Video- and sensor-based gait analysis systems are rapidly emerging for use in ‘real world’ scenarios outside of typical instrumented motion analysis laboratories. Unlike laboratory systems, such systems do not use kinetic data from force plates, rather, gait events such as initial contact (IC) and terminal contact (TC) are estimated from video and sensor signals. There are, however, detection errors inherent in kinematic gait event detection methods (GEDM) and comparative study between classic laboratory and video/sensor-based systems is warranted. For this study, three kinematic methods: coordinate based treadmill algorithm (CBTA), shank angular velocity (SK), and foot velocity algorithm (FVA) were compared to ‘gold standard’ force plate methods (GS) for determining IC and TC in adults (n = 6), typically developing children (n = 5) and children with cerebral palsy (n = 6). The root mean square error (RMSE) values for CBTA, SK, and FVA were 27.22, 47.33, and 78.41 ms, respectively. On average, GED was detected earlier in CBTA and SK (CBTA: −9.54 ± 0.66 ms, SK: −33.41 ± 0.86 ms) and delayed in FVA (21.00 ± 1.96 ms). The statistical model demonstrated insensitivity to variations in group, side, and individuals. Out of three kinematic GEDMs, SK GEDM can best be used for sensor-based gait event detection.

Recumbent stationary cycling is a potential exercise modality for individuals with cerebral palsy (CP) that lack the postural control needed for upright exercises. Functional electrical stimulation (FES) of lower extremity muscles can help such individuals reach the cycling intensities that are required for aerobic benefits. The aim of this study was to examine the effect of cycling with and without FES assistance to that of a no-intervention control group on the cardiorespiratory fitness of children with CP. Thirty-nine participants were randomized to a FES group that underwent an 8-week FES-assisted cycling program, the volitional group (VOL), who cycled without FES, or a no-intervention control group (CON) (15 FES, 11 VOL, 13 CON). Cadence, peak VO₂, and net rise in heart rate were assessed at baseline, end of training, and washout (8-weeks after cessation of training). Latent growth curve modeling was used for analysis. The FES group showed significantly higher cycling cadences than the VOL and CON groups at POST and WO. There were no differences in improvements in the peak VO₂ and peak net HR between groups. FES-assisted cycling may help children with CP attain higher cycling cadences and to retain these gains after training cessation. Higher training intensities may be necessary to obtain improvements in peak VO₂ and heart rate.

Machine learning-based gait systems facilitate the real-time control of gait assistive technologies in neurological conditions. Improving such systems needs the identification of kinematic signals from inertial measurement unit wearables (IMUs) that are robust across different walking conditions without extensive data processing. We quantify changes in two kinematic signals, acceleration and angular velocity, from IMUs worn on the frontal plane of bilateral shanks and thighs in 30 adolescents (8–18 years) on a treadmills and outdoor overground walking at three different speeds (self-selected, slow, and fast). Primary curve-based analyses included similarity analyses such as cosine, Euclidean distance, Poincare analysis, and a newly defined bilateral symmetry dissimilarity test (BSDT). Analysis indicated that superior–inferior shank acceleration (SI shank Acc) and medial–lateral shank angular velocity (ML shank AV) demonstrated no differences to the control signal in BSDT, indicating the least variability across the different walking conditions. Both SI shank Acc and ML shank AV were also robust in Poincare analysis. Secondary parameter-based similarity analyses with conventional spatiotemporal gait parameters were also performed. This normative dataset of walking reports raw signal kinematics that demonstrate the least to most variability in switching between treadmill and outdoor walking to help guide future machine learning models to assist gait in pediatric neurological conditions.

Progressive gait impairments remain a major challenge for children with cerebral palsy (CP), often limiting independence and quality of life. Although neuromuscular electrical stimulation (NMES) has shown promise for improving gait, most NMES-assisted gait studies in children with CP have targeted only a single muscle. In this exploratory study, we characterized acute biomechanical responses of six children with CP to seven NMES protocols during treadmill walking. The protocols comprised multi-muscle stimulation, single-muscle stimulation, and an individualized physical therapist-derived (PT-derived) condition. Each protocol was compared with a no-stimulation baseline and typically developing (TD) reference data. The primary and secondary outcomes were the Gait Deviation Index (GDI) and Coefficient of Variation (CV), complemented by the targeted outcome measures, including lower-limb joint kinematics using phase-by-phase Dynamic Time Warping (DTW), maximum knee extension, and stride length. We found that the comparison between multi-muscle and single-muscle conditions did not identify a statistically significant difference in GDI. CV analyses showed smaller distances from TD reference values for multi-muscle conditions, although not significant. Stimulation conditions significantly affected maximum knee extension and DTW during loading response at the ankle and pre-swing at the hip; however, no individual condition differed significantly from no stimulation after false discovery rate correction. The PT-derived condition showed favorable directional changes in selected outcomes, but no consistent advantage over the other protocols. These preliminary observations characterize acute response patterns and generate hypotheses for future studies; they do not establish the comparative efficacy of multi-muscle, single-muscle, or individualized NMES protocols.

Progressive gait impairments remain a major challenge for children with cerebral palsy (CP), often limiting independence and quality of life. While neuromuscular electrical stimulation (NMES) shows promise for improving gait, prior studies in this population have largely targeted only a single muscle. To address this gap, we examined multi-muscle versus single-muscle NMES during treadmill walking in six children with CP, comparing seven protocols, including an individualized physical-therapist-derived (PT-derived) protocol against a no-stimulation baseline and typically developing controls. Gait quality was assessed using the Gait Deviation Index (GDI) as the primary outcome, alongside the Coefficient of Variation (CV) and targeted kinematic measures. Results were mixed: group-level GDI differences were not significant, though CV analyses showed multi-muscle conditions produced more consistently favorable gait patterns than single-muscle stimulation. Stimulation condition significantly affected maximum knee extension and Dynamic Time Warping during ankle loading response and hip pre-swing; however, after correction for multiple comparisons, no protocol differed significantly from baseline. The PT-derived condition showed favorable directional trends across several outcomes, though not consistently significant. These preliminary findings suggest multi-muscle, individualized NMES strategies may produce phase- and joint-specific biomechanical effects, but larger studies with better gait-phase detection and longer exposure are needed before drawing clinical conclusions.