Jerry Chen

Bio: Jerry Chen is an academic researcher from Yuan Ze University. The author has contributed to research in topics: HVAC & Air conditioning. The author has an hindex of 2, co-authored 4 publications receiving 18 citations.

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

Abstract: Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life Yet, its mechanism and the way to immune to it is still a question to be answered This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors Various physiological signals (ie, heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue

Integrations between Autonomous Systems and Modern Computing Techniques: A Mini Review.

Abstract: The emulation of human behavior for autonomous problem solving has been an interdisciplinary field of research. Generally, classical control systems are used for static environments, where external disturbances and changes in internal parameters can be fully modulated before or neglected during operation. However, classical control systems are inadequate at addressing environmental uncertainty. By contrast, autonomous systems, which were first studied in the field of control systems, can be applied in an unknown environment. This paper summarizes the state of the art autonomous systems by first discussing the definition, modeling, and system structure of autonomous systems and then providing a perspective on how autonomous systems can be integrated with advanced resources (e.g., the Internet of Things, big data, Over-the-Air, and federated learning). Finally, what comes after reaching full autonomy is briefly discussed.

Industry 5.0: Intelligent Sensor Based Autonomous Control System for HVAC Systems in Chemical Fiber Factory

Abstract: This work presents a human-machine integration approach, which is an autonomous control system that integrates the traditional expert-oriented strategy and intelligent sensor based data-driven strategy. This system offers effective solutions that are able to further improve the energy efficiency and decrease the energy consumption of heating, ventilation and air-conditioning (HVAC) system. In previous study, it concludes that energy efficient HVAC systems could be obtained by making strategic use and well-structured combination of the existing air conditioning technologies. However, HVAC also have intricate and complex structures that consist of air handler, terminal unit, duct system, compressor, thermostat, etc. Traditionally, the well-tuned proportional-integral-derivative (PID) controller could have well performance around normal working points but its tolerance to variations of process parameter would be seriously affected when the uncertainty is introduced to the environment due to short/long term weather changes from outdoors or events/activities happens indoors. The autonomous system is a novel approach that aims to include all three characteristics of Industry 5.0 (i.e., sustainability, resilience and human-centricity). The system integrated commercialize wind sensor as data collection set which are being widely deploy throughout the HVAC environment. By accessing the detail wind flow data in the HVAC environment, a control model could be used to optimize the air handling unit (AHU) output according to the real-time environment, which enhance the performance on energy conservation.

The Application of a Self-Made Integrated Three-in-One Microsensor and Commercially Available Wind Speed Sensor to the Cold Air Pipe of the Heating, Ventilation, and Air Conditioning in a Factory for Real-Time Wireless Measurement

Abstract: In this study, the integrated three-in-one (temperature, humidity, and wind speed) microsensor was made through the technology of the Micro-electro-mechanical Systems (MEMS) to measure three important physical quantities of the internal environment of the cold air pipe of the Heating, Ventilation and Air Conditioning (HVAC) in the factory, plan the installation positions of the integrated three-in-one microsensor and commercially available wind speed sensor required by the internal environment of the cold air pipe, and conduct the actual 310-h long term test and comparison. In the experiment, it was also observed that the self-made micro wind speed sensor had higher stability compared to the commercially available wind speed sensor (FS7.0.1L.195). The self-made micro wind speed sensor has a variation range of ±200 mm/s, while the commercially available wind speed sensor a variation range of ±1000 mm/s. The commercially available wind speed sensor (FS7.0.1L.195) can only measure the wind speed; however, the self-made integrated three-in-one microsensor can conduct real-time measurements of temperature and humidity according to the environment at that time, and use different calibration curves to know the wind speed. As a result, it is more accurate and less costly than commercially available wind speed sensors. The material cost of self-made integrated three-in-one microsensor includes chemicals, equipment usage fees, and wires. In the future, factories may install a large number of self-made integrated three-in-one microsensors in place of commercially available wind speed sensors. Through real-time wireless measurements, the self-made integrated three-in-one microsensors can achieve the control optimization of the HVAC cold air pipe’s internal environment to improve the quality of manufactured materials.

Sensors for daily life: A review

Abstract: Sensor technologies have improved the everyday life of human beings through their applications in almost all fields. Sensors are devices that detect changes in the source/environment and collect signals, and accordingly, the reaction is designed. There is a range of sources, including light, temperature, movements, and pressure etc., which may be used. A wide range of applications is utilised using innovative sensor technologies in lifestyle, healthcare, fitness, manufacturing, and daily life. In the medical field, the difficulty to take medicine is eased by drug donors fitted with sensors. It reminds them to take medicine via a signal and also supply the necessary medicine at the specified moment. In health care, older individuals, athletes, and risk patients benefit from modern sensor technology. The current industrial trends driving innovation include ultrasound, radar, and non-contact optoelectronic solutions and laser technology. The paper gives a brief overview of the numerous types of sensors that are utilised in everyday life. Various capabilities of sensors for day-to-day healthcare are discussed. Various features, associated nomenclature, and measures for sensors in day-to-day routine life are discussed diagrammatically and finally, the paper identifies and discusses twenty-two significant applications of sensors for daily life. Sensors also produce vital information and exchange data with other connected devices and administration systems when linked to a network. Thus, for the effective running of many companies, sensors are critical. Various types of sensors are used in our daily life, which is more accurate and makes quicker analysis.

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

Abstract: Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life Yet, its mechanism and the way to immune to it is still a question to be answered This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors Various physiological signals (ie, heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue

Smart devices and wearable technologies to detect and monitor mental health conditions and stress: A systematic review

Abstract: Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

Wearable Sensors for Healthcare: Fabrication to Application

Abstract: This paper presents a substantial review of the deployment of wearable sensors for healthcare applications. Wearable sensors hold a pivotal position in the microelectronics industry due to their role in monitoring physiological movements and signals. Sensors designed and developed using a wide range of fabrication techniques have been integrated with communication modules for transceiving signals. This paper highlights the entire chronology of wearable sensors in the biomedical sector, starting from their fabrication in a controlled environment to their integration with signal-conditioning circuits for application purposes. It also highlights sensing products that are currently available on the market for a comparative study of their performances. The conjugation of the sensing prototypes with the Internet of Things (IoT) for forming fully functioning sensorized systems is also shown here. Finally, some of the challenges existing within the current wearable systems are shown, along with possible remedies.