[Advances in digital twins technology of human skeletal muscle].

The human skeletal muscle drives skeletal movement through contraction. Embedding its functional information into the human morphological framework and constructing a digital twin of skeletal muscle for simulating physical and physiological functions of skeletal muscle are of great significance for the study of "virtual physiological humans". Based on relevant literature both domestically and internationally, this paper firstly summarizes the technical framework for constructing skeletal muscle digital twins, and then provides a review from five aspects including skeletal muscle digital twins modeling technology, skeletal muscle data collection technology, simulation analysis technology, simulation platform and human medical image database. On this basis, it is pointed out that further research is needed in areas such as skeletal muscle model generalization, accuracy improvement, and model coupling. The methods and means of constructing skeletal muscle digital twins summarized in the paper are expected to provide reference for researchers in this field, and the development direction pointed out can serve as the next focus of research.

Dietary Nutritional Information Autonomous Perception Method Based on Machine Vision in Smart Homes.

In order to automatically perceive the user's dietary nutritional information in the smart home environment, this paper proposes a dietary nutritional information autonomous perception method based on machine vision in smart homes. Firstly, we proposed a food-recognition algorithm based on YOLOv5 to monitor the user's dietary intake using the social robot. Secondly, in order to obtain the nutritional composition of the user's dietary intake, we calibrated the weight of food ingredients and designed the method for the calculation of food nutritional composition; then, we proposed a dietary nutritional information autonomous perception method based on machine vision (DNPM) that supports the quantitative analysis of nutritional composition. Finally, the proposed algorithm was tested on the self-expanded dataset CFNet-34 based on the Chinese food dataset ChineseFoodNet. The test results show that the average recognition accuracy of the food-recognition algorithm based on YOLOv5 is 89.7%, showing good accuracy and robustness. According to the performance test results of the dietary nutritional information autonomous perception system in smart homes, the average nutritional composition perception accuracy of the system was 90.1%, the response time was less than 6 ms, and the speed was higher than 18 fps, showing excellent robustness and nutritional composition perception performance.

Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes.

Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social robot to detect embarrassing situations. Firstly, we designed an improved neural network structure based on the You Only Look Once (YOLO) model to obtain feature information. By focusing on reducing area redundancy and computation time, we proposed a bounding-box merging algorithm based on region proposal networks (B-RPN), to merge the areas that have similar features and determine the borders of the bounding box. Thereafter, we designed a feature extraction algorithm based on our improved YOLO and B-RPN, called F-YOLO, for our training datasets, and then proposed a real-time object detection algorithm based on F-YOLO (RODA-FY). We implemented RODA-FY and compared models on our MAT social robot. Secondly, we considered six types of situations in smart homes, and developed training and validation datasets, containing 2580 and 360 images, respectively. Meanwhile, we designed three types of experiments with four types of test datasets composed of 960 sample images. Thirdly, we analyzed how a different number of training iterations affects our prediction estimation, and then we explored the relationship between recognition accuracy and learning rates. Our results show that our proposed privacy detection system can recognize designed situations in the smart home with an acceptable recognition accuracy of 94.48%. Finally, we compared the results among RODA-FY, Inception V3, and YOLO, which indicate that our proposed RODA-FY outperforms the other comparison models in recognition accuracy.

Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification.

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification.

A regularized stochastic configuration network based on weighted mean of vectors for regression.

The stochastic configuration network (SCN) randomly configures the input weights and biases of hidden layers under a set of inequality constraints to guarantee its universal approximation property. The SCN has demonstrated great potential for fast and efficient data modeling. However, the prediction accuracy and convergence rate of SCN are frequently impacted by the parameter settings of the model. The weighted mean of vectors (INFO) is an innovative swarm intelligence optimization algorithm, with an optimization procedure consisting of three phases: updating rule, vector combining, and a local search. This article aimed at establishing a new regularized SCN based on the weighted mean of vectors (RSCN-INFO) to optimize its parameter selection and network structure. The regularization term that combines the ridge method with the residual error feedback was introduced into the objective function in order to dynamically adjust the training parameters. Meanwhile, INFO was employed to automatically explore an appropriate four-dimensional parameter vector for RSCN. The selected parameters may lead to a compact network architecture with a faster reduction of the network residual error. Simulation results over some benchmark datasets demonstrated that the proposed RSCN-INFO showed superior performance with respect to parameter setting, fast convergence, and network compactness compared with other contrast algorithms.

FPGAN: Face de-identification method with generative adversarial networks for social robots.

In this paper, we propose a new face de-identification method based on generative adversarial network (GAN) to protect visual facial privacy, which is an end-to-end method (herein, FPGAN). First, we propose FPGAN and mathematically prove its convergence. Then, a generator with an improved U-Net is used to enhance the quality of the generated image, and two discriminators with a seven-layer network architecture are designed to strengthen the feature extraction ability of FPGAN. Subsequently, we propose the pixel loss, content loss, adversarial loss functions and optimization strategy to guarantee the performance of FPGAN. In our experiments, we applied FPGAN to face de-identification in social robots and analyzed the related conditions that could affect the model. Moreover, we proposed a new face de-identification evaluation protocol to check the performance of the model. This protocol can be used for the evaluation of face de-identification and privacy protection. Finally, we tested our model and four other methods on the CelebA, MORPH, RaFD, and FBDe datasets. The results of the experiments show that FPGAN outperforms the baseline methods.