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IntroductionSarcopenia is a common age-related disease, defined as a decrease in muscle strength and function owing to reduced skeletal muscle. One way to diagnose sarcopenia is through gait analysis and foot-pressure imaging.Motivation and research gapWe collected our own multimodal dataset from 100 subjects, consisting of both foot-pressure and skeleton data with real patients, which provides a unique resource for future studies aimed at more comprehensive analyses. While artificial intelligence has been employed for sarcopenia detection, previous studies have predominantly focused on skeleton-based datasets without exploring the combined potential of skeleton and foot pressure dataset. This study conducts separate experiments for foot-pressure and skeleton datasets, it demonstrates the potential of each data type in sarcopenia classification.MethodsThis study had two components. First, we collected skeleton and foot-pressure datasets and classified them into sarcopenia and non-sarcopenia groups based on grip strength, gait performance, and appendicular skeletal muscle mass. Second, we performed experiments on the foot-pressure dataset using the ResNet-18 and spatiotemporal graph convolutional network (ST-GCN) models on the skeleton dataset to classify normal and abnormal gaits due to sarcopenia. For an accurate diagnosis, real-time walking of 100 participants was recorded at 30 fps as RGB + D images. The skeleton dataset was constructed by extracting 3D skeleton information comprising 25 feature points from the image, whereas the foot-pressure dataset was constructed by exerting pressure on the foot-pressure plates.ResultsAs a baseline evaluation, the accuracies of sarcopenia classification performance from foot-pressure image using Resnet-18 and skeleton sequences using ST-GCN were identified as 77.16 and 78.63%, respectively.DiscussionThe experimental results demonstrated the potential applications of sarcopenia and non-sarcopenia classifications based on foot-pressure images and skeleton sequences.

Background and Objectives: Previous studies have revealed that independent variables (lower extremity strength, postural control ability, and body composition) influence gait performance and variability, but the difference in the relative influence between these variables is unclear. Hence, this study determines the variable that is the most influential predictor of gait performance and variability among potential independent variables in the elderly. Materials and Methods: Seventy-eight subjects aged ≥60 years participated. For each subject, the gait variables and lower extremity muscle strength were measured using an accelerometer worn on both feet during a 6-minute walk and a manual force sensor, respectively. The static balance ability was measured through two force plates, and the body composition was measured by applying bioelectrical impedance analysis. Linear regression analyses were performed stepwise to determine whether these variables affect gait performance and variability. Results: After adjusting for sex and gait performance, the ankle strength, body fat mass, mean velocity in the medial–lateral direction, ankle plantar flexion strength, and girth were predictors of gait speed dorsiflexion, gait performance, swing width of the gait performance, walking speed, and gait variability, respectively. Conclusions: Overall, gait performance in the elderly is related to muscle strength, postural control, and body composition in a complex manner, but gait variability appears to be more closely related to ankle muscle strength. This study provides further evidence that muscle strength is important in motor function and stability.

The rambling and trembling analysis separates the center of pressure (COP) fluctuations into two components: rambling (supraspinal contribution) and trembling (muscle stiffness / reflexive properties contribution). We examined whether the trembling component is correlated to the contractile properties (muscle stiffness and contraction time) of lower limb superficial skeletal muscles to experimentally test the rambling and trembling hypothesis. We hypothesized that muscle stiffness and contraction time, would be: (a) more correlated with; and (b) have a greater impact on the trembling component compared to the rambling component. Thirty-two healthy young adults were recruited for the study and tensiomyography was used to assess mechanical muscle responses to a single electrical stimulus to calculate muscle stiffness and contraction time based on radial muscle belly displacement measurements of lower limb muscles unilaterally. Moreover, upright postural control was assessed using a force plate to record ground reaction forces and moments and calculate the COP fluctuations during two 30 seconds trials. From the COP fluctuations, rambling and trembling time series were extracted, and all fluctuation time series were described using a number of different time-domain and frequency-domain parameters in both the anterior-posterior and medial-lateral directions. Our results demonstrated that both muscle stiffness and contraction time were moderately correlated with time-domain and frequency-domain parameters of the trembling component, as compared with those of the rambling component which was not as well correlated. Moreover, they also predicted the trembling component better. Overall, these results imply that postural control during quiet stance is, in part, related to intrinsic muscle stiffness in the lower extremities. Moreover, we showed that the rambling and trembling hypothesis is effective in separating postural sway fluctuations during upright posture to extract the contributions of muscle stiffness / reflexive properties (trembling), and likely the supraspinal contribution (rambling).