Gait, fundamental to human movement, becomes compromised in cerebral palsy (CP), a childhood‐onset central nervous system motor disorder. Precise assessment of patients’ gait is crucial for tailored rehabilitation interventions. Currently, clinical scales assessing CP gait dysfunction mostly, while valuable, rely on subjective clinician observations. To enhance objectivity and efficiency in CP diagnosis and rehabilitation, there is a need for more objective assessment procedures. This study introduces a multi‐modal and multi‐scale feature fusion (MMFF) framework, a new framework for automating gait dysfunction assessment in children with CP. By utilizing surface electromyography and acceleration signals recorded during children's walking, MMFF generates a feature vector enriched with adaptively refined feature maps, cross‐mode correlations, and both local and global information. Validation of MMFF's effectiveness is evident through an accomplished classification accuracy of 99.13%. The mean values for precision, recall, and F1‐score in Gross Motor Function Classification System (GMFCS)‐1, GMFCS‐2, and GMFCS‐3, reaching 99.00%, 99.00%, and 98.33%, respectively, further reflect the accuracy of functional assessments at each level. This study underscores MMFF's potential as an objective, streamlined tool for clinicians, promising improved gait assessment and well‐informed rehabilitation strategies for children with CP.

Retail robots have increasingly become popular in retail stores, serving as collaborative assistants by completing tedious and repetitive tasks. As key participants in a retail environment, understanding how these robots influence the movements and behaviors of nearby customers can provide valuable insights to improve robot ergonomics. This article examined neuromuscular information changes with and without the presence of a retail robot. Sixteen participants were recruited to perform item picking and sorting tasks in a high-fidelity retail setting. Surface electromyography (EMG) signals were collected from four muscles—biceps brachii, brachioradialis, upper trapezius and erector spinae (ES), to measure muscle activity and EMG-EMG coherence. A global decrease in power spectral density (PSD) was observed across all monitored muscles with the robot's presence, indicating a reduction in overall muscular energy expenditure. The introduction of the retail robot also led to a significant reduction in the peak values and waveform length of the ES muscle. These findings not only highlight the potential of retail robots to enhance the overall shopping experience but also provide crucial insights for enhancing ergonomics during robotic design.

While retail mobile cobots typically handle routine service tasks, they may also serve as proactive safety-intervention systems, such as guiding individuals away from hazardous areas. However, the neuromuscular impact of such proactive robotic guidance on targeted users remains poorly understood. We conducted a mixed-reality experiment with 16 participants, simulating a retail shelf-picking task across three robot-initiated guidance modes: no robot, low-speed, and high-speed robotic intervention. Surface electromyography (EMG) recorded from 10 muscle groups showed that robotic guidance significantly increased muscle activity compared to the baseline ( p = .001). However, increasing robot speed did not significantly raise muscle activation ( p = .064). These findings suggest that carefully designed faster proactive guidance may help reduce hazard exposure time while maintaining comparable levels of neuromuscular demand relative to slower interventions. Future research would systematically evaluate the broader impacts of robotic guidance behaviors on human movements, neuromuscular, and safety outcomes.

Background: Gait is a clinically relevant indicator of functional decline in aging populations. However, most studies classify older adults by chronological rather than functional age, which may obscure early impairments detectable through kinematic profiling. This study examined whether stratifying older adults by functional status using the Short Physical Performance Battery (SPPB) enhances sensitivity in detecting gait abnormalities and instability-related compensatory patterns. Methods: A total of 190 adults completed gait trials on a pressure-sensitive walkway. Twenty-eight spatial, temporal, and variability-based gait parameters were derived. Participants were categorized as young adults or older adults, who were further stratified into high- and low-functioning groups based on SPPB scores. Analysis of covariance (ANCOVA) was performed, adjusting for habitual walking speed to isolate functional effects. Findings: After adjusting for speed, the low-functioning group demonstrated longer stance and double-support durations, wider step width, and greater step-to-step variability in both spatial and temporal domains compared with both the high-functioning and young reference groups. These findings indicate a compensatory, instability-driven control strategy that challenges the assumption of a “slower but steady” gait in aging. High-functioning older adults exhibited gait patterns more closely resembling those of younger adults. Interpretation: Functional classification using the SPPB provided greater sensitivity than chronological age in detecting early mobility decline. Gait variability emerged as a salient biomarker of impaired neuromuscular control. Integrating quantitative gait profiling with validated functional assessments may improve early screening, targeted intervention, and fall prevention strategies.

Efficient walking depends on the interaction between both gait mechanisms and energy demand; however, this relationship may differ across age and functional groups. This study examines this association in younger and older adults. Sixty-four participants were divided into two groups: younger adults aged ≤40 years and older adults with reduced physical function, defined as Short Physical Performance Battery scores ≤9, and. Metabolic outcomes included peak, gross reserve metabolic costs, while gait metrics included 10 spatial, temporal, and variability measures. Pearson correlations were calculated separately to examine the differences within each group. Younger adults demonstrated stronger associations between metabolic outcomes and temporal gait measures, whereas older adults showed stronger associations involving gait variability, asymmetry, and support-time measures. These findings suggest that age plays a critical role when there is a shift in gait and metabolic cost coupling, with inefficient locomotion in older adults more closely related to variability and stability control.