Hand dysfunctions in Parkinson's disease include rigidity, muscle weakness, and tremor, which can severely affect the patient's daily life. Herein, a multimodal sensor glove is developed for quantifying the severity of Parkinson's disease symptoms in patients’ hands while assessing the hands’ multifunctionality. Toward signal processing, various algorithms are used to quantify and analyze each signal: Exponentially Weighted Average algorithm and Kalman filter are used to filter out noise, normalization to process bending signals, K‐Means Cluster Analysis to classify muscle strength grades, and Back Propagation Neural Network to identify and classify tremor signals with an accuracy of 95.83%. Given the compelling features, the flexibility, muscle strength, and stability assessed by the glove and the clinical observations are proved to be highly consistent with Kappa values of 0.833, 0.867, and 0.937, respectively. The intraclass correlation coefficients obtained by reliability evaluation experiments for the three assessments are greater than 0.9, indicating that the system is reliable. The glove can be applied to assist in formulating targeted rehabilitation treatments and improve hand recovery efficiency.

Edge detection is an important part of image segmentation, in this paper, the edge detection algorithm based on traditional Canny operator for medical images is studied. The Canny operator is improved using Otsu algorithm and double-gate limit detection method, and the ability of Canny operator edge detection is strengthened. The simulation of the algorithm is realized on the computer platform by MATLAB, and the experimental results are analyzed from two image objective evaluation indexes of information entropy and mean square error. The experimental results show that compared with the traditional Canny algorithm, the improved adaptive double threshold Canny algorithm has better edge detection effect, richer image details, better noise suppression and less false edges.

With a focus on disease prevention and health promotion, a reactive and disease-centric healthcare system is revolutionized to a point-of-care model by the application of wearable devices. The convenience and low cost made it possible for long-term monitoring of health problems in long-distance traveling such as flights. While most of the existing health monitoring systems on aircrafts are limited for pilots, point-of-care systems provide choices for passengers to enjoy healthcare at the same level. Here in this paper, an airline point-of-care system containing hybrid electrocardiogram (ECG), breathing, and motion signals detection is proposed. At the same time, we propose the diagnosis of sleep apnea-hypopnea syndrome (SAHS) on flights as an application of this system to satisfy the inevitable demands for sleeping on long-haul flights. The hardware design includes ECG electrodes, flexible piezoelectric belts, and a control box, which enables the system to detect the original data of ECG, breathing, and motion signals. By processing these data with interval extraction-based feature selection method, the signals would be characterized and then provided for the long short-term memory recurrent neural network (LSTM-RNN) to classify the SAHS. Compared with other machine learning methods, our model shows high accuracy up to 84–85% with the lowest overfit problem, which proves its potential application in other related fields.

Traditional hand rehabilitation devices present a challenge in providing personalized training that can lead to finger movements exceeding the safe range, resulting in secondary injuries. To address this issue, we introduce a soft rehabilitation training glove with the function of safety and personalization, which can allow patients to select training modes based on rehabilitation and provide real-time monitoring, as well as feedback on finger movement data. The inner glove is equipped with bending sensors to access the maximum/minimum angle of finger movement and to provide data for the safety of rehabilitation training. The outer glove contains flexible drivers, which can drive fingers for different modes of rehabilitation training. As a result, the rehabilitation glove can drive five fingers to achieve maximum extension/flexion angles of 15.65°/85.97°, 15.34°/89.53°, 16.78°/94.27°, 15.59°/88.82°, and 16.73°/88.65°, from thumb to little finger, respectively, and the rehabilitation training frequency can reach six times per minute. The safety evaluation result indicated an error within ±6.5° of the target-motion threshold. The reliability assessment yielded a high-intra-class correlation coefficient value (0.7763–0.9996). Hence, the rehabilitation glove can achieve targeted improvement in hand function while ensuring safety.

The 3D quantitative analysis of facial morphology is of importance in plastic surgery (PS), which could help surgeons design appropriate procedures before conducting the surgery. We propose a system to simulate and guide the shaping effect analysis, which could produce a similar but more harmonious face simulation. To this end, first, the depth camera based on structured light coding is employed for facial 3D data acquisition, from which the point cloud data of multiple facial perspectives could be obtained. Next, the cascade regression tree algorithm is used to extract the esthetic key points of the face model and to calculate the facial features composed of the key points, such as the nose, chin, and eyes. Quantitative facial esthetic indexes are offered to doctors to simulate PS. Afterward, we exploit a face mesh metamorphosis based on finite elements. We design several morphing operators, including augmentation, cutting, and lacerating. Finally, the regional deformation is detected, and the operation effect is quantitatively evaluated by registering the 3D scanning model before and after the operation. The test of our proposed system and the simulation of PS operations find that the measurement error of facial geometric features is 0.458 mm, and the area is 0.65 mm². The ratings of the simulation outcomes provided by panels of PS prove that the system is effective. The manipulated 3D faces are deemed more beautiful compared to the original faces respecting the beauty canons such as facial symmetry and the golden ratio. The proposed algorithm could generate realistic visual effects of PS simulation. It could thus assist the preoperative planning of facial PS.

Sleep Apnea Syndrome (SAS) is a serious threat to the life and health of patients. In this paper, a breathing coil is used as a flexible sensor, and a breathing signal conditioning circuit is designed to collect breathing signals. At the same time, an algorithm for determining the incidence of apnea is designed. First, all the maximum points in the respiratory signal and the corresponding extreme positions are found; then the cubic spline interpolation function is used to fit the upper and lower values of the maximum and the minimum envelope, and find the average value of the upper and lower envelopes; finally, the peak or trough point by searching for the local minimum or local maximum in the average envelope was found, and then further segmented calculations was performed. The test results show that the misjudgment range between the number of sleep apneas recorded by the simulation experiment and the number of sleep apneas obtained by the algorithm is between -2 and 3 times. The designed system terminal equipment is accurate, comfortable to wear, can be monitored for a long time. And is suitable for family daily sleep monitoring.