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%.

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 $R^2$ 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.

Traditional torque measurement has greatly depended on the contact between sensors and the rotating shaft. However, the high-speed rotation of the shaft leads to significant sensor wear, compromising the stability of the measurement system. Additionally, the newly collected test data tend to be high in latency and noise, which introduces accuracy challenges for artificial intelligence algorithms in torque measurement. This paper presents the measurement method, hardware platform design, data calibration and software optimization of a torque and rotational speed measurement system, which uses a non-invasive method and accurately estimate torque and rotational speed under both low and high-speed operating conditions by analyzing the mechanical phase difference between two pulse signals. The pulse signals are precisely captured by Hall sensors and an FPGA microcontroller circuit, while signal delay and noise are optimized through a CNN-LSTM artificial intelligence algorithm and system calibration programming. To verify the correctness of the proposed method, a test platform equipped with Hall sensors and servo motor and 200N·m standard torsion device for measuring the torque and rotational speed under torque force generation device's control was implemented. The effectiveness of the proposed method is demonstrated by achieving a measurement error rate within 1%, with the rotational speed error reduced from 3.15% to 0.86% and the torque error decreased to 0.96%.