Safety of Special Waveform of Transcranial Electrical Stimulation (TES): In Vivo Assessment.

Intermittent theta burst (iTBS) powered by direct current stimulation (DCS) can safely be applied transcranially to induce neuroplasticity in the human and animal brain cortex. tDCS-iTBS is a special waveform that is used by very few studies, and its safety needs to be confirmed. Therefore, we aimed to evaluate the safety of tDCS-iTBS in an animal model after brain stimulations for 1 h and 4 weeks. Thirty-one Sprague Dawley rats were divided into two groups: (1) short-term stimulation for 1 h/session (sham, low, and high) and (2) long-term for 30 min, 3 sessions/week for 4 weeks (sham and high). The anodal stimulation applied over the primary motor cortex ranged from 2.5 to 4.5 mA/cm2. The brain biomarkers and scalp tissues were assessed using ELISA and histological analysis (H&E staining) after stimulations. The caspase-3 activity, cortical myelin basic protein (MBP) expression, and cortical interleukin (IL-6) levels increased slightly in both groups compared to sham. The serum MBP, cortical neuron-specific enolase (NSE), and serum IL-6 slightly changed from sham after stimulations. There was no obvious edema or cell necrosis seen in cortical histology after the intervention. The short- and long-term stimulations did not induce significant adverse effects on brain and scalp tissues upon assessing biomarkers and conducting histological analysis.

Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models.

Women often wear high-heeled shoes for professional or esthetic reasons. However, high-heeled shoes can cause discomfort and injury and can change the body's center of gravity when maintaining balance. This study developed an assessment system for predicting the maximal safe range for heel height by recording the plantar pressure of participants' feet by using force-sensing resistor (FSR) sensors and conducting analyses using regression models. Specifically, 100 young healthy women stood on an adjustable platform while physicians estimated the maximal safe height of high-heeled shoes. The collected FSR data combined with and without personal features were analyzed using regression models. The experimental results showed that the regression model based on the pressure data for the right foot had better predictive power than that based on data for the left foot, regardless of the module. The model with two heights had higher predictive power than that with a single height. Furthermore, adding personal features under the condition of two heights afforded the best predictive effect. These results can help wearers choose maximal safe high-heeled shoes to reduce injuries to the bones and lower limbs.

Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm.

Insufficient physical activity is common in modern society. By estimating the energy expenditure (EE) of different physical activities, people can develop suitable exercise plans to improve their lifestyle quality. However, several limitations still exist in the related works. Therefore, the aim of this study is to propose an accurate EE estimation model based on depth camera data with physical activity classification to solve the limitations in the previous research. To decide the best location and amount of cameras of the EE estimation, three depth cameras were set at three locations, namely the side, rear side, and rear views, to obtain the kinematic data and EE estimation. Support vector machine was used for physical activity classification. Three EE estimation models, namely linear regression, multilayer perceptron (MLP), and convolutional neural network (CNN) models, were compared and determined the model with optimal performance in different experimental settings. The results have shown that if only one depth camera is available, optimal EE estimation can be obtained using the side view and MLP model. The mean absolute error (MAE), mean square error (MSE), and root MSE (RMSE) of the classification results under the aforementioned settings were 0.55, 0.66, and 0.81, respectively. If higher accuracy is required, two depth cameras can be set at the side and rear views, the CNN model can be used for light-to-moderate activities, and the MLP model can be used for vigorous activities. The RMSEs for estimating the EEs of standing, walking, and running were 0.19, 0.57, and 0.96, respectively. By applying the different models on different amounts of cameras, the optimal performance can be obtained, and this is also the first study to discuss the issue.

An Assessment System for Post-Stroke Manual Dexterity Using Principal Component Analysis and Logistic Regression.

Hand function assessment is crucial for patients with stroke, who must perform regular repetitive tasks during rehabilitation. However, the conventional evaluation method is subjective and not uniform among physicians. A novel method is proposed in this paper to analyze raw data from a data glove equipped with 16 six-axis inertial measurement units. The proposed method can provide accurate assistance to physicians and objectively assess patients' hand function. Three tasks (the thumb task, the grip task, and the card-turning task) were conducted to evaluate participants' hand function. Representative parameters of hand function in each task and overall evaluation were extracted through principal component analysis and used to develop logistic regression models. The results revealed that all three tasks can be used to perfectly predict healthy subjects and subjects with stroke, with the thumb task exhibiting the highest predictive accuracy for the severity of hand dysfunction. Overall, the proposed method can serve as an efficient method for physicians to assess the hand function of patients with stroke.

Depth-Camera-Based System for Estimating Energy Expenditure of Physical Activities in Gyms.

Energy expenditure (EE) monitoring is crucial to tracking physical activity (PA). Accurate EE monitoring may help people engage in adequate activity and therefore avoid obesity and reduce the risk of chronic diseases. This study proposes a depth-camera-based system for EE estimation of PA in gyms. Most previous studies have used inertial measurement units for EE estimation. By contrast, the proposed system can be used to conveniently monitor subjects' treadmill workouts in gyms without requiring them to wear any devices. A total of 21 subjects were recruited for the experiment. Subjects' skeletal data acquired using the depth camera and oxygen consumption data simultaneously obtained using the K4b2 device were used to establish an EE predictive model. To obtain a robust EE estimation model, depth cameras were placed in the side view, rear side view, and rear view. A comparison of five different predictive models and these three camera locations showed that the multilayer perceptron model was the best predictive model and that placing the camera in the rear view provided the best EE estimation performance. The measured and predicted metabolic equivalents of task exhibited a strong positive correlation, with r = 0.94 and coefficient of determination r2 = 0.89. Furthermore, the mean absolute error was 0.61 MET, mean squared error was 0.67 MET, and root mean squared error was 0.76 MET. These results indicate that the proposed system is handy and reliable for monitoring user's EE when performing treadmill workouts.