###### Abstract  
In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a Convolutional Neural Network (CNN) model for event sensing. Our focus is on leveraging deep learning to detect security-critical gestures, converting millimeter-wave parameters into point cloud images, and enhancing recognition accuracy. CNNs present complexity challenges in deep learning. To address this, we developed flexible quantization methods, simplifying You Only Look Once (YOLO)-v4 operations with an 8-bit fixed-point number representation. Cross-simulation validation showed that CPU-based quantization improves speed by 300% with minimal accuracy loss, even doubling the YOLO-tiny model's speed in a GPU environment. We established a Raspberry Pi 4-based system, combining simplified deep learning with Message Queuing Telemetry Transport (MQT) Internet of Things (IoT) technology for nursing care. Our quantification method significantly boosted identification speed by nearly 2.9 times, enabling millimeter-wave sensing in embedded systems. Additionally, we implemented hardware-based quantization, directly quantifying data from images or weight files, leading to circuit synthesis and chip design. This work integrates AI with mmWave sensors in the domain of nursing security and hardware implementation to enhance recognition accuracy and computational efficiency. Employing millimeter-wave radar in medical institutions or homes offers a strong solution to privacy concerns compared to conventional cameras that capture and analyze the appearance of patients or residents.  
mmWave radar; integrated sensing and communication; convolutional neural network; artificial intelligence of things; gesture recognition +
Footnote †: journal:  
0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-00000-00000-00000-00000-000000-000000-is becoming a key feature of 6G Radio Access Networks (RANs) [9], allowing the use of dense small-area infrastructure to build sensing networks. Millimeter-wave radar transmits signals with wavelengths in the millimeter range, which is one of the advantages of the technology. By capturing the reflected signals, the radar system can determine the distance, velocity, and angle of an object to create a micro-Doppler effect, which can be processed to provide a unique data set, such as distance, velocity, and angle, as well as the ability to distinguish between different targets, so that the sensor can detect the characteristics of different objects within the detection range. For example, this data allows sensors to sense echo signals, and modulation effects from tiny motions, including characteristics typical of objects such as the rotational speed of a bicycle wheel, a person's swaying arm, or an animal's running limbs. In this paper, we will build a sensing system that can detect the object position and gesture recognition for nursing-secure care using an AI model, which can be used in embedded systems. We take the MQTT [10] IoT protocols to transmit our identification results to create an intelligent system on a smart embedded platform and run a test system to validate our research.  
The general architecture of this paper is as shown in Figure 1. Sensors can detect user gestures or postures, and after computation through our system, commands can be emitted to control designated IoT devices.