Muscle fatigue during spinal stimulation and resistive ankle exoskeleton use in children with cerebral palsy

Muscle fatigue during spinal stimulation and resistive ankle exoskeleton use in children with cerebral palsy | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Muscle fatigue during spinal stimulation and resistive ankle exoskeleton use in children with cerebral palsy View ORCID Profile Charlotte R. DeVol , View ORCID Profile Victoria M. Landrum , Siddhi R. Shrivastav , Heather A. Feldner , Kristie F. Bjornson , Chet T. Moritz , View ORCID Profile Katherine M. Steele doi: https://doi.org/10.1101/2025.08.23.25334289 Charlotte R. DeVol 1 Department of Mechanical Engineering, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Charlotte R. DeVol Victoria M. Landrum 1 Department of Mechanical Engineering, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Victoria M. Landrum Siddhi R. Shrivastav 2 Department of Electrical and Computer Engineering, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Heather A. Feldner 3 Department of Rehabilitation Medicine, University of Washington , Seattle, WA, USA 4 Center for Research and Education on Accessible Technology and Experiences, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristie F. Bjornson 5 Department of Pediatrics, University of Washington , Seattle, WA, USA 6 Seattle Children’s Research Institute , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chet T. Moritz 2 Department of Electrical and Computer Engineering, University of Washington , Seattle, WA, USA 3 Department of Rehabilitation Medicine, University of Washington , Seattle, WA, USA 7 Department of Neurobiology and Biophysics, University of Washington , Seattle, WA, USA 8 Center for Neurotechnology , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katherine M. Steele 1 Department of Mechanical Engineering, University of Washington , Seattle, WA, USA 4 Center for Research and Education on Accessible Technology and Experiences, University of Washington , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katherine M. Steele For correspondence: kmsteele{at}uw.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Evaluating fatigue during rehabilitation can help prevent overexertion to improve motor learning. The purpose of this study was to quantify how walking with transcutaneous spinal cord stimulation (tSCS) impacts muscle fatigue during treadmill training with and without a resistive ankle exoskeleton (Exo) in children with cerebral palsy (CP). Nine children with CP (4-14 years old) participated in four walking conditions: (1) No Device, (2) tSCS only, (3) Exo only, and (4) tSCS+Exo. Plantarflexor maximum voluntary contraction (MVC) was performed before and after each condition. Soleus amplitude root mean square (RMS) and median frequency (MDF) from electromyography recordings were used as biomarkers for fatigue. Participants only showed signs of soleus fatigue in the first five minutes of the Exo only condition with increased RMS (p < 0.001) and decreased MDF (p < 0.001). After ten minutes of walking, there was a slight decrease in MDF in the No Device and tSCS only conditions (p = 0.002), but no change in RMS. There were no significant differences in changes in MVC force, RMS, or MDF between conditions. Walking with tSCS may reduce the impact of the Exo on soleus fatigue for children with CP. Introduction One of the main challenges among ambulatory people with cerebral palsy (CP) is fatigue in daily life. Individuals with CP use two to three times the energy of nondisabled peers to walk 1 – 3 . Fatigue limits mobility for both children and adults with CP, affecting their ability to perform tasks of daily living and interact with peers 4 – 6 . One component of fatigue is muscle fatigue, defined as a reduction in force output 7 , 8 . Prior to an observable decrease in force output, there are myoelectric manifestations of muscle fatigue. This includes a decrease in the conduction velocity of muscle fibers, which can be quantified as a simultaneous decrease in the frequency and increase in amplitude of electromyographic (EMG) recordings 9 . When performing repetitive tasks, such as walking, the muscles of children with CP fatigue faster than their peers 10 – 12 . Changes in muscle properties and reductions in the ability to activate all available muscle fibers may make children with CP more likely to rely on weaker, high endurance muscle fibers compared to peers and, thus, make them more prone to muscle fatigue 13 . Considering muscle fatigue is important when developing rehabilitation interventions. When physical training, such as walking, is performed with rest breaks to prevent over-exertion, the nervous system is more adaptable to learning new information, a process known as neuroplasticity 14 . Neuroplasticity is key for children with CP who are learning new tasks during physical therapy. High muscular effort can cause sustained muscle fatigue during a training session and may reduce neuroplasticity and performance 14 , 15 . Despite the importance of muscle fatigue on neuroplasticity, fatigue is often not quantified during the development or evaluation of rehabilitation interventions. Understanding fatigue during training can provide useful information for determining optimal session length and level of exertion during an intervention, thus helping develop more effective and individualized training. Several new rehabilitation interventions have recently been introduced for children with CP that may influence muscle fatigue, neuroplasticity, and function. Exoskeletons that provide ankle resistance during push-off have improved walking speed and planarflexor control 16 – 18 . However, resistance applied during gait training may lead to more muscle fatigue compared to standard gait training. Understanding the effects of a resistive ankle exoskeleton on muscle fatigue may inform treatment session length or resistance levels to optimize conditions for neuroplasticity. Similarly, neuromodulation may also be a viable tool for reducing muscle fatigue and optimizing performance. Specifically, transcutaneous spinal cord stimulation (tSCS) combined with physical training, such as gait training, has recently shown promising improvements in muscle co-contraction, gross motor function, spasticity, and gait kinematics for children with CP 19 – 24 . Applying tSCS during walking can reduce muscle co-contraction and improve coordination between hip and knee motion during gait 21 . Through improved coordination, tSCS may reduce muscle demand during training to mitigate fatigue and improve conditions for neuroplasticity. Understanding the impact of tSCS on muscle fatigue may assist in its translation into clinical care. The purpose of this study was to quantify muscle fatigue before, during, and after gait training with and without tSCS and a resistive ankle exoskeleton (Exo) targeting plantarflexor muscles for children with CP. We evaluated muscle activity before, during, and after walking on a treadmill under four conditions: No Device, tSCS only, Exo only, and tSCS + Exo. Compared to walking with No Device, we hypothesized that the Exo would increase muscle fatigue. We further hypothesized that the combination of tSCS with the Exo would reduce the rate of fatigue compared to the Exo alone. Methods Participants Nine children with CP were recruited to evaluate gait training with tSCS and an Exo ( Table 1 ). Prior to enrollment, informed age-appropriate assent and consent were obtained from participants and their caregivers, respectively. All visits were completed at the University of Washington. This study was approved by the University of Washington Human Subjects Division (IRB identifier: STUDY00014877) and registered at ClinicalTrials.gov ( NCT05520359 ; registered on 25 August 2022). The study methods were performed in accordance with the approved protocol and University of Washington Institutional Review Board guidelines and regulations. Individuals were eligible to participate if they were ambulatory, Gross Motor Function Classification System levels I-II, had spastic CP, had no prior selective dorsal rhizotomy, had not undergone a lower extremity surgery or botulinum toxin injections in the past year, did not have uncontrolled seizures, were able to follow 2-3 step commands, and had no implants that might interfere with electrical stimulation. Informed consent was obtained from all participants’ legal guardians for the use of any photos within the manuscript that show a participant. View this table: View inline View popup Download powerpoint Table 1. Participant Characteristics: Experimental Protocol Participants completed four visits where they walked with or without an Exo and tSCS ( Figure 1 ). These walking conditions included: No Device, tSCS only (tSCS), resistive exoskeleton (Exo), or the combination of tSCS and Exo (tSCS+Exo). P06 and P07 did not complete the tSCS+Exo visit due to a change in eligibility. Participants walked on the treadmill at a preferred speed that was determined at the first visit and held constant at subsequent visits. Walking conditions were performed on an inground treadmill (Bertec, Columbus, OH) on separate days at least five days apart in a pseudo-randomized order. Rest breaks were provided as needed with a maximum of two rest breaks during each session. Download figure Open in new tab figure 1. overview of study design, data processing, and devices evaluated. a) participants completed four study visits walking for a maximum of 20 minutes with: no device, spinal stimulation (tscs), a resistive ankle exoskeleton (exo), and both tscs and exo (tscs+exo). participants performed a maximum voluntary contraction (mvc) of the plantarflexor muscles while lying in a supine position before and after walking. soleus muscle activity was recorded during each mvc and walking condition. b) processing pipeline for electromyography (emg) data to calculate median frequency (mdf) and amplitude root mean square (rms) as biomarkers of muscle fatigue. c) placement of electrodes for the spinal cord neuromodulator (sconetm) from spinex. d) the spark exoskeleton by biomotum provided a torque at the ankle to resist during push-off. fsr = force sensitive resistor. For the tSCS and tSCS+Exo visits, tSCS was administered via the Spinal Cord Neuromodulator (SCONE™, SpineX, Inc.) following previously reported protocols 21 . Stimulation was applied using adhesive gel electrodes placed just below the T11 and L1 spinous processes using 3.2 cm round electrodes. The ground electrodes were 5.1 x 8.6 cm rectangular electrodes placed over the anterior superior iliac spine ( Figure 1C ). The amplitude for the subthreshold stimulation was determined foreach individual at their first visit using tSCS and held constant at subsequent visits, with the exception of P06 who needed a slight adjustment of tSCS due to comfort during their first use of tSCS ( Table 1 ). The amplitude of tSCS was determined based on children’s self-reported ease of walking, sensation beneath the cathodes, and a clinical interventionist’s observation of gait quality and participant’s behavior within the first ten minutes of use. The tSCS was then turned off and a break was provided until walking began. The amplitude of tSCS was also lowered or turned off during seated breaks and turned back on during walking, as sitting can change the sensation at the site of stimulation compared to standing 25 . For the Exo and tSCS+Exo walking conditions, we used the SPARK exoskeleton by Biomotum (Portland, OR) to provide resistance at the ankle during the push-off phase of gait 17 . The SPARK is an untethered exoskeleton with a waist strap that carries the battery pack, Bowden cables that connect the battery and motor to an ankle actuator, and foot plates inside the users’ shoe. The SPARK records timing of gait events from a force sensitive resistor in the foot plate and uses this information to time when it applies a torque at the ankle to resist push-off ( Figure 1D ). The ankle resistance torque of the Exo was set at 12% of each participant’s bodyweight, similar to previous studies 16 , 17 , 26 . The SPARK pairs with a mobile application to control the level of resistance, which can also provide audiovisualfeedback on the torque applied at the ankle. The audiovisual feedback feature was not used during the first five minutes of walking with the Exo. Data after this period, when the audiovisual feedback was used, was excluded from this analysis because it may impact soleus muscle activity 26 . During all walking conditions, electromyography (EMG) data of the soleus muscle were recorded at 2000 Hz (Delsys, Inc., Natick MA). We focused on the soleus muscle because the Exo is designed to target the plantarflexor muscles, and other plantarflexors are not accessible to record with surface EMG with the Exo’s calf cuff in place 17 . We analyzed the more-affected lower limb for all participants, defined as the side of the body with more spasticity. Before (pre) and after (post) each walking condition, maximum voluntary contraction (MVC) of the more-affected plantarflexors were obtained using an isometric manual muscle test with a hand-held dynamometer (Activbody Inc., San Diego, CA). Participants laid supine and were instructed to keep their knees fully extended while plantarflexing their foot as strongly as possible for 5 seconds against a researcher providing resistance with the hand-held dynamometer 18 . Participants were given visual feedback of their force value output and instructed to raise the number as high as possible and continue pushing until time was up. The MVC was repeated three times before and after each walking condition and EMG data of the soleus muscle were recorded throughout and averaged across the three trials. Trials were dropped if there was an outlier in force value, as judged visually by the research team during a session, and an extra MVC was taken to include three total MVC’s before and after each walking condition. P06 did not complete the MVC for any walking conditions. Data Analysis Soleus muscle median frequency (MDF) and root mean square (RMS) were used as biomarkers for myoelectric manifestations of muscle fatigue 9 , 27 . EMG data for walking conditions were analyzed using a custom MATLAB script (MathWorks, Natick, MA). Soleus EMG signals were bandpass filtered from 20 – 400 Hz using a 4th order Butterworth filter. Soleus firing rate was quantified by segmenting bandpass filtered soleus EMG using a Gabor transform moving window centered at 15-second intervals and calculating the MDF within each window 10 , 28 . The Gabor transform filters EMG data in the time domain using a Gaussian function, where a = −0.01 is the window width of the filter, tis the time, and b = 15 seconds, the distance between centers of the moving window. The MDF was calculated using the built-in MATLAB function, medfreq(). EMG data were then zero-centered, rectified, and low-pass filtered (4th order Butterworth; 10 Hz; Figure 2 ) before calculating RMS amplitude in 15-second intervals 27 . Soleus firing rate and RMS were normalized to the calculated firing rate and peak activation of the first 15 seconds of walking, respectively, so reported values are a percent of the start value. Download figure Open in new tab Figure 2. Soleus EMG over the gait cycle for each walking condition: No Device, tSCS only, Exo Only, and tSCS+Exo. Each plot shows the gait cycles averaged over a 15-second duration at minute zero, five, and ten. Thin grey lines are for each participant and the thick black lines are the average across participants. The vertical axis is the EMG amplitude normalized to the RMS over the first 15-seconds. During the five second MVC task, EMG data were also recorded. The same EMG analyses described above were repeated, except that MDF and RMS calculations were made over the entire five second window. Statistical Analysis We used linear mixed effects models (LMM) to quantify the rate of change of EMG RMS and MDF across five minutes of treadmill walking for all four walking conditions: No Device, tSCS only, Exo only, and tSCS+Exo. Only the first five minutes were used for all four conditions because after this point, participants began taking breaks or using audiovisual feedback in the conditions thatincluded Exo, potentially altering muscle fatigue in a nonlinear manner 26 . Use of an LMM was chosen since all walking conditions contained the same participants. The equations for the EMG RMS and MDF LMMs were: where EMG Outcome ij is the observed value of EMG RMS or MDF for the i -th observation of the𝑗-th subject. These equations had the best model fit under the assumption that both walking condition and time influence fatigue rate. We also extended the LMM to included 10 minutes of treadmill walking for tSCS and No Device conditions, during which no participants took rest breaks. Time was measured in seconds, resulting in the fixed effects units as percent per second (%/s) and reported with the 95-percent confidence intervals in brackets. We compared percent change in force output, EMG RMS, and EMG MDF during the post-post MVC task recorded before and after each walking condition. Data sets were not normally distributed, as tested by the Kolmogorov-Smirnov test and a Kruskal-Wallis test was used to measure significant differences between walking conditions. All statistical tests were performed in MATLAB. Results Fatigue During Five Minutes of Treadmill Walking: All Conditions Muscle fatigue, quantified as an increase in RMS and a decrease in MDF, occurred only during the first five minutes of the Exo condition ( Figure 3 ). Walking with No Device for five minutes did not cause a significant change in either RMS or MDF (p = 0.67 and 1.0, respectively). Walking with the Exo, there was a significant increase in RMS ( 𝛽 3 = 0.066%/s [0.029, 0.10]) and decrease in MDF ( 𝛽 3 = -0.081 %/s [-0.11, -0.049])compared to walking with No Device (p<0.001). Walking with tSCS only or Exo + tSCS had no effect on RMS (tSCS p = 0.40; Exo + tSCS p = 0.43) or MDF (tSCS p = 0.46; Exo + tSCS p = 0.89) compared to walking with No Device. Download figure Open in new tab Figure 3. Myoelectric manifestations of muscle fatigue during five minutes treadmill walking under the four different conditions: No Device, tSCS only, Exo only, and tSCS+Exo. A) The linear mixed effects model (LMM) for RMS of the EMG signal normalized to the peak RMS during the first 15 seconds of each condition. B) The RMS fixed effects (𝛽 3 in Eqn. 2 ) of the interaction between time and condition with respect to the No Device condition show a significant increase with Exo only. C) The LMM for MDF of the EMG signal, normalized to the MDF in the first 15 seconds of each condition. D) The MDF fixed effects (𝛽 3 in Eqn. 2 ) of the interaction between time and condition with respect to the No Device condition show a significant decrease with Exo only. Areas indicating an increase in fatigue relative to No Device are indicated with grey arrows and shaded boxes. * significant difference relative to No Device condition (p < 0.05). Fatigue During Ten Minutes of Treadmill Walking: No Device and tSCS Only We also quantified changes in RMS and MDF during ten minutes of consecutive walking with No Device and tSCS only to see if longer trials would induce muscle fatigue ( Figure 4 ). Download figure Open in new tab Figure 4. Myoelectric manifestations of muscle fatigue during ten minutes treadmill walking under two different conditions: No Device and tSCS only. A) The LMM for RMS of the EMG signal normalized to the peak RMS during the first 15 seconds of each condition. B) The LMM for MDF of the EMG signal, normalized to the MDF in the first 15 seconds of each condition. There were no significant differences between the tSCS and No Device condition for either RMS or MDF (p > 0.05). Walking for 10 minutes with No Device led to no changes in RMS (p-value = 0.81) and a small but significant reduction in MDF (𝛽 3 = -0.0088 %/s [-0.014, -0.0031]; p-value < 0.002). Walking with tSCS for ten minutes resulted in no significant differences in RMS or MDF compared to walking with No Device (RMS p-value = 0.88; MDF p-value = 0.68). Fatigue during Pre-Post MVC Despite the observed muscle fatigue within the first 5 minutes of the Exo condition, there were no significant changes in MVC force after twenty minutes of walking for any condition ( Figure 5 ; p = 0.20). The force produced during the MVC task had a median increase after the Exo only condition of 6% (-15 to 34 Newtons, N), and decreased after training with No Device, tSCS only, and tSCS+Exo with reductions of 18% (-51 to 23 N), 3% (-15 to 41 N) and 9% (-43 to 7 N), respectively. Both RMS and MDF of the EMG signal increased slightly during the MVC after each walking trial, but the changes were similar between conditions (RMS p = 0.29; MDF p = 0.95). The EMG RMS during MVC increased by only 6%, 2%, 3%, and 7% after training with No Device, tSCS only, Exo only, and tSCS+Exo, respectively. The EMG MDF during MVC increased 6%, 6%, 7%, and 5% after trainingwith No Device, tSCS only, Exo only, and tSCS+Exo, respectively. Download figure Open in new tab Figure 5. Percent change during plantarflexor MVC on the more-affected side in A) peak force, B) soleus EMG RMS and C) soleus EMG MDF after each walking condition. Discussion As hypothesized, training with the Exo induced the greatest rate of muscle fatigue within the first five minutes of walking, as indicated with a simultaneous increase in soleus RMS and decrease in MDF. Soleus fatigue was not observed during the tSCS+Exo condition, suggesting that tSCS may reduce the impact of the Exo on muscle fatigue during training. Walking with No Device or with tSCS only were very similar in their rates of muscle fatigue, suggesting that five to ten minutes of treadmill training at a comfortable speed induced minimal myoelectric manifestations of muscle fatigue. Changes in the soleus RMS and MDF during MVC also did not indicate muscle fatigue. Children with CP have greater muscle fatigue during five minutes of walking compared to typically developing (TD) peers. Eken et al., (2019) reported a significantly greater rate of fatigue via increased EMG RMS and decreased MDF in the gastrocnemius and soleus muscles on the more-affected side of children with CP compared to TD peers 29 . Their study evaluated overground walking, while here we utilized walking on a treadmill, which may offload the plantarflexors and put more demand on the quadriceps 30 . Parent et al., (2019) reported significant reductions in rectus femoris MDF across six minutes of treadmill walking in children with CP, but no change in other muscles 10 . Similarly, we did not observe simultaneous changes in RMS and MDF that would indicate muscle fatigue in the soleusduring five minutes of treadmill walking with No Device. The exoskeleton provides a dorsiflexion torque that counters the desired action at push-off, to encourage the user to increase plantarflexor engagement 18 . The increased rate of fatigue during training with the Exo suggests that the Exosuccessfully engaged the soleus and significantly increased the rate of fatigue. These findings are specifically for a resistive ankle exoskeleton. Assistive ankle exoskeletons have been shown to reduce muscle demand for children with CP and may result in reduced muscle fatigue 31 . In ten consecutive minutes of walking with No Device and tSCS only, we observed a small but significant decline in MDF with no change in RMS. Changes in force output can also induce changes in RMS and MDF 27 . It is possible that in addition to some muscle fatigue, participants also had a simultaneous decline in force output that masked myoelectric manifestations of muscle fatigue in the RMS. We aimed to control for differences in force produced at the ankle by keeping walking speed consistent between walking conditions. But participants were allowed to use handrails for safety, which could have offloaded some bodyweight from the legs. Future work measuring force output either with an exoskeleton or instrumented treadmill could examine changes in force alongside fatigue. We observed minimal changes in the EMG RMS and MDF during the MVC task performed before and after each walking condition. Priorwork evaluating fifteen minutes of treadmill training has shown to reduce MVC force of the quadricep muscles by 11% in children with CP, with no change among TD peers 32 . In our study, we focused only on the soleus muscles, which did not indicate any fatigue with assessment of the MVC tasks alone. The increased sensory feedback when using either Exo or tSCS may improve coordination of muscles and increase the ability of the user to maximally engage in the MVC task, despite muscle fatigue 16 , 33 . Thus, future work should consider muscle fatigue and coordination of activity across multiple muscles to identify the possible mechanisms altering fatigue during walking, but not during the pre-post MVC tasks. Another possibility for no change during the MVC testing is that this task is not as physically demanding as walking for children with CP and occurs after the children have had a break from walking. Prior research has suggested that children with CP show similar and even reduced rates offatigue compared to nondisabled peers during tasks requiring maximal effort, but this maximal effort (i.e. force output) is also reduced 29 , 34 . One contributing factor to reduced maximal effort in children with CP is the potential activation of fewer muscle fibers during volitional maximal effort tasks compared to TD peers 35 . As a result, we may observe muscle fatigue more accurately during walking, where the demand on muscles could be higher than during the MVC task. There are several limitations of this study. First, we evaluated the first five minutes of walking in our initial linear mixed effects model of all walking conditions, similar to prior studies on fatigue during walking in children with CP 10 , 28 . We were unable to include the conditions with the Exo for more than five minutes due to participants taking rest breaks and using the audiovisual feedback after the first five minutes to focus on one leg at a time 26 . Future research should consider alternative analysis methods to capture these additional cofounders and evaluate fatigue over longer time periods 36 . Second, we quantified fatigue in the soleus muscle, as the exoskeleton primarily targets the plantarflexors and covers the gastrocnemius. The soleus is generally considered the more fatigue resistant plantarflexor muscle 37 , so greater changes in fatigue may be observed in the gastrocnemius. The exoskeleton may also increase the engagement of other muscles, such as the rectus femoris. Additional EMG processing will be needed to remove signal artifact from tSCS for more proximal muscles 38 . Finally, MVC was quantified with a manual dynamometer with resistance to plantarflexion provided by a researcher. Measuring MVC with a manual dynamometer is a common practice 18 , 29 , 39 , 40 , but use of an isokinetic dynamometer, such as a Biodex system, would produce more accurate force measurements. Further, a direct recording of forces produced during walking could clarify changes in RMS that may also be due to force output. In conclusion, walking with an Exo increased the rate of muscle fatigue during the first five minutes of treadmill walking compared to walking with No Device. The addition of tSCS reduced fatigue in the soleus muscle and likely increased force output and, thus, engagement with the device. These results suggest that tSCS may be a viable tool for reducing muscle fatigue during training to maximize rehabilitation benefits of physical therapy for children with CP. Data Availability All data produced in the present study are available upon reasonable request to the authors. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Author Contributions All authors conceived of and designed the research. C.R.D., V.M.L., and S.R.S. performed data collection. C.R.D., V.M.L., and K.M.S. carried out all data analysis and interpreted experimental results. C.R.D. and V.M.L. prepared figures and drafted the manuscript. All authors edited and revised the manuscript and approved the final version. Funding This work was supported by Seattle Children’s Hospital CP Research Pilot Study Fund 2022 Award, NSF Graduate Research Fellowship Program Award DGE-1762114, and the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R01NS091056. Declarations Competing interests C.T.M. serves as a clinical advisor to the company SpineX, who provided the stimulator for the study. SpineX also licensed IP generated by the team at the University of Washington including C.R.D., S.R.S., C.T.M., and K.M.S.. Acknowledgements The authors thank the children and their families for the time they dedicated to the research. We also thank the fellow researchers who supported our data collection visits and preliminary data analysis, especially Katherine Green and Yusuke Maruo. 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