An association between respiratory muscle weakness and sarcopenia may provide a clue to the mechanism of sarcopenia development. We aimed to clarify this relationship among community-dwelling older adults. In total, 117 community-dwelling older adults were assessed and classified into 4 groups: robust, respiratory muscle weakness, sarcopenia, and respiratory sarcopenia. The respiratory sarcopenia group (12%) had a significantly higher percentage of males and had lower BMI, skeletal muscle index, skeletal muscle mass, phase angle, and oral function than the robust group (32.5%). All physical functions were significantly lower. The respiratory muscle weakness group (54.7%) had a significantly lower BMI and slower walking speed, compared with the robust group. The sarcopenia group (0.8%) was excluded from the analysis. The percent maximum inspiratory pressure was significantly lower in both the respiratory muscle weakness and respiratory sarcopenia groups, compared with the robust group. Almost all participants with sarcopenia showed respiratory muscle weakness. In addition, approximately 50% had respiratory muscle weakness, even in the absence of systemic sarcopenia, suggesting that respiratory muscle weakness may be the precursor of sarcopenia. The values indicating physical function and skeletal muscle mass in the respiratory muscle weakness group were between those in the robust and the respiratory sarcopenia groups.

Background: Ultrasonography (US) is a feasible and accessible method for the measurement of skeletal muscle mass. This technique presents acceptable intra-rater reliability; however, there are a few reports on its inter-rater reliability. Additionally, relative reliability should equally be inspected to determine the presence of systematic errors. Therefore, this study aimed to investigate the intra- and inter-rater reliabilities and absolute reliability of rectus femoris muscle thickness as measured using US. Methods: The participants included in our study comprised 12 healthy young men (26.5 ± 3.9 years. Rectus femoris muscle thickness was measured from the right side of the thigh using US by two trained physical therapists. Inter- and intra-rater reliabilities were determined using the intraclass correlation coefficient (ICC) (1, 1) and ICC (2, 1) methods, respectively. Absolute reliability was evaluated using Bland − Altman analysis. Additionally, we calculated the minimal detectable change at the 95% level of confidence (MDC ₉₅ ). Result: According to the results of the Bland − Altman analysis, no fixed or proportional errors were present. The ICC (1, 1) was 0.95, and the ICC (2, 1) was 0.70. The MDC ₉₅ values of rectus femoris thickness for the intra- and inter-rater reliabilities were 2.0 mm and 4.3 mm, respectively. Conclusions: In our study, intra- and inter-rater reliabilities were measured at “excellent” and “moderate” levels in the healthy individuals based on a previously defined scale. Moreover, we determined the measurement error for quantifying rectus femoris thickness. Therefore, the measurement of rectus femoris thickness using US could be considered applicable in clinical research.

Recent advancements have made two-dimensional (2D) clinical gait analysis systems more accessible and portable than traditional three-dimensional (3D) clinical systems. This study evaluates the reliability and validity of gait measurements using monocular and composite camera setups with VisionPose, comparing them to the Vicon 3D motion capture system as a reference. Key gait parameters—including hip and knee joint angles, and time and distance factors—were assessed under normal, maximum speed, and tandem gait conditions during level walking. The results show that the intraclass correlation coefficient (ICC(1,k)) for the 2D model exceeded 0.969 for the monocular camera and 0.963 for the composite camera for gait parameters. Time–distance gait parameters demonstrated excellent relative agreement across walking styles, while joint range of motion showed overall strong agreement. However, accuracy was lower for measurements during tandem walking. The Cronbach’s alpha coefficient for time–distance parameters ranged from 0.932 to 0.999 (monocular) and from 0.823 to 0.998 (composite). In contrast, for joint range of motion, the coefficient varied more widely, ranging from 0.826 to 0.985 (monocular) and from 0.314 to 0.974 (composite). The correlation coefficients for spatiotemporal gait parameters were greater than 0.933 (monocular) and 0.837 (composite). However, for joint angle parameters, the coefficients were lower during tandem walking. This study underscores the potential of 2D models in clinical applications and highlights areas for improvement to enhance their reliability and application scope.

Two-dimensional (2D) clinical gait analysis systems are more affordable and portable than contemporary three-dimensional (3D) clinical models. Using the Vicon 3D motion capture system as the standard, we evaluated the internal statistics of the Imasen and open-source OpenPose gait measurement systems, both designed for 2D input, to validate their output based on the similarity of results and the legitimacy of their inner statistical processes. We measured time factors, distance factors, and joint angles of the hip and knee joints in the sagittal plane while varying speeds and gaits during level walking in three in-person walking experiments under normal, maximum-speed, and tandem scenarios. The intraclass correlation coefficients of the 2D models were greater than 0.769 for all gait parameters compared with those of Vicon, except for some knee joint angles. The relative agreement was excellent for the time–distance gait parameter and moderate-to-excellent for each gait motion contraction range, except for hip joint angles. The time–distance gait parameter was high for Cronbach’s alpha coefficients of 0.899–0.993 but low for 0.298–0.971. Correlation coefficients were greater than 0.571 for time–distance gait parameters but lower for joint angle parameters, particularly hip joint angles. Our study elucidates areas in which to improve 2D models for their widespread clinical application.

The intricate dynamics of the human musculoskeletal system require complex mathematical computations for accurate simulation, posing challenges in estimating muscle activity. Real-time processes and comprehensive analysis greatly influence the effectiveness of monitoring applications. The objective of our research was to enhance real-time muscle activity predictions by incorporating environmental data into human musculoskeletal simulations, focusing on the upper extremity. Our model, developed using MuJoCo software, consisted of 50 Hill-type muscles and integrated environmental context. Information on human posture was collected from single RGBD sensors positioned at 32 three-dimensional node locations. We used inverse kinematics computations to convert this data into joint angle parameters for our simulation model. The stretch reflex of each muscle was regulated to initiate movement in the target joints. Desired muscle stretch length was derived from the mechanical interaction between the bone structure and the muscle-tendon actuator connected to it. Our model also allowed for the application of artificial force to simulate external load conditions. To validate our model, we performed basic movements with the upper extremity and measured muscle activity using EMG sensors. Our results confirmed the model’s ability to accurately predict muscle activation and the force exerted by each muscle. Further experiments demonstrated its potential for seamless integration with dynamic environmental conditions, thereby enhancing its utility as a comprehensive human physical monitoring system.

Significant developments in sensing technology have had many impacts, enhancing monitoring and assessment accuracy across diverse fields. In the field of physical therapy, sensing, which plays a pivotal role in tele-physiotherapy, rapidly expanded amid the COVID-19 pandemic. Its primary objective is to monitor biological signals and patient movements at remote locations. To further enhance the effectiveness and the scope of tele-physiotherapy, it is essential to further develop sensing and data analysis technologies. However, there are usability and analysis issues that have limited its use. The development of these technologies will not only enhance the accuracy of deep learning by AI through the acquisition of big data, but also has the potential to elucidate movement characteristics associated with movement disorders or pathological conditions. Furthermore, improving sensing technologies can broaden applications extending beyond tele-physiotherapy to impact daily life. Looking forward, it holds promise for improving our understanding of disease prognosis and progression.