Coronavirus disease 19 (COVID-19) is a virus that spreads through contact with the respiratory droplets of infected persons, so quarantine is mandatory to break the infection chain. This paper proposes a wearable device with the Internet of Things (IoT) integration for real-time monitoring of body temperature the indoor condition via an alert system to the person in quarantine. The alert is transferred when the body thermal exceeds the allowed threshold temperature. Moreover, an algorithm Repetition Spikes Counter (RSC) based on an accelerometer is employed in the role of human activity recognition to realize whether the quarantined person is doing physical exercise or not, for auto-adjustment of threshold temperature. The real-time warning and stored data analysis support the family members/doctors in following and updating the quarantined people’s body temperature behavior in the tele-distance. The experiment includes an M5stickC wearable device, a Microelectromechanical system (MEMS) accelerometer, an infrared thermometer, and a digital temperature sensor equipped with the user’s wrist. The indoor temperature and humidity are measured to restrict the virus spread and supervise the room condition of the person in quarantine. The information is transferred to the cloud via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker. The Bluetooth is integrated as an option for the data transfer from the self-isolated person to the electronic device of a family member in the case of Wi-Fi failed connection. The tested result was obtained from a student in quarantine for 14 days. The designed system successfully monitored the body temperature, exercise activity, and indoor condition of the quarantined person that handy during the Covid-19 pandemic.

Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things (IoT) network. The system includes a Passive Pyroelectric Infrared Detector for human detection and an analog flame sensor to sense the appearance of the concerned objects and then transmit the signal to the workstation via Wi-Fi based on the microcontroller Espressif32 (Esp32). The computer vision models YOLOv8 (You Only Look Once version 8) and Cascade Classifier are trained and implemented into the workstation, which is able to identify people, some potentially dangerous objects, and fire. The drone is also controlled by three algorithms—distance maintenance, automatic yaw rotation, and potentially dangerous object avoidance—with the support of a proportional–integral–derivative (PID) controller. The Smart Drone Surveillance System has good commands for automatic tracking and streaming of the video of these specific circumstances and then transferring the data to the involved parties such as security or staff.

This research explores the integration of Artificial Intelligence (AI) in wearable devices, including smartwatches, fitness trackers, smart clothing, and smart eyewear. Machine Learning (ML) and Deep Learning (DL) play a crucial role in enhancing these devices, leveraging sophisticated algorithms within the Internet of Things (IoT) ecosystem. AI-powered wearables incorporate metrology and advanced computational techniques, with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) driving applications in activity recognition, health monitoring, and personalized recommendations. The work presents case studies, highlighting AI applications in smart devices, such as stress detection via Heart Rate Variability, personalized exercise guidance, muscular activity monitoring, and real-time image recognition. Real-world implementations will illustrate the practical deployment of AI in commercial wearable products. Additionally, the research will address privacy and data security challenges associated with AI-driven wearable technology, ensuring the safeguarding of user information.

Accelerometer of the Microelectromechanical systems (MEMS) based inertial measurement units (IMUs) is key to inclination measurement in the industry 4.0. However, external vibration negatively impacts the precision of orientation angles during operation. Many inclinometer companies have demanded to develop a solution for vibration impact on accelerometer without other sensors' support because of economic problems. This article presents a new algorithm Orientation Axes Crossover Processing (OACP) on vibration optimization for MEMS accelerometer without sensor fusion. The proposed filter works on a principle based on the characteristics of vibration impact on whether the X-axis or Y-axis to optimally minimize the noise. A high accurate setup is built-up based on the Pan-Tilt Unit and a TUMAC vibrator for the verification of new filters, implemented into LSM9DS1 (3D accelerometer, 3D gyroscope). The new filter is able to work independently, and also fuse with the Low-pass filter or Kalman filter to enhance the dynamic response, only 0.163 seconds as maximum delay during vibration. The experimental results show that the proposed algorithm always accomplishes smaller variations than Low-pass filter, about 0.2 degrees in standard deviation. The compromise between vibration immunity and dynamic response is analyzed in detail to demonstrate the optimal performances of the concerned filters. The project was carried out at the `Sensor System' in Italy which is an industrial company in the inclinometer field.

In the heavy industry, high-quality inclinometer has been required in terms of accuracy, stability, and safety function for durable use in harsh environments. This paper proposes a robust orientation system for industrial inclinometer based on Microelectromechanical Systems (MEMS) accelerometer. An industrial motor (Vexta) is used as an oriental reference. The STM microcontroller (MCU) has the role of memorizing the inclination sent from the motor with the corresponding acceleration into a Lookup Table (LUT) by using Flash memory. The full redundancy included dual accelerometers and MCUs to enhance the safety function for the operating system via two inclination channels. The support from the Low-pass filter and the zero-offset compensation for temperature change contribute to the measurement precision. For industrial applications, Control Area Network open (CANopen) is used as an embedded network communication system. Both static and dynamic cases were tested to verify the behavior of the inclination sensor. The designed structure provides high efficiency for inclinometer performance with accuracy ±0.2° and the stability is always guaranteed thanks to safety function. The designed inclinometer was used for a real excavator to verify its stability and operating performance. The project was carried out at the ’Sensor System’ in Italy, an industrial company in the inclinometer field.