
Myo = Myoelectric device.
†Activity specific device.

Insert Table 1

2.2 Data collection

A 16-Channel Delsys Trigno surface EMG System (Delsys, Natick, USA) was used to capture affected and unaffected forearm muscle activity. The system consisted of wireless Trigno Mini Sensors with an inter-electrode spacing of 10 mm and dual on-board differential references. The sEMG signal input was set to a range of $\pm$11mV and the data were bandpass filtered at 20 Hz to 450 Hz^18,19. A National Instruments USB-6210 data acquisition system (National Instruments Corp., Austin, USA) sampled sEMG data at 6,000 Hz and stored data in a MATLAB (MathWorks, Inc., Natick, USA) file structure for postprocessing and analysis. Additionally, a Logitech C922 high-definition camera (Logitech International S.A., Lausanne, Switzerland) sampled at 15 frames per second was synchronized with the sEMG data.

2.3 Experiment

We first performed “conceptual training” with each participant in which we introduced our experimental equipment, described terminology used during testing, provided a simple overview of sEMG, and allowed them to familiarize themselves²⁰. After, we adhered the sEMG electrodes to their skin over the affected and unaffected forearm muscles using double-sided adhesive. The participants were asked to contract their affected limb as if they were making a fist while we palpated to find where the superficial muscles on their ventral side were most firm and we placed the first sEMG electrode there. If participants had a small residual limb length, less than about 10 cm, then the first sEMG electrode was placed in the center of their residual limb starting on the ventral side. In either case, the remaining sEMG electrodes were placed around the circumference of the participants’ forearm muscles following the typical circumferential equidistant protocol^21–23. This procedure was repeated in the unaffected limb and electrodes were placed on the most proximal two-thirds of the forearm musculature (Fig. 1). As reported in prior work, 7 sEMG electrodes were used on each side²⁴ (with the exception of SHR-A’s affected limb where 4 sEMG electrodes were adhered due to size limitations).

Insert Fig. 1

Participants were then informed of which hand grasp or movement was to be performed and were allowed to familiarize themselves before proceeding to data collection. A strategic set of 10 hand grasps and movements were selected. For simplicity, we will refer to these as hand grasp configurations, which include commonly used hand grasps in daily living²⁵ and movements: index flexion (IF), key pinch (KP), pulp pinch (PP), index point (IP), cylindrical wrap (CW), cylindrical wrap wrist rotate (CR), tripod pinch (TP), wrist extension (WE), wrist flexion (WF), and wrist rotation (WR) (Fig. 2a). To ensure rich data sets for analysis of the muscle activity and due to the potential cognitive demands of attempting to move their missing limb, 10 repetitions of each movement were performed. Participants were tasked with envisioning and attempting the prompted grasp configuration with their affected limb while simultaneously mirroring this action with their unaffected limb. Participants performed the 10 repetitions across 2 trials where a single trial began with the participant in the relaxed phase for 4 seconds followed by 3 seconds of contraction as indicated by an auditory tone. The relaxation (no tone) and contraction (tone) phases were repeated 5 times each in a single trial ([5 relaxations and contractions] * 2 trials = 10 relax and contract repetitions for each hand movement). All participants were provided multiple opportunities for rest to mitigate fatigue. The structure of the experimental trials is illustrated in Fig. 2b.

Insert Fig. 2

2.4 sEMG pre-processing

A program was written in MATLAB to pre-process (condition) the data collected from the sEMG electrodes by first concatenating data across the 10 repetitions for individual grasps. Additionally, to remove the effect of participant reaction times (i.e., from when they first hear the tone to when they contract their muscles), 15% of the data over the relevant time interval (contraction/relaxation) was discarded from both the onset and recession of the grasp²⁷.

We first performed analysis across sEMG channels, defined as the time-series voltage data produced from an individual sEMG electrode. From each channel we assessed RMS and MNF characteristics, features that are commonly implemented and are sensitive to capturing the biological phenomenon of muscle excitation during grasp movements^15,28,29. Prior to deriving the RMS of each channel, the sEMG data was first full-wave rectified [9], while the MNF was derived from the raw conditioned sEMG data. Then for a given limb, grasp, and sEMG channel the RMS and MNF were calculated with equations (1) and (2). Where ${x}_{i}$ is the $i$-th of $N$ samples from a single conditioned contraction (3s) or relaxation (4s) repetition (e.g., $N$= 6,000Hz * 3s * 0.7 = 12,600 samples for contraction)³⁰. Additionally, ${f}_{j}$ and ${p}_{j}$ are the frequency variable and power spectrum at a given bin $j$ of $M$ frequency bins^30,31.

$$RMS= \sqrt{\frac{\sum _{i=1}^{N}{\left|{x}_{i}\right|}^{2}}{N}}$$

$$MNF= \frac{\sum _{j=1}^{M}{f}_{j}{p}_{j}}{\sum _{j=1}^{M}{p}_{j}}$$

For a given limb, the RMS and MNF were normalized to their corresponding maximum across grasps, on an individual channel basis. All data presented here is therefore a percentage of the maximum characteristic per channel.

RMS and MNF characteristics were evaluated in both limbs. This included muscle excitation visualizations, investigating within-grasp consistency with Kendall’s Coefficient of Concordance^32,33 (see below for more details), and performing dissimilarity analysis to understand the distinguishability of hand grasp configurations. Furthermore, comparisons of hand grasp consistency and the relatedness of grasp characteristics across limbs with the RDM condition-label randomization analysis³⁴ were conducted. All analyses were performed on an individual participant basis.

3.1 Muscle excitation

3.1.1 Visualization

The visualization of muscle excitation across hand grasp configurations and limbs was done to observe patterns in muscle activity across sEMG channels for RMS and MNF characteristics. First, each sEMG channel for each grasp was checked for normality with the Shapiro-Wilk Test (due to the limited number of samples)³⁵. The majority of the data was found to not be normally distributed. Therefore, all data were plotted using box and whisker plots for each sEMG channel and grasp which included the relaxation phase.

3.1.2 Within grasp consistency

To understand the degree to which muscle excitation during individual grasps were reproducible, measures of consistency were evaluated. We quantified the within-grasp consistency given the RMS and MNF muscle excitation across sEMG channels for participants each time the same grasp was performed. Due to the nonparametric nature of the RMS and MNF data, the within-grasp consistency was determined with Kendall’s Coefficient of Concordance, W^32,33. This test produces a value of agreement from the ranked order of the 10 repetitions for RMS and MNF characteristics across the number of entities (sEMG channels). Therefore, Kendall’s W provides a measure of agreement (with 0 indicating no agreement and 1 indicating complete agreement) in muscle excitation across sEMG channels (i.e., reproducibility of muscle excitation patterns.) The strength of agreement/consistency adapted from^36–38 are defined as: W < 0.20 (poor consistency), 0.20 ≤ W < 0.40 (minimal consistency), 0.40 ≤ W < 0.60 (weak consistency), 0.60 ≤ W < 0.80 (moderate consistency), and W ≥ 0.80 (strong consistency).

3.2 Across grasp dissimilarity

To examine the distinguishability of muscle response when children with UCBED attempted to perform each hand grasp configuration, the spread (given by the interquartile range (IQR)) and median amplitude of both RMS and MNF characteristics were analyzed. In literature, representational geometry is often applied to quantify the distinguishability in measures of physiological activity (e.g., sEMG, EEG, fMRI)^26,34,39 when participants are presented with different stimuli. Here we applied and adapted these techniques to understand the distinguishability of muscle excitation (sEMG channels) by treating the hand grasp configurations as the stimuli. We illustrated this distinguishability through a qualitative visual representation.

To perform these analyses, a split-data representational dissimilarity matrix (sdRDM) was produced and analyzed. The data generated from performing a hand grasp multiple times was first split into two equal data sets containing even and odd repetitions. Then the median and IQR of the RMS and MNF were obtained for each data set on a per-channel basis. Each entry of the sdRDM represented the rank-correlation distance between two data sets for every pair of hand grasp configurations across the sEMG channels. This distance was calculated from Kendall’s Tau-b (${\tau }_{b}$) rank correlation coefficient, to account for ties, and is defined as (1 - ${\tau }_{b}$). Kendall’s correlation coefficient was used to accommodate the fact that our data sets were not normally distributed when checked with the Shapiro-Wilk Test³⁵. Here the “objects” of Kendall’s correlation coefficient test were defined as the median or IQR of the RMS or MNF for each sEMG channel (depending on which measure was being tested), and the two “variables” were any two pairs of hand grasp configurations. Additionally, a representational dissimilarity matrix (RDM) was produced as described above, without splitting the data into two datasets, and multidimensional scaling (MDS) was applied for visualization as adapted from⁴⁰. A depiction of the dissimilarity dataflow as described above is given in Fig. 3.

Insert Fig. 3

To determine a distinguishable structure of hand grasp configurations across the characteristics, the non-parametric RDM-level condition-label randomization test for exemplar discriminability index (EDI) was performed on the sdRDMs⁴¹ (hereby referred to as the sdRDM analysis). The EDI was defined as the average between-exemplar dissimilarity estimate (the sdRDM off-diagonals) minus the average within-exemplar dissimilarity estimate (the sdRDM diagonal): EDI = mean(between-exemplar dissimilarity) – mean(within-exemplar dissimilarity), as adapted from⁴¹. A larger EDI was an indication that the hand grasp configurations may be more discriminable, and alternatively, smaller EDI values suggested hand grasp configurations are less discriminable. The sdRDM analysis consisted of simulating the null distribution by performing an exhaustive permutation of the rows of the sdRDM and calculating the EDI for every permutation. Therefore, we define the null hypothesis H₀ as there being no structure to the data and pairwise distances of hand grasp configurations being equal (i.e., the intra-grasp and inter-grasp dissimilarities can be shuffled without change to the sdRDM structure). The EDI of the unshuffled sdRDM was then calculated and used to estimate the p-value by a proportion, i.e., by taking the EDIs in the null distribution that were greater than the unshuffled EDI. A significance level of α = 0.05 was used. A p-value less than this threshold would indicate that there is significant structure in the data and that pairwise distances of hand grasp configurations are not equal. This would suggest that as participants performed missing hand grasp configurations the muscle excitation derived from these movements were distinguishable from one another.

To qualitatively visualize the distinguishability across hand grasp configurations, the RDM constructed from both data sets (i.e., unsplit data) was used and multidimensional scaling (MDS) was applied (Fig. 3). MDS is a dimensionality reduction technique with the objective of reflecting the dissimilarity between items by projecting their distances in a lower dimensional space³⁹. Nonmetric MDS was applied to the dissimilarity matrices (RDMs) through the MATLAB function mdscale with the squared stress criterion (sstress)²⁶. The statset parameter for the maximum number of iterations (MaxIter) was increased to allow for convergence of the MDS solution.

3.3 Differences across limbs

3.3.1 Consistency across limbs

To further understand if the consistency of the children’s affected muscle excitation was similar to that of their unaffected limb, across-limb comparisons were made. The previously-discussed within-grasp consistency (across hand grasp configurations), was used to determine overall consistency for each limb. Therefore, all grasp consistencies for the affected limb were compared to all grasp consistencies for the unaffected limb of a given participant. First, we assessed the normality for measures of consistency across the 10 grasps for each limb with the Shapiro-Wilk Test³⁵. Then, due to the non-parametric nature of the data, a Wilcoxon Signed Rank test with an α = 0.05 was used for comparisons. The null hypothesis, H₀, was defined such that the difference between the median grasp consistency across the two limbs is zero. In this way, we were able to determine if there were any statistical differences in the participants’ ability to attempt repeatable missing hand grasp configurations with their affected limb compared to their unaffected limb.

3.3.2 Relatedness across limbs

In order to understand if attempted hand grasps were related across the affected and unaffected limbs, measures of the RMS and MNF characteristics were evaluated with the non-parametric RDM label randomization analysis adapted from³⁴. The affected limb RDM was selected, and 50,000 random permutations of both the rows and columns were performed. At each permutation, the RDM of the affected limb and the unaffected limb were compared with Kendall’s Tau-a (${\tau }_{a}$) correlation, provided in the representational similarity analysis toolbox⁴², thereby allowing for simulation of the null distribution. The null hypothesis, H₀, is defined as two RDMs being unrelated. We calculated the correlation between two RDMs, which were subject to no label permutations, and found an estimate for the p-value through the proportion of correlations in the null distribution greater than the non-permuted correlation³⁴. A significance level of α = 0.05 was selected and if the estimated p-value was less than the significance level then there was favor for the alternative hypothesis H₁ that there is relatedness between the two RDMs. This suggests that the muscle excitation produced when participants performed missing hand grasp configurations is statistically related to that of their unaffected limb.

4.1 Muscle excitation

4.1.1 Visualization

To visualize sEMG patterns across channels, the median RMS and MNF muscle excitation across limbs and hand grasp configurations were calculated as participants attempted to perform each hand grasp (Fig. 4, Fig. 5, and Supplementary Figs S1-S18 online). For the RMS characteristic, the unaffected limb behaved as expected with only a subset of sEMG channels recording muscle excitation. For example, during wrist extension and flexion, the extensor carpi radialis longus and flexor carpi radialis muscles are activated separately for each wrist movement⁴³. A similar phenomenon can be seen in Fig. 4 as exemplified by participant SHR-C and throughout participant data sets. Different patterns of sEMG activity were observed for the RMS characteristic across attempted hand grasp configurations of participants' affected limbs, thereby providing a visual indication of the extent to which children with UCBED could actuate their affected muscles. Interestingly, although not in all cases, when the participants were asked to attempt a hand grasp with their affected limb the majority of sEMG channels recorded muscle excitation above the relaxed state (see Fig. 4 and the RMS Supplementary Figures online). Additionally, it is important to note that for some participants the affected limb RMS characteristic across the sEMG channels demonstrated relatively large degrees of variability, e.g., participant SHR-B produced a large RMS spread across sEMG channels for most hand grasp configurations (see Supplementary Fig. S3 online). In contrast, participant SHR-D had very few visually distinct patterns of RMS muscle excitation with similar spread across grasps. Therefore, participants have shown (1) visually distinct patterns of muscle excitation across hand grasp configurations, (2) minimal reproducibility with large spreads, and (3) consistent but few patterns of muscle excitation, all of which play a role in the ability to decode motor intent.

Insert Fig. 4

For the MNF characteristic, patterns were illustrated in frequency-based muscle excitation for the unaffected and affected limbs among participants across sEMG channels. The MNF muscle excitation for both limbs showed parallels to their RMS counterparts. While the unaffected excitation differed across sEMG channels from grasp to grasp, the majority of the affected excitation was above the relaxed state, as illustrated by participant SHR-C Fig. 5 and the MNF Supplementary Figures online. Although there were variations in the degree of visual patterns for the RMS characteristics across grasps, the MNF characteristics revealed additional distinct patterns for most participants across both limbs (see MNF Supplementary Figures online). This indicates that separately and together RMS and MNF characteristics may provide sufficient information to differentiate between hand grasp configurations.

Insert Fig. 5

4.1.2 Grasp excitation consistency

Within-grasp consistency was calculated from Kendall’s Coefficient of Concordance, W, to quantify participants' ability to consistently perform patterns of RMS and MNF muscle excitation for each limb over the multiple trials of hand grasp configurations. Here consistencies ranged from W < 0.20, 0.20 ≤ W < 0.40, 0.40 ≤ W < 0.60, 0.60 ≤ W < 0.80, and W ≥ 0.80 with poor, minimal, weak, moderate, and strong consistency, respectively. In general, for the unaffected limb, measures of consistency were moderate to strong which is indicative of typical repetitive muscle actuation and few participants had poor to weak consistency. Specifically, for the RMS consistency, all participants except for SHR-B had at least 9 hand grasp configurations with moderate to strong consistency i.e., a W of greater than 0.60. Interestingly, SHR-B had the top two lowest RMS consistencies in the unaffected limb for the grasp-type cylindrical wrap with wrist rotated (CR) and cylindrical wrap (CW), with values of W = 0.25 and W = 0.27, respectively. SHR-B only had 4 out of 10 hand grasp configurations with moderate to strong consistencies. Similarly, participant SHR-I had the third lowest consistency of W = 0.30 for wrist flexion (WF). Alternatively, for the MNF unaffected limb within-grasp consistency, all participants except SHR-B had moderate to strong consistency values for at least 5 hand grasp configurations while SHR-B had only 2. The top three lowest consistencies were present in participants SHR-F (W = 0.16 for wrist extension (WE)), SHR-A (W = 0.21 for pulp pinch (PP)), and SHR-B (W = 0.24 for wrist rotation (WR)) in the poor to minimal consistency range. All hand grasp consistency values for the unaffected limb can be seen in the first column of Fig. 6.

When attempting to move their missing limb, participants were able to produce repeatable patterns of affected muscle excitation, although there was variability across participants. Repeatable patterns are an important facet for robust control of prostheses given that 6–9 common hand grasp movements can account for nearly 80% of activities in daily living^25,44, indicating the efficacy of prosthesis use in this population. When evaluating the RMS within-grasp consistency in the affected limb, we found at least 5 of the 10 hand grasp configurations had moderate to strong consistency. Here, participant SHR-A had the lowest consistency value of W = 0.14 for the cylindrical wrap (CW). Participant SHR-B had the second and third lowest consistency values: W = 0.17 in the cylindrical wrap (CW) and W = 0.22 in the cylindrical wrap with wrist rotated (CR). Additionally, the MNF within-grasp consistency for the affected limb showed that all participants except SHR-B had moderate to strong consistency values for 4 or more hand grasp configurations. SHR-G had the lowest consistency value of W = 0.13 for index flexion (IF). Participant SHR-B had only 1 hand grasp, wrist flexion (WF), with a moderate consistency value, and had the second through the sixth lowest consistency values from poor to weak. All other consistency values for the affected limb can be seen in the second column of Fig. 6. Together, we see that 5 of 10 grasps for RMS and 4 of 10 grasps for MNF approached moderate to strong consistency (i.e., nearing the consistency for 6–9 hand grasp movements). In conclusion, this indicates that the participants were able to perform reproducible attempted hand grasp configurations in their affected limb to a similar degree as their unaffected limb.

Insert Fig. 6

4.2 Across grasp dissimilarity

To further understand if these hand grasps were dissimilar or distinguishable from one another, additional evaluation was performed given the emergence of muscle excitation patterns in previous visualizations across sEMG channels when participants attempted each hand grasp configuration. To determine whether sufficient information for distinguishable structure in hand grasp configurations was present, we performed the sdRDM analysis. As such, the analysis demonstrated whether pairwise distances between grasps for RMS and MNF measures of spread (i.e., interquartile range (IQR)) and amplitude were typical of muscle excitation movements. To note, typical muscle excitation has been observed to be the variability in sEMG signals across channels from task to task, or even within the same movement¹⁵. Furthermore, to qualitatively visualize the distinguishability across hand grasp configurations, multidimensional scaling (MDS) was used.

The sdRDM analysis of the median and IQR measures for RMS and MNF indicated that distinguishable structures of sEMG data were present when the participants with UCBED attempted to perform missing hand grasp configurations. This analysis also showed distinguishable structures of sEMG data were present in participants’ unaffected limbs, as is expected of typical muscle contractions. However, this was not the case for all participants e.g., the analysis did not provide sufficient evidence to suggest there was a distinguishable structure for the median RMS of SHR-B’s unaffected and affected limbs with p = 0.188 and p = 0.267, respectively. Additionally, sdRDM analysis of the affected limb did not provide sufficient evidence to suggest a distinguishable structure for the following participants: SHR-D (p = 0.106, RMS IQR), SHR-F (p = 0.118, MNF IQR), and SHR-I (p = 0.163, RMS IQR). All other sdRDM analyses for the affected and unaffected limb showed a distinguishable structure of the hand grasp configurations; all p-values for each limb across participants and characteristics can be found in Table 2.

Table 2

Split-data representational dissimilarity analysis to distinguish the structure of amplitude and spread of RMS and MNF characteristics.
Participants	Unaffected				Affected
	RMS EDI		MNF EDI		RMS EDI		MNF EDI
	Median	IQR	Median	IQR	Median	IQR	Median	IQR
SHR-A	0.552 p < 0.001	0.483 p < 0.001	0.629 p < 0.001	0.235 p = 0.005	0.319 p = 0.003	0.459 p = 0.006	0.519 p < 0.001	0.452 p < 0.001
SHR-B	0.095‡	0.546 p < 0.001	0.252‡	0.728†	0.047‡	0.256 p = 0.036	0.167‡	0.510†
SHR-B	p = 0.188*	0.546 p < 0.001	p = 0.018	p < 0.001	p = 0.267*	0.256 p = 0.036	p = 0.023	p < 0.001
SHR-C	0.550 p < 0.001	0.472 p < 0.001	0.720 p < 0.001	0.377 p < 0.001	0.535 p < 0.001	0.294 p = 0.003	0.571 p < 0.001	0.423 p < 0.001
SHR-D	0.455 p < 0.001	0.463 p < 0.001	0.667 p < 0.001	0.205‡	0.466 p < 0.001	0.114‡	0.665†	0.199 p = 0.015
SHR-D	0.455 p < 0.001	0.463 p < 0.001	0.667 p < 0.001	p = 0.020	0.466 p < 0.001	p = 0.106*	p < 0.001	0.199 p = 0.015
SHR-E	0.421 p < 0.001	0.453 p < 0.001	0.648 p < 0.001	0.235 p = 0.007	0.489 p < 0.001	0.273 p < 0.001	0.599 p < 0.001	0.212 p = 0.018
SHR-F	0.554 p < 0.001	0.459 p < 0.001	0.438 p < 0.001	0.603 p < 0.001	0.474 p < 0.001	0.254 p = 0.004	0.590 p < 0.001	0.119‡
SHR-F	0.554 p < 0.001	0.459 p < 0.001	0.438 p < 0.001	0.603 p < 0.001	0.474 p < 0.001	0.254 p = 0.004	0.590 p < 0.001	p = 0.118*
SHR-G	0.635†	0.578†	0.783†	0.449 p < 0.001	0.629†	0.519†	0.529 p < 0.001	0.303 p = 0.007
SHR-G	p < 0.001	p < 0.001	p < 0.001	0.449 p < 0.001	p < 0.001	p = 0.002	0.529 p < 0.001	0.303 p = 0.007
SHR-H	0.457 p < 0.001	0.368‡	0.468 p < 0.001	0.214 p = 0.007	0.351 p < 0.001	0.212 p = 0.008	0.233 p = 0.005	0.364 p < 0.001
SHR-H	0.457 p < 0.001	p < 0.001	0.468 p < 0.001	0.214 p = 0.007	0.351 p < 0.001	0.212 p = 0.008	0.233 p = 0.005	0.364 p < 0.001
SHR-I	0.552 p < 0.001	0.497 p < 0.001	0.453 p < 0.001	0.366 p < 0.001	0.474 p < 0.001	0.116	0.309 p < 0.001	0.180 p = 0.021
SHR-I	0.552 p < 0.001	0.497 p < 0.001	0.453 p < 0.001	0.366 p < 0.001	0.474 p < 0.001	p = 0.163*	0.309 p < 0.001	0.180 p = 0.021
*Red p-values from the sdRDM analysis indicate a failure to reject the null hypothesis (i.e., there was not sufficient evidence to suggest a distinguishable structure of the hand grasp configurations), given a significance level of α = 0.05.
†Green EDI values indicate the maximum within a column.
‡Yellow EDI values indicate the minimum within a column.

Further investigation of the sdRDM exemplar discriminability index (EDI) highlighted participants that may be good candidates for control of dexterous prostheses i.e., large amplitude-based (median) EDI values suggest increased distinguishability of prompted missing hand grasp configurations. Additionally, the large EDI values for IQR measures indicated two findings. First, typical muscle excitation, which is indicative of distinguishable movements and therefore potentially effective control of dexterous prostheses. Second, variability may be too large across sEMG signals which may indicate poor consistency, upon which the muscle excitation plots and measures of consistency should be further investigated. Here, maximum and minimum EDI values are highlighted within the RMS or MNF measures for the unaffected and affected limbs across participants. SHR-G had the majority of maximum EDI values for 3 of the 4 measures of the unaffected limb and 2 of the 4 measures for the affected limb. In contrast, SHR-B had the majority of minimum EDI values for 2 of the 4 measures of the unaffected and affected limb, respectively. EDI values across limbs and measures for RMS and MNF are highlighted for each participant in Table 2.

Insert Table 2

To qualitatively visualize the differences across hand grasp configurations, the complete data set across trials (unsplit) was used to create the RDM for each limb. As previously mentioned, nonmetric MDS was used on each limb’s RDM to reflect the higher dimensional distances across hand grasps in a three-dimensional subspace. The MDS illustrated distinguishable differences between the various hand grasp configurations with few grasps close and/or overlapping in the subspace as seen by the separation of points in the top panel of Fig. 7a and b. Figure 7 shows both the median and IQR of RMS and MNF characteristics with the MDS (top) and corresponding RDM (bottom). Additionally, all MDS and RDM plots for participants can be seen in Supplementary Figs S19-S27 online. These results reveal, through a qualitative visualization, that the measures analyzed provide information to distinguish attempted hand grasps by visual separation.

Insert Fig. 7

4.3 Differences across limbs

4.3.1 Consistency across limbs

To get a better understanding of the differences in the participants’ ability to attempt consistent hand grasp configurations with their affected limb, statistical comparisons were made to the consistency of their unaffected limb. We found RMS consistency in the affected limb to be statistically lower than the unaffected limb for participants SHR-A, C, D, E, and H. Alternatively for MNF, participants SHR-C, SHR-E, and SHR-I had statistically lower consistency in the affected limb when compared to the unaffected limb. In each of the preceding cases, the unaffected limb had an overall higher median than the affected limb. The consistency for RMS and MNF across participants with highlighted statistical differences are shown in the top and bottom panels of Fig. 8, respectively. The resulting consistencies in RMS and MNF across limbs (Fig. 8) indicated that some participants had difficulty reproducing hand grasp configurations to a similar degree as that of their unaffected limb. This inability to reproduce grasps, in turn, may hinder their potential to use multi-grasp prostheses.

Insert Fig. 8

4.3.2 Relatedness across limbs

To understand if the structure of the affected limb RDM was related to that of the unaffected limb, the non-parametric RDM label randomization test was used³⁴. It was found that the majority of participants had some degree of shared information such that the pair-wise distances between hand grasps for their affected limb was related to that of their unaffected limb. There was relatedness with the exception of the following participants: SHR-F (p = 0.060, RMS IQR), SHR-B (p = 0.055, median MNF), and SHR-F (p = 0.054, median MNF). Additionally, Table 3 shows the non-permuted Kendall’s Tau-a (${\tau }_{a}$) correlations used for the RDM test with corresponding p-values. These results indicate that the majority of participants had related information across limbs for amplitude and spread of RMS and MNF characteristics.

Table 3

Relatedness of RDMs across limbs for amplitude and spread of RMS and MNF characteristics.
Participants	RDM Relatedness Across Limbs
	RMS Correlation		MNF Correlation
	Median	IQR	Median	IQR
SHR-A	0.219 p = 0.002	0.266 p = 0.001	0.234 p = 0.002	0.288 p < 0.001
SHR-B	0.401 p < 0.001	0.182 p = 0.015	0.119 p = 0.055*	0.381 p < 0.001
SHR-C	0.255 p = 0.005	0.159 p = 0.017	0.318 p < 0.001	0.293 p < 0.001
SHR-D	0.272 p < 0.001	0.215 p = 0.005	0.180 p = 0.008	0.364 p = 0.001
SHR-E	0.465 p < 0.001	0.262 p < 0.001	0.298 p = 0.001	0.254 p < 0.001
SHR-F	0.200 p = 0.005	0.116 p = 0.060*	0.123 p = 0.054*	0.200 p = 0.003
SHR-G	0.439 p < 0.001	0.349 p < 0.001	0.204 p = 0.002	0.301 p < 0.001
SHR-H	0.224 p = 0.014	0.158 p = 0.013	0.423 p < 0.001	0.200 p = 0.005
SHR-I	0.179 p = 0.007	0.144 p = 0.030	0.202 p = 0.011	0.137 p = 0.023
*Red cells highlight values from the RDM label randomization test, which indicate there was not sufficient evidence for the relatedness across limbs (with a significance level of α = 0.05).

Insert Table 3

This study investigated the extent to which affected muscles respond when children with UCBED attempted to perform various hand grasp configurations. We have shown that even though these children were born with limb deficiency and have never used their affected muscles to actuate an intact limb there is still patterned, consistent, and distinguishable muscle activity in response to attempted missing hand grasp configurations.

5.1 Visible patterns of muscle excitation

Across most participants, different sEMG patterns of muscle excitation for both RMS and MNF characteristics were seen in the affected limb indicating their ability to excite coordinated muscle excitation as they attempted the various hand grasps (Fig. 4, Fig. 5, and Supplementary Figs S1-S18 online). Furthermore, as expected in the unaffected limb, there were differences in muscle excitation as captured across sEMG channels. Although this was true for many participants’ affected limbs, it was not true for all. For example, SHR-A had few visually distinguishable patterns in their RMS muscle excitation across attempted hand grasps, which can be seen in the Supplementary Fig. S1 online. This finding is likely attributed to the affected limb circumference (15 cm) and length (13 cm), the smallest circumference out of the entire cohort. Therefore, due to the size, it is doubtful that the participant has developed similar muscular structure and mass to those participants with longer affected limbs. Additionally, participant SHR-B showed a large interquartile range in RMS muscle excitation in their affected limb and interestingly, similar results were also observed in their unaffected limb. This large spread in data across the hand grasp configurations and limbs was likely attributed to the child’s age, as they were the youngest (8 years old) among our participants; their short attention span for the experimental task thereby affected grasp reproducibility. Finally, when viewing participant SHR-D there was apparent muscle excitation, although few visual patterns emerged in the affected limb for both RMS and MNF characteristics across the attempted hand grasps. The few visual patterns may be attributed to a premature sensory-motor system due to age (9 years old) and limb length (12.5 cm); there is still physical development to account for. Although it is clear that most participants can generate coordinated muscle activation in their affected side, it also appears that limb size may be a relevant factor in detecting muscle activity. Furthermore, age and cognitive factors such as attention spans may also be important in the reproducibility of muscle excitation and ultimately the efficacy of advanced prosthesis control systems.

5.2 Reproducible attempted hand movements

The participants' ability to reproduce attempted hand grasp movements was explored through measures of within-grasp consistency. Although the unaffected limb had quantitatively higher consistency than that of the affected limb, statistical differences were found when comparisons were made across limbs (Fig. 8). One exception was participant SHR-B who had the lowest consistency across both limbs for RMS and MNF characteristics, which aligns with the visualization of muscle excitation that depicted large interquartile ranges across sEMG channels (see Supplementary Figs S3 and S4 online). Additionally, the MNF consistency was quantitatively lower than that of the RMS across participants. This finding may be attributed to MNF itself because although it is a robust measure of muscle excitation, it is often used as an assessment of and is sensitive to muscle fatigue^15,29. Even though participants were given multiple rest periods, it is possible they became fatigued at various stages which would explain our findings. Fatigue may be particularly relevant in this cohort given that despite consistent patterns of grasp-to-grasp muscle excitation, the activity may be deemed as physically demanding because they have never contracted their affected muscles in these repeatable, distinct ways. Additionally, participants’ ability to produce repeatable patterns of muscle excitation in their affected limb suggests the potential for the use of multi-grasp prostheses. From this potential, it follows that given proper training, consistency is expected to approach that of their unaffected limb (i.e., nearing the consistency for 6–9 hand grasp movements). The effects of physical conditioning, training, and fatigue are all important considerations for the use of advanced prostheses and muscle-based control systems in this population.

5.3 Distinguishable attempted hand movements

There was a distinguishable structure to the RMS and MNF characteristics in the affected limb for the majority of the participants (Table 2). Moreover, there was a degree of relatedness across limbs as children attempted missing hand grasp configurations (Table 3). However, this was not true for all participants. For example, participant SHR-B did not show a distinguishable structure for median RMS and relatedness for the median MNF across limbs, as previously shown in the results. This may support the large spread seen in the muscle excitation across sEMG channels and the lack of consistency in hand grasp configurations for this individual participant (see Supplementary Figs S3 and S4 online). As discussed previously, these findings are likely attributed to factors of age and limb size. For measures of spread, some participants did not demonstrate a distinguishable structure (i.e., SHR-D and SHR-I for RMS IQR, and SHR-F for MNF IQR). Moreover, for the latter (SHR-F), we did not find that the pair-wise distances between hand grasps for the RMS IQR measures were related across limbs. We found muscle excitation was conceivably both distinguishable across movements and related across limbs as a result of participants simultaneously mirroring hand grasp movements. Accordingly, participants were trying to imagine performing the same grasp to the same degree with every repetition. As a result, this still strongly indicates children’s potential ability to actuate their affected muscles in distinguishable and consistent patterns, and with proper training may be able to effectively control dexterous prostheses.

The EDI values (Table 2) provided additional insight into the distinguishability of the muscle excitation structure. To note, participant SHR-G had the majority of maximum EDI values across all participants and limbs. This was likely attributed to a more mature sensory-motor system as the participant was 19 years old and uses a myoelectric prosthesis; that is, they control their device using the sEMG of their affected muscles. Although not a pattern recognition sEMG control system that necessitated contraction of their affected muscles in unique patterns, this participant still used 2-site control that required isolation and contraction of larger muscle groups in their affected limb. Presumably, this may have ameliorated their ability to generate consistent distinguishable muscle excitation patterns when prompted during the experiment. Participant SHR-B had a majority of the lowest EDI values across limbs, which parallels the previously discussed consistency and distinguishability results. Undoubtedly, these two participants can allow us to begin understanding the variability that may be present in this population of children and also provides a further appreciation for the improvements possible with regular prosthesis use and actuation of affected muscles through training.

The multidimensional scaling (MDS) plots are useful to visualize the distinguishability of the various hand grasp configurations. Although most participants showed a distinguishable structure across grasps (see Supplementary Figs S19-S27 online), there were a few exceptions. The RMS and MNF correlation distances between hand grasp configurations were determined by the median across repetitions. If a participant had low grasp consistency the median would not be useful to define the grasps, and the RDM and MDS plots would not demonstrate the distinguishability of hand grasp configurations. We can see this clearly demonstrated in participant SHR-B, who exhibited poor RMS and MNF consistency measures of muscle excitation for both the affected and unaffected limb.

We have shown in a limited cohort of children with UCBED that they possess a degree of biological control over their affected muscles with the ability to perform consistent and distinguishable hand grasp configurations. However, not all participants were able to achieve this degree of biological control over their affected muscles which may have been attributed to age and limb size, since cognitive demands and robust musculature are important to achieve meaningful muscle activation. Our findings also suggest that children with UCBED may have the ability to use advanced prosthesis control systems. However, further work is needed to examine age and limb size with larger cohorts and more statistical power to effectively adapt, translate, and prescribe dexterous prostheses to the pediatric population. The bulk of literature for dexterous upper limb prosthetic devices is saturated in adult-based control systems using sEMG. Very limited success has been found in prior studies with UCBED cohorts^11,12. Moreover, these studies either did not investigate populations of children or simply applied commercially available control systems that were built on research and refinement in populations with acquired amputations. Our work shows distinguishable and consistent patterns of muscle excitation found in children with UCBED when they attempt to perform hand grasp configurations. We suggest that these findings warrant further investigation into techniques to best tune, adapt, and leverage existing advanced control techniques, such that they may be most effective in meeting the unique demand of children, most of whom will have a congenital upper limb absence².

Data availability

The data that support the findings of this study are available from Shriners Children's Hospital – Northern California but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request and with permission of Shriners Children's Hospital.

Acknowledgements

The authors would like to the thank participants for their time and patience. This research was supported by the National Science Foundation CISE-IIS Human Centered Computing Program under award number 2133879, and through the Shriners Children’s Hospital Clinical Research Program under award number 79139-NCA-23.

Author contributions

J.S.S. and W.M.J. conceived the study and provided supervision. M.A.J. and A.M.B. provided their clinical expertise for study protocols. M.A.B. and J.F. performed the experiment protocols for data procurement. All authors performed conceptualization of the study along with the development of the study methodology. M.A.B. performed data analysis, visualization, and wrote the original draft. All authors reviewed and edited the manuscript.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Understanding the capacity of children with congenital unilateral below-elbow deficiency to actuate their affected muscles

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Journal Publication

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Abstract

Figures

1. Introduction

2. Methods

2.1 Participants

2.2 Data collection

2.3 Experiment

2.4 sEMG pre-processing

3. Analysis

3.1 Muscle excitation

3.1.1 Visualization

3.1.2 Within grasp consistency

3.2 Across grasp dissimilarity

3.3 Differences across limbs

3.3.1 Consistency across limbs

3.3.2 Relatedness across limbs

4. Results

4.1 Muscle excitation

4.1.1 Visualization

4.1.2 Grasp excitation consistency

4.2 Across grasp dissimilarity

4.3 Differences across limbs

4.3.1 Consistency across limbs

4.3.2 Relatedness across limbs

5. Discussion

5.1 Visible patterns of muscle excitation

5.2 Reproducible attempted hand movements

5.3 Distinguishable attempted hand movements

6. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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