Work-related musculoskeletal disorders (WMSDs) are common in the agricultural industry due to repetitive tasks, like plant transplanting, which involve sustained bending, squatting, and awkward postures. This study uses wearable sensors to evaluate human biomechanics during simulated transplanting and assesses the potential of collaborative robot (cobot) assistance to reduce physical strain. Sixteen participants performed transplanting tasks under manual and cobot-assisted conditions. Kinematic and electromyographic (EMG) data were collected using Xsens motion capture and Trigno EMG systems. Cobot assistance significantly reduced the segment velocity and acceleration in key spinal regions (L5/S1, L1/T12, T1/C7), indicating lower dynamic spinal loading. It also altered muscle activation, decreasing biceps brachii use while increasing activation in stabilizing muscles such as the flexor carpi radialis, brachioradialis, and upper trapezius. Task duration decreased by 59.46%, suggesting improved efficiency. These findings highlight cobots’ potential to enhance ergonomic outcomes by encouraging controlled movements and reducing postural stress. However, the shift in muscle activation underscores the need for task-specific cobot tuning. This research supports the use of integrated IMU and EMG systems to inform cobot design and enable real-time biomechanical monitoring in labor-intensive settings.

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.