Purpose: Sarcopenia is a symptom in which muscle mass decreases due to decreasing in the number of muscle fibers and muscle cross-sectional area as aging. This study aimed to develop a machine learning classification model for predicting sarcopenia through a inertial measurement unit (IMU)-based physical performance measurement data of female elderly. Patients and Methods: Seventy-eight female subjects from an elderly population (aged: 78.8± 5.7 years) volunteered to participate in this study. To evaluate the physical performance of the elderly, the experiment conducted timed-up-and-go test (TUG) and 6-minute walk test (6mWT) with worn a single IMU. Based on literature review, 132 features were extracted from collected data. Feature selection was performed through the Kruskal–Wallis test, and features datasets were constructed according to feature selection. Three major machine learning-based classification algorithms classified the sarcopenia group in each dataset, and the performance of classification models was compared. Results: As a result of comparing the classification model performance for sarcopenia prediction, the k-nearest neighborhood algorithm (kNN) classification model using 40 major features of TUG and 6mWT showed the best performance at 88%. Conclusion: This study can be used as a basic research for the development of self-monitoring technology for sarcopenia.

A decrease in dynamic balance ability (DBA) in the elderly is closely associated with aging. Various studies have investigated different methods to quantify the DBA in the elderly through DBA evaluation methods such as the timed up and go test (TUG) and the six-minute walk test (6MWT), applying the G-Walk wearable system. However, these methods have generally been difficult for the elderly to intuitively understand. The goal of this study was thus to generate a regression model based on machine learning (ML) to predict the age of the elderly as a familiar indicator. The model was based on inertial measurement unit (IMU) data as part of the DBA evaluation, and the performance of the model was comparatively analyzed with respect to age prediction based on the IMU data of the TUG test and the 6MWT. The DBA evaluation used the TUG test and the 6MWT performed by 136 elderly participants. When performing the TUG test and the 6MWT, a single IMU was attached to the second lumbar spine of the participant, and the three-dimensional linear acceleration and gyroscope data were collected. The features used in the ML-based regression model included the gait symmetry parameters and the harmonic ratio applied in quantifying the DBA, in addition to the features of description statistics for IMU signals. The feature set was differentiated between the TUG test and the 6MWT, and the performance of the regression model was comparatively analyzed based on the feature sets. The XGBoost algorithm was used to train the regression model. Comparison of the regression model performance according to the TUG test and 6MWT feature sets showed that the performance was best for the model using all features of the TUG test and the 6MWT. This indicated that the evaluation of DBA in the elderly should apply the TUG test and the 6MWT concomitantly for more accurate predictions. The findings in this study provide basic data for the development of a DBA monitoring system for the elderly.

This study aims to evaluate the reliability and validity of the Leg Strength Analyzer (IB-LS) in assessing isometric knee flexion and extension strength in elderly adults, compare its performance with that of the CSMI dynamometer, and examine its agreement with isokinetic knee strength measurements. A total of 21 elderly participants (mean age: 65.10 ± 4.56 years) were recruited. Participants underwent knee flexion and extension strength assessments using both the IB-LS and CSMI devices, with isokinetic strength at 60°/s measured in a follow-up session at least one day later. The IB-LS demonstrated high test–retest reliability in elderly adults (ICC = 0.856–0.987). The validity analysis comparing IB-LS isometric peak torque with CSMI isometric peak torque showed moderate to high validity (ICC = 0.826–0.946). Furthermore, IB-LS isometric peak torque and CSMI isokinetic 60°/s peak torque demonstrated high agreement (ICC = 0.775–0.881), demonstrating its strong association with isokinetic strength assessments. Bland–Altman analysis revealed that mean differences between IB-LS and CSMI isometric peak torque values ranged from 13.2 to 93.5 Nm, with limits of agreement (LoA) spanning from −55.8 to 192.5 Nm. When comparing IB-LS isometric peak torque with CSMI isokinetic 60°/s peak torque, mean differences ranged from 31.0 to 56.0 Nm, with LoA from −28.5 to 138.9 Nm. The IB-LS is a reliable and valid tool for evaluating isometric knee strength in elderly adults. Its strong agreement with the CSMI dynamometer and close correlation with isokinetic strength measurements indicate that IB-LS can be a feasible alternative for assessing knee strength in clinical and research settings focused on elderly populations.

Although the older population has been rapidly growing, the availability of formal caregivers remains limited. Assistance provided by care robots has helped lower this burden; however, whether using a care robot while providing excretion care (EC) is quantitatively increasing or decreasing caregivers’ physical care burden has not been extensively studied. This study aimed to quantitatively compare the physical burden experienced by caregivers while providing manual excretion care (MC) using a paper diaper versus robot-aided care (RC). Ten formal caregivers voluntarily participated in the experiment. MC and RC tasks were structuralized according to phases and classified by characteristics. The experiment was conducted in a smart care space. The physical load of formal caregivers was estimated by muscular activity and subjective rating of perceived physical discomfort. The results demonstrated that although the physical load on the lower back and upper extremities during the preparation and post-care phases were greater in RC than MC, RC markedly alleviated caregivers’ physical load when performing front tasks. In the preparation-care phases, the physical loads on the lower back and upper extremities were approximately 40.2 and 39.6% higher in the case of RC than MC, respectively. Similar to the preparation-care phases, the physical loads on the lower back and upper extremities during post-care phases were approximately 39.5 and 61.7% greater in the case of RC than MC, respectively. On the other hand, in the front-care phases, the physical loads on the lower back and upper extremities were approximately 25.6 and 34.9% lower in the case of RC than MC, respectively. These findings can quantitatively explain the effectiveness and features of a care robot to stakeholders and provide foundational research data for the development of EC robots. This study emphasizes the implementation and promotion of the dissemination, popularization, and development of care robots to fulfill formal caregiving needs.

An inappropriate thermal environment negatively impacts workers, causing mental stress and safety accidents. Unskilled workers are more vulnerable to industrial accidents and thermal stress compared with skilled workers due to suboptimal and unfamiliar work. Previous studies have focused on individual characteristics (such as gender, age, and race), with limited emphasis on the thermal comfort sensation. This study identified the preferred thermal environment according to work experience and how mental stress differed between unskilled and skilled workers by examining their thermal comfort sensation. Predicted mean vote (PMV) was used as an indicator of the thermal environment, and five environments were constructed for PMV: −2, −1, 0, 1, and 2. Participants were recruited among current workers and the public. Mental stress and thermal comfort sensation were assessed using heart rate variability and thermal comfort vote, respectively. This study demonstrated that the skilled group experienced higher mental stress and a lower thermal comfort sensation. Contrastingly, in the sensitivity analysis, the unskilled group exhibited greater sensitivity to changes in the thermal environment. Through a comprehensive analysis, this study derived an optimal PMV range for each group. The findings can provide a reference for configuring the optimal thermal environment of the workplace.

A compromised stability in the elderly population is considered a major factor for fall risk assessment. The dynamic stability of human gait with various mathematical metrics has been extensively studied to find a prediction index and fall prevention strategies that can be embedded in a wearable monitoring sensor. In this study, the zero-moment point method (ZMP) was utilized for analyzing the gait stability of young and elderly female populations. Participants in the young and elderly female groups with no musculoskeletal disorders and fall experience were asked to walk at a habitual speed on 10 m flat ground. Dynamic instability is defined by the percentage of the ZMP values that fall outside the base of support during one gait cycle. The ZMP trajectory between the left and right leg swing was not symmetrical considering flat-ground walking. Also, there was no statistical difference in the dynamic stability in the anterior–posterior direction (71.3 ± 7.9% for the young group and 73.6 ± 7.6% for the elderly group), but walking in the medial–lateral direction was more stable in the elderly group (53.9 ± 8.6%) than in the young group (44.1 ± 11.2%) because the habitual walking speed decreased in the elderly group.