Upper torso and pelvis linear velocity during the downswing of elite golfers

BACKGROUND: During a golf swing, analysis of the movement in upper torso and pelvis is a key step to determine a motion control strategy for accurate and consistent shots. However, a majority of previous studies that have evaluated this movement limited their analysis only to the rotational movement of segments, and translational motions were not examined. Therefore, in this study, correlations between translational motions in the 3 axes, which occur between the upper torso and pelvis, were also examined. METHODS: The experiments were carried out with 14 male pro-golfers (age: 29 ± 8 years, career: 8.2 ± 4.8years) who registered in the Korea Professional Golf Association (KPGA). Six infrared cameras (VICON; Oxford Metrics, Oxford, UK) and SB-Clinc software (SWINGBANK Ltd, Korea) were used to collect optical marker trajectories. The center of mass (CoM) of each segment was calculated based on kinematic principal. In addition, peak value of CoM velocity and the time that each peak occurred in each segment during downswing was calculated. Also, using cross-correlation analysis, the degree of coupling and time lags of peak values occurred between and within segments (pelvis and upper torso) were investigated. RESULTS: As a result, a high coupling strength between upper torso and pelvis with an average correlation coefficient = 0.86 was observed, and the coupling between segments was higher than that within segments (correlation coefficient = 0.81 and 0.77, respectively). CONCLUSIONS: Such a high coupling at the upper torso and pelvis can be used to reduce the degree of motion control in the central nervous system and maintain consistent patterns in the movement. The result of this study provides important information for the development of optimal golf swing movement control strategies in the future

A Novel Detection Model and Its Optimal Features to Classify Falls from Low- and High-Acceleration Activities of Daily Life Using an Insole Sensor System

In order to overcome the current limitations in current threshold-based and machine learning-based fall detectors, an insole system and novel fall classification model were created. Because high-acceleration activities have a high risk for falls, and because of the potential damage that is associated with falls during high-acceleration activities, four low-acceleration activities, four high-acceleration activities, and eight types of high-acceleration falls were performed by twenty young male subjects. Encompassing a total of 800 falls and 320 min of activities of daily life (ADLs), the created Support Vector Machine model’s Leave-One-Out cross-validation provides a fall detection sensitivity (0.996), specificity (1.000), and accuracy (0.999). These classification results are similar or superior to other fall detection models in the literature, while also including high-acceleration ADLs to challenge the classification model, and simultaneously reducing the burden that is associated with wearable sensors and increasing user comfort by inserting the insole system into the shoe

Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester

Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to agriculture. For this reason, this study proposes weakly supervised crop area segmentation (WSCAS) to identify the uncut crop area efficiently for path guidance. Weakly supervised learning has advantage for training models because it entails less laborious annotation. The proposed method trains the classification model using area-specific images so that the target area can be segmented from the input image based on implicitly learned localization. This way makes the model implementation easy even with a small data scale. The performance of the proposed method was evaluated using recorded video frames that were then compared with previous deep-learning-based segmentation methods. The results showed that the proposed method can be conducted with the lowest inference time and that the crop area can be localized with an intersection over union of approximately 0.94. Additionally, the uncut crop edge could be detected for practical use based on the segmentation results with post-image processing such as with a Canny edge detector and Hough transformation. The proposed method showed the significant ability of using automatic perception in agricultural navigation to infer the crop area with real-time level speed and have localization comparable to existing semantic segmentation methods. It is expected that our method will be used as essential tool for the automatic path guidance system of a combine harvester

eXplainable AI (XAI)-Based Input Variable Selection Methodology for Forecasting Energy Consumption

This research proposes a methodology for the selection of input variables based on eXplainable AI (XAI) for energy consumption prediction. For this purpose, the energy consumption prediction model (R2 = 0.871; MAE = 2.176; MSE = 9.870) was selected by collecting the energy data used in the building of a university in Seoul, Republic of Korea. Applying XAI to the results from the prediction model, input variables were divided into three groups by the expectation of the ranking-score (Fqvar) (10 ≤ Strong, 5 ≤ Ambiguous Weak Strong + Ambiguous group (R2 = 0.917; MAE = 1.859; MSE = 6.639) or the Strong group (R2 = 0.916; MAE = 1.816; MSE = 6.663) showed higher prediction results than other cases (p Strong group and the Strong + Ambiguous group (R2: p = 0.408; MAE: p = 0.488; MSE: p = 0.478). This means that when considering the input variables of the Strong group (Fqvar: Year = 14.8; E-Diff = 12.8; Hour = 11.0; Temp = 11.0; Surface-Temp = 10.4) determined by the XAI-based methodology, the energy consumption prediction model showed excellent performance. Therefore, the methodology proposed in this study is expected to determine a model that can accurately and efficiently predict energy consumption

Analysis of sensory system aspects of postural stability during quiet standing in adolescent idiopathic scoliosis patients

Abstract Background The aim of this study was to quantitatively analyze quite standing postural stability of adolescent idiopathic scoliosis (AIS) patients in respect to three sensory systems (visual, vestibular, and somatosensory). Method In this study, we analyzed the anterior-posterior center of pressure (CoP) signal using discrete wavelet transform (DWT) between AIS patients (n = 32) and normal controls (n = 25) during quiet standing. Result The energy rate (∆E EYE %) of the CoP signal was significantly higher in the AIS group than that in the control group at levels corresponding to vestibular and somatosensory systems (p < 0.01). Conclusions This implies that AIS patients use strategies to compensate for possible head position changes and spinal asymmetry caused by morphological deformations of the spine through vestibular and somatosensory systems. This could be interpreted that such compensation could help them maintain postural stability during quiet standing. The interpretation of CoP signal during quiet standing in AIS patients will improve our understanding of changes in physical exercise ability due to morphological deformity of the spine. This result is useful for evaluating postural stability before and after treatments (spinal fusion, bracing, rehabilitation, and so on)

Improvement of Gear Durability for an 86 kW Class Agricultural Tractor Transmission by Material Selection

This study was conducted to ensure gear durability and design optimal transmission of agricultural tractors. A field test was conducted using an 86 kW agricultural tractor for plow and rotary tillage, which are typical agricultural operations. The field test was completed after about 107 h due to transmission noise and operational problems. As a result of disassembling the transmission, it was found that the range shift A and B gears were damaged. In the case of the range shift A gear, it was judged that plastic deformation occurred due to low contact stress, and the bending stress was low, therefore gear tooth breakage occurred in the range shift B gear. In order to ensure the durability of the transmission, four materials of alloy steel for machine structural use, such as SCr420, SNCM220, SCM822, and SNC815, were selected, and the safety factor and service life according to the gear materials were compared using simulation software. As a result of simulation analysis, SCM822 satisfied the target life value and was selected as a material for change. The damaged range shift A and B gears were changed to SCM822, and an axle dynamometer test was performed for the verification of the modified transmission. After conducting the axle dynamometer test, the transmission was disassembled, and it was confirmed that the range shift A and B gears were in normal condition. Therefore, it was considered that the durability of the transmission was ensured by satisfying the target life requirements of the gears. In the future, the transmission simulation model for 86 kW class agricultural tractor is expected to be utilized for the development of tractor transmissions, cost reduction, and optimal design

Improvement of Gear Durability for an 86 kW Class Agricultural Tractor Transmission by Material Selection

This study was conducted to ensure gear durability and design optimal transmission of agricultural tractors. A field test was conducted using an 86 kW agricultural tractor for plow and rotary tillage, which are typical agricultural operations. The field test was completed after about 107 h due to transmission noise and operational problems. As a result of disassembling the transmission, it was found that the range shift A and B gears were damaged. In the case of the range shift A gear, it was judged that plastic deformation occurred due to low contact stress, and the bending stress was low, therefore gear tooth breakage occurred in the range shift B gear. In order to ensure the durability of the transmission, four materials of alloy steel for machine structural use, such as SCr420, SNCM220, SCM822, and SNC815, were selected, and the safety factor and service life according to the gear materials were compared using simulation software. As a result of simulation analysis, SCM822 satisfied the target life value and was selected as a material for change. The damaged range shift A and B gears were changed to SCM822, and an axle dynamometer test was performed for the verification of the modified transmission. After conducting the axle dynamometer test, the transmission was disassembled, and it was confirmed that the range shift A and B gears were in normal condition. Therefore, it was considered that the durability of the transmission was ensured by satisfying the target life requirements of the gears. In the future, the transmission simulation model for 86 kW class agricultural tractor is expected to be utilized for the development of tractor transmissions, cost reduction, and optimal design