Enhancing accuracy and convenience of golf swing tracking with a wrist-worn single inertial sensor.

In this study, we address two technical challenges to enhance golf swing trajectory accuracy using a wrist-worn inertial sensor: orientation estimation and drift error mitigation. We extrapolated consistent sensor orientation from specific address-phase signal segments and trained the estimation with a convolutional neural network. We then mitigated drift error by applying a constraint on wrist speed at the address, backswing top, and finish, and ensuring that the wrist's finish displacement aligns with a virtual circle on the 3D swing plane. To verify the proposed methods, we gathered data from twenty male right-handed golfers, including professionals and amateurs, using a driver and a 7-iron. The orientation estimation error was about 60% of the baseline, comparable to studies requiring additional sensor information or calibration poses. The drift error was halved and the single-inertial-sensor tracking performance across all swing phases was about 17 cm, on par with multimodal approaches. This study introduces a novel signal processing method for tracking rapid, wide-ranging motions, such as a golf swing, while maintaining user convenience. Our results could impact the burgeoning field of daily motion monitoring for health care, especially with the increasing prevalence of wearable devices like smartwatches.

Intravascular Papillary Endothelial Hyperplasia in Association with Eccrine Angiokeratomatous Hamartoma.

Eccrine angiokeratomatous hamartoma is a variant of eccrine angiomatous hamartoma. Histopathologically, it shows both features of eccrine angiomatous hamartoma with components of angiokeratoma. Eccrine angiokeratomatous hamartoma is extremely rare. Eccrine angiokeratomatous hamartoma in our case co-existed with intravascular papillary endothelial hyperplasia. This is the first reported case.

Preoperative mean platelet volume is a prognostic biomarker for survival in patients with gastric cancer: A cohort study.

This study aimed to evaluate the prognostic potential of mean platelet volume (MPV) in gastric cancer (GC) patients. Patients with stage I-III GC who underwent gastrectomy were enrolled in this study. Cox regression analysis was performed to evaluate the determinants of overall survival (OS) and disease-free survival (DFS). The discriminative capacity of the model was determined using the Harrell concordance index (C-index). The net benefit of the model was validated using decision curve analysis (DCA). Data from 401 patients were analyzed. Multivariate Cox regression analysis revealed that age, stage, serum albumin level (ALB), perineural invasion (PNI) and MPV were determinants of both OS and DFS. The MPV model consisted of 5 covariates (age, stage, ALB, PNI, and MPV level), and the baseline model constituted the same covariates as the MPV model, except for the MPV level. C-indices for OS and DFS were higher in the MPV model than in the baseline model. When the models were validated using DCA, the MPV model showed a greater net benefit than the baseline model for nearly all the threshold probabilities. Age, stage, ALB, PNI, and MPV are prognostic factors for OS and DFS. The discriminative capacities for OS and DFS in the MPV model were higher than those in the baseline model, thus implying the clinical significance of the MPV level as a determinant of survival in GC.

Cutaneous Mycobacterium massiliense Infection Caused by Skin Coining 'Gua Sha' in Korean Healthy Female.

Nontuberculous mycobacteria are ubiquitous environmental organisms that are rare pathogens in immunocompetent individuals. However, cutaneous nontuberculous mycobacteria infections have been increasingly associated with invasive procedures, including surgery, liposuction, filler injection, intramuscular injection, mesotherapy, piercing, acupuncture, and cupping therapy. Herein, we report the first case of cutaneous nontuberculous mycobacteria infection caused by the East-Asian traditional treatment 'Gua Sha', also known as scraping, coining or spooning in English. A 35-year-old healthy female presented with widespread, painful skin nodules and pustules on her upper and lower extremities that had developed after Gua Sha treatment for body contouring. Histopathologic examination of the lesions revealed granulomatous inflammation in the dermis and the culture isolates were identified as Mycobacterium massiliense with molecular identification. The patient was successfully treated with intermittent incision and drainage of persistent nodules and oral clarithromycin based on antimicrobial susceptibility testing. We recommend implementation of a standard safety protocol for Gua Sha practitioners to minimize the risk of infection transmission.

Effects of Short-term Mobile Application Use on Weight Reduction for Patients with Type 2 Diabetes.

BACKGROUND: The increasing prevalence of type 2 diabetes mellitus (T2DM) has led to a significant health burden. Technological advancements have highlighted the benefits of digital therapeutics for chronic diseases. In this study, we aimed to investigate the effects of a mobile application on weight reduction in patients with T2DM. METHODS: A total of 48 patients with T2DM was included in this single-center, randomized, controlled trial. In addition to conventional treatment, participants in the intervention group used a mobile application-based self-management system for diet, exercise, and medication adherence. The primary outcome of this study was weight change after 3 months of intervention, and secondary outcomes were metabolic parameters. RESULTS: After 12 weeks, no significant differences in body weight change were observed between the intervention and control groups (P=0.229). However, a significant difference was found in waist circumference (WC) between the two groups, wherein the control group showed an increase in WC (from 95.00±8.89 cm to 95.76±9.72 cm), while the intervention group showed a reduction (from 91.93±6.25 cm to 90.75±6.01 cm) with a significant time by group interaction (P=0.016). Additionally, participants with good compliance exhibited a more evident reduction in WC (P=0.037). However, no significant differences were found in other metabolic parameters between the two groups. CONCLUSION: Lifestyle modification using short-term mobile applications effectively reduced WC, especially in patients with good adherence to the application. However, weight reduction was not achieved.

Estimation of Three-Dimensional Lower Limb Kinetics Data during Walking Using Machine Learning from a Single IMU Attached to the Sacrum.

Kinetics data such as ground reaction forces (GRFs) are commonly used as indicators for rehabilitation and sports performance; however, they are difficult to measure with convenient wearable devices. Therefore, researchers have attempted to estimate accurately unmeasured kinetics data with artificial neural networks (ANNs). Because the inputs to an ANN affect its performance, they must be carefully selected. The GRF and center of pressure (CoP) have a mechanical relationship with the center of mass (CoM) in the three dimensions (3D). This biomechanical characteristic can be used to establish an appropriate input and structure of an ANN. In this study, an ANN for estimating gait kinetics with a single inertial measurement unit (IMU) was designed; the kinematics of the IMU placed on the sacrum as a proxy for the CoM kinematics were applied based on the 3D spring mechanics. The walking data from 17 participants walking at various speeds were used to train and validate the ANN. The estimated 3D GRF, CoP trajectory, and joint torques of the lower limbs were reasonably accurate, with normalized root-mean-square errors (NRMSEs) of 6.7% to 15.6%, 8.2% to 20.0%, and 11.4% to 24.1%, respectively. This result implies that the biomechanical characteristics can be used to estimate the complete three-dimensional gait data with an ANN model and a single IMU.

Estimation of the ground reaction forces from a single video camera based on the spring-like center of mass dynamics of human walking.

In clinical studies, the ground reaction forces (GRFs) during walking have found being highly useful. Therefore, the force sensing shoes with small sensors and estimation methods based on kinematics from motion capture systems or inertial measurement units were proposed. Recent studies demonstrated methods of extracting GRFs from whole-body joint kinematics, which requires a significant computational load. In this study, we propose a vertical and anterior-posterior GRFs estimation method using a single camera based on the dynamic relationship between the center of mass (CoM) and the GRFs in terms of spring mechanics. The estimation method consisted of two steps: the extraction of the vertical CoM from the video clip and the conversion of the CoM information into GRFs using a walking model. From the image of the greater trochanter that is positioned near the pelvic joint, the vertical CoM was extracted. This was done after removing the artifacts by pelvic rotation and postural change of lower limbs. The parameters of a compliant bipedal walking model were tuned to best match the CoM trajectory coupled with GRFs by spring mechanics. A video camera was used to record the walking trials of five healthy young participants from the side. The walking trials was conducted at three different speeds on the instrumented treadmill; each lasted one minute long. The GRF prediction errors were approximately 9-11%, with the best matching trials found to be at a self-selected gait speed. The prediction of anterior-posterior GRF components showed a more consistent match than the vertical GRF. The results demonstrated the possibility of marker-less kinetics prediction from video images incorporating the mechanical characteristics of the CoM.

Spring-loaded inverted pendulum modeling improves neural network estimation of ground reaction forces.

Inertial-measurement-unit (IMU)-based wearable gait-monitoring systems provide kinematic information but kinetic information, such as ground reaction force (GRF) are often needed to assess gait symmetry and joint loading. Recent studies have reported methods for predicting GRFs from IMU measurement data by using artificial neural networks (ANNs). To obtain reliable predictions, the ANN requires a large number of measurement inputs at the cost of wearable convenience. Recognizing that the dynamic relationship between the center of mass (CoM) and GRF can be well represented by using spring mechanics, in this study we propose two GRF prediction methods based on the implementation of walking dynamics in a neural network. Method 1 takes inputs to the network that were CoM kinematics data and Method 2 employs forces approximated from CoM kinematics by applying spring mechanics. The gait data of seven young healthy subjects were collected at various walking speeds. Leave-one-subject-out cross-validation was performed with normalized root mean square error and r as quantitative measures of prediction performance. The vertical and anteroposterior (AP) GRFs obtained using both methods agreed well with the experimental data, but Method 2 yielded improved predictions of AP GRF compared to Method 1 (p = 0.005). These results imply that knowledge of the dynamic characteristics of walking, combined with a neural network, could enhance the efficiency and accuracy of GRF prediction and help resolve the tradeoff between information richness and wearable convenience of wearable technologies.

Golf Swing Segmentation from a Single IMU Using Machine Learning.

Golf swing segmentation with inertial measurement units (IMUs) is an essential process for swing analysis using wearables. However, no attempt has been made to apply machine learning models to estimate and divide golf swing phases. In this study, we proposed and verified two methods using machine learning models to segment the full golf swing into five major phases, including before and after the swing, from every single IMU attached to a body part. Proposed bidirectional long short-term memory-based and convolutional neural network-based methods rely on characteristics that automatically learn time-series features, including sequential body motion during a golf swing. Nine professional and eleven skilled male golfers participated in the experiment to collect swing data for training and verifying the methods. We verified the proposed methods using leave-one-out cross-validation. The results revealed average segmentation errors of 5-92 ms from each IMU attached to the head, wrist, and waist, accurate compared to the heuristic method in this study. In addition, both proposed methods could segment all the swing phases using only the acceleration data, bringing advantage in terms of power consumption. This implies that swing-segmentation methods using machine learning could be applied to various motion-analysis environments by dividing motion phases with less restriction on IMU placement.

Three Dimensional Upper Limb Joint Kinetics of a Golf Swing with Measured Internal Grip Force.

The biomechanics of a golf swing have been of interest to golfers, instructors, and biomechanists. In addition to the complexity of the three-dimensional (3D) dynamics of multi-segments of body, the closed-chain body posture as a result of both hands holding a club together makes it difficult to fully analyze the 3D kinetics of a golf swing. To identify the hand-grip joint force and torque applied by each hand, we directly measured the 3D internal grip force of nine registered professional golfers using an instrumented grip. A six-axis force-torque sensor was connected to a custom-made axially separated grip, which was then connected to a driver shaft using a manufactured screw thread. Subjects participated in two sessions of data collection featuring five driver swings with both a regular and customized sensor-embedded grip, respectively. Internal grip force measurement and upper limb kinematics were used to calculate the joint force and torque of the nine-linkage closed-chain of the upper limb and club using 3D inverse dynamics. Direct measurement of internal grip forces revealed a threefold greater right-hand torque application compared to the left hand, and counterforce by both hands was also found. The joint force and torque of the left arm tended to precede that of the right arm, the majority of which had peaks around the impact and showed a larger magnitude than that of the left arm. Due to the practical challenge of measuring internal force, heuristic estimation methods based on club kinematics showed fair approximation. Our results suggest that measuring the internal forces of the closed-chain posture could identify redundant joint kinetics and further propose a heuristic approximation.

Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning.

Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.

A bipedal compliant walking model generates periodic gait cycles with realistic swing dynamics.

A simple spring mechanics model can capture the dynamics of the center of mass (CoM) during human walking, which is coordinated by multiple joints. This simple spring model, however, only describes the CoM during the stance phase, and the mechanics involved in the bipedality of the human gait are limited. In this study, a bipedal spring walking model was proposed to demonstrate the dynamics of bipedal walking, including swing dynamics followed by the step-to-step transition. The model consists of two springs with different stiffnesses and rest lengths representing the stance leg and swing leg. One end of each spring has a foot mass, and the other end is attached to the body mass. To induce a forward swing that matches the gait phase, a torsional hip joint spring was introduced at each leg. To reflect the active knee flexion for foot clearance, the rest length of the swing leg was set shorter than that of the stance leg, generating a discrete elastic restoring force. The number of model parameters was reduced by introducing dependencies among stiffness parameters. The proposed model generates periodic gaits with dynamics-driven step-to-step transitions and realistic swing dynamics. While preserving the mimicry of the CoM and ground reaction force (GRF) data at various gait speeds, the proposed model emulated the kinematics of the swing leg. This result implies that the dynamics of human walking generated by the actuations of multiple body segments is describable by a simple spring mechanics.

Tinea capitis in adults: A 18-year retrospective, single-centre study in Korea.

BACKGROUND: Tinea capitis (TC) is a dermatophyte infection involving hair and scalp and occurs primarily in prepubertal children. However, data on adults are limited. OBJECTIVES: The aim of this study was to evaluate epidemiological, clinical and mycological characteristics of TC in adults in Korea. PATIENTS/METHODS: We retrospectively evaluated 82 adults (44.3%) among 185 TC patients at a tertiary hospital during June 2000-2017. RESULTS: Mean patient age was 66.9 ± 15.8 (20-90) years with female predominance; mean disease duration until mycological diagnosis, 22.5 (1-144) weeks; and misdiagnosis rate, 65.9%. Most common presumptive initial diagnoses were seborrhoeic dermatitis (24.4%) and bacterial folliculitis (18.3%). Chronic systemic illness and accompanying alopecia were found in 61 (74.4%) and 46 (56.1%) patients, respectively. Pustular type was found in 26.8% patients, followed by seborrhoeic dermatitis-like 25.6%, grey patch 23.2%, kerion celsi 22.0% and black dot 2.4%. Forty-eight patients (58.5%) had tinea infection at other skin areas. Microsporum canis (56.5%) and Trichophyton rubrum (21.7%) were the most common causative organisms; 92.7% patients achieved complete resolution, and seven patients (9.2%) had a recurrence. CONCLUSIONS: We report the largest, most recent series of case studies of adult TC. Adult TC is not an uncommon problem, especially in elderly women, and has distinctive epidemiological and clinicomycological characteristics compared to those in prepubertal children. Recognising adult TC profile will help clinicians avoid misdiagnosis and provide appropriate treatment.

Estimation of unmeasured ground reaction force data based on the oscillatory characteristics of the center of mass during human walking.

To enhance the wearability of portable motion-monitoring devices, the size and number of sensors are minimized, but at the expense of quality and quantity of data collected. For example, owing to the size and weight of low-frequency force transducers, most currently available wearable gait measurement systems provide only limited, if any, elements of ground reaction force (GRF) data. To obtain the most GRF information possible with a minimal use of sensors, we propose a GRF estimation method based on biomechanical knowledge of human walking. This includes the dynamics of the center of mass (CoM) during steady human gait resembling the oscillatory behaviors of a mass-spring system. Available measurement data were incorporated into a spring-loaded inverted pendulum with translating pivot. The spring stiffness and simulation parameters were tuned to match, as accurately as possible, the available data and oscillatory characteristics of walking. Our results showed that the model simulation estimated reasonably well the unmeasured GRF. Using the vertical GRF and CoP profile for gait speeds ranging from 0.93 to 1.89 m/s, the anterior-posterior (A-P) GRF was estimated and resulted in an average correlation coefficient of R = 0.982 ±â€¯0.009. Even when the ground contact timing and gait speed information were alone available, our method estimated GRFs resulting in R = 0.969 ±â€¯0.022 for the A-P and R = 0.891 ±â€¯0.101 for the vertical GRFs. This research demonstrates that the biomechanical knowledge of human walking, such as inherited oscillatory characteristics of the CoM, can be used to gain unmeasured information regarding human gait dynamics.

Kinematics of lower limbs during walking are emulated by springy walking model with a compliantly connected, off-centered curvy foot.

The dynamics of the center of mass (CoM) during walking and running at various gait conditions are well described by the mechanics of a simple passive spring loaded inverted pendulum (SLIP). Due to its simplicity, however, the current form of the SLIP model is limited at providing any further information about multi-segmental lower limbs that generate oscillatory CoM behaviors and their corresponding ground reaction forces. Considering that the dynamics of the CoM are simply achieved by mass-spring mechanics, we wondered whether any of the multi-joint motions could be demonstrated by simple mechanics. In this study, we expand a SLIP model of human locomotion with an off-centered curvy foot connected to the leg by a springy segment that emulates the asymmetric kinematics and kinetics of the ankle joint. The passive dynamics of the proposed expansion of the SLIP model demonstrated the empirical data of ground reaction forces, center of mass trajectories, ankle joint kinematics and corresponding ankle joint torque at various gait speeds. From the mechanically simulated trajectories of the ankle joint and CoM, the motion of lower-limb segments, such as thigh and shank angles, could be estimated from inverse kinematics. The estimation of lower limb kinematics showed a qualitative match with empirical data of walking at various speeds. The representability of passive compliant mechanics for the kinetics of the CoM and ankle joint and lower limb joint kinematics implies that the coordination of multi-joint lower limbs during gait can be understood with a mechanical framework.