It is an objective reality that deaf-mute people have difficulty seeking medical treatment. Due to the lack of sign language interpreters, most hospitals in China currently do not have the ability to interpret sign language. Normal medical treatment is a luxury for deaf people. In this paper, we propose a sign language recognition system: Heart-Speaker. Heart-Speaker is applied to a deaf-mute consultation scenario. The system provides a low-cost solution for the difficult problem of treating deaf-mute patients. The doctor only needs to point the Heart-Speaker at the deaf patient and the system automatically captures the sign language movements and translates the sign language semantics. When a doctor issues a diagnosis or asks a patient a question, the system displays the corresponding sign language video and subtitles to meet the needs of two-way communication between doctors and patients. The system uses the MobileNet-YOLOv3 model to recognize sign language. It meets the needs of running on embedded terminals and provides favorable recognition accuracy. We performed experiments to verify the accuracy of the measurements. The experimental results show that the accuracy rate of Heart-Speaker in recognizing sign language can reach 90.77%.
In the past few years, people’s attitudes toward early childhood education (PAUD) have undergone a complete transformation. Personalized and intelligent communication methods are highly praised, which also promotes the further focus on timely and effective human–computer interaction. Since traditional English learning that relies on parents consumes more time and energy and is prone to errors and omissions, this paper proposes a system based on a convolution neural network (CNN) and automatic speech recognition (ASR) to achieve an integrated process of object recognition, intelligent speech interaction, and synchronization of learning records in children’s education. Compared with platforms described in the literature, not only does it shoot objects in the real-life environment to obtain English words, their pronunciation, and example sentences corresponding to them, but also it combines the technique of a three-dimensional display to help children learn abstract words. At the same time, the cloud database summarizes and tracks the learning progress by a horizontal comparison, which makes it convenient for parents to figure out the situation. The performance evaluation of image and speech recognition demonstrates that the overall accuracy remains above 96%. Through comprehensive experiments in different scenarios, we prove that the platform is suitable for children as an auxiliary method and cultivates their interest in learning English.
The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single camera to detect multiple products in real-time performance without any labels, and the system realizes the integration of weighing, identification, and online settlement in the process of non-barcode items. The system includes a self-service vending device and a multi-device data management platform. The flexible configuration of the structure gives the system the possibility of identifying fruits from multiple angles. The height of the system can be adjusted to provide self-service for people of different heights; then, deep learning skill is applied implementing product detection, and real-time multi-object detection technology is utilized in the image-based checkout system. In addition, on the multi-device data management platform, the information docking between embedded devices, WeChat applets, Alipay, and the database platform can be implemented. We conducted experiments to verify the accuracy of the measurement. The experimental results demonstrate that the correlation coefficient R2 between the measured value of the weight and the actual value is 0.99, and the accuracy of non-barcode item prediction is 93.73%. In Yangpu District, Shanghai, a comprehensive application scenario experiment was also conducted, proving that our system can effectively deal with the challenges of various sales situations.
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