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The rapid development of electronic material and sensing technology has enabled research to be conducted on liquid metal-based soft sensors. The application of soft sensors is widespread and has many applications in soft robotics, smart prosthetics, and human-machine interfaces, where these sensors can be integrated for precise and sensitive monitoring. Soft sensors can be easily integrated for soft robotic applications, where traditional sensors are incompatible with robotic applications as these types of sensors show large deformation and very flexible. These liquid-metal-based sensors have been widely used for biomedical, agricultural and underwater applications. In this research, we have designed and fabricated a novel soft sensor that yields microfluidic channel arrays embedded with liquid metal Galinstan alloy. First of all, the article presents different fabrication steps such as 3D modeling, printing, and liquid metal injection. Different sensing performances such as stretchability, linearity, and durability results are measured and characterized. The fabricated soft sensor demonstrated excellent stability and reliability and exhibited promising sensitivity with respect to different pressures and conditions. 

Biomimicr y is a i eld of study that involves imitating the designs and processes of nature to solve problems using man- made systems. Biomimicr y offer s an empathetic, interconnected under standing of how life wor ks and where we i t in. In bio- inspired designs, the main challenge is to develop a sustainable and effective fr amewor k that can be used in the real wor ld. M ost of the soft robotic designs are state-of-the-ar t models that cannot be used in real sensing applications. In this research, we propose a sustainable sensor-integr ated modular soft robot model that can be used for locomotion and gr ipping applications. This wor k presents the steps involved in modeling, designing, and fabr icating soft and l exible end effector s that can be used for soft robots with integr ated soft stretchable sensor s. We demonstr ate the design methodology involved in modeling and simulating the proposed model for robots that would require effector s with increased functionalities. 

This research aims to develop a wearable monitoring system that can help in monitoring respiratory distress. Drowning-related accidents are 3rd leading cause of unintentional death. Among the victims most of them are children and world statistics state that there are 4000 fatalities per day in children due to drowning. In this research project, we propose a wearable system that can help in monitoring the heart rate, oxygen level, and body temperature variability, along with the accelerometer. Our proposed framework is designed as an anklet that can help detect drowning incidents early 

The amount of greenhouse gas emissions from streetlights is equivalent to 2.6 million cars with as many as 26 million streetlights in the United States. The proposed IoT controller integrates sensors to make these streetlights as hubs for smart environment monitoring with effective energy usage. Conservation of energy is one of the main concerns in the modern era, and energy coming from the sun can be utilized efficiently alongside a smart streetlight management system instead of conventional streetlight management techniques. Additionally, with streetlights being present throughout a city, the opportunity to collect city-wide weather data is proposed. To this end, a solar-powered IoT-based smart street lighting and environmental monitoring system is proposed. The proposed energy-efficient IoT-based system uses a microcontroller to control light-emitting diode (LED) streetlights depending on lighting conditions and vehicle detection, ensuring that the streetlights can be turned on when needed. 

This paper proposes a reconfigurable framework to automated reconfigurable secure home system. The proposed system has 3 main features: monitoring controlling of smart home automation through password-protected door lock system, the monitoring controlling of day-to-day electronic devices, fire safety with room temperature monitoring and antitheft system. In this paper, different sensor combinations are to be integrated with the FPGA board. For the Fire safety module, a temperature monitoring sensor has been used that will display the temperature continuously. If the temperature in the room exceeds 60 degree Celsius, then it will turn on the buzzer or alarm for fire safety. For the Anti-theft system, infrared (IR) sensors are fitted in each window. If any unauthorized person tries to breach through the window, both IR sensor signals will be turned ON and the output of the buzzer goes to high. For the door lock system, a finite state machine (FSM) has been implemented. This work focuses on a scalable smart home automation framework using the FPGA board that can be used for integration of multiple sensors in a cost-effective way. The research in this project is focused on processing multiple analog sensor inputs through the FPGA board. 

Sitting is the most common status of modern human beings. Many people are sitting with bad posture which may lead to postural pain. Especially for people with short term-disabilities, sitting in the right posture is very important. In this research, we propose a posture recognition system on an office chair that can categorize different health-related sitting postures to prevent harm from bad sitting postures. The smart chair system consists of an array of five flex sensors integrated into an FPGA board. The output of the system is the classification result of the sitting posture. In this paper, several health-related sitting postures are selected. The sitting postures are: 1-sit straight; 2-left recline; 3-right recline; 4-lounge;5-lean backward; 6-cross left leg; 7-cross right leg. The proposed reconfigurable framework will be integrated as part of smart ambient assisted living with user-centered health monitoring system. 

Wearable devices are ubiquitous and Internet of Things (IoT) devices have made it possible by connecting real-time devices to virtual cloud. There are also a tremendous number of IoT-enabled consumer products for various healthcare applications. Mostly, IoT devices are used for health monitoring systems, though other business and service communities are customizing the IoT technology for greater opportunity and long-term benefit. Wearable health devices have been used for better health monitoring and exchanging more data with the physician to get the guidance of treatment or earlier diagnostic. Health monitoring in athletes is one of the multifaceted applications of wearable IoT devices whereas these devices collect and store data on their performance and progression. This technology can protect athletes by detecting any adverse health problem that occurs during the training period or at the time of the game. In this paper, we investigate the real-time monitoring of physiological parameters of the athlete during game time and performance analysis from the stored data. Continuous health monitoring during game time and off-days will reduce sports-related risks,stress and injuries of an athlete even sometimes it can save them from life-risk fatal accidents. This research integrates an IoT-based framework to develop a stress index for athletes that can be used as an indicator for monitoring athlete’s health. The proposed framework helps in monitoring the variability of the sensor information for the long-term analysis. 

With a steady rise in the aging population, researchers predict that one out of every six people will be over the age of 65 by 2020. Seniors need to monitor their health consistently as chronic diseases such as diabetes, arthritis, dementia, are highly prevalent among them. This has lead to an increase in the use of devices such as sensors, cameras, and robots, with technologies such as artificial intelligence and the internet of medical things. These can help in designing innovative solutions for improving the daily life of seniors and helping them to be more independent. Though the recent advancements in technology can help in developing a better assisted living, the key challenge is to make a reliable framework that can help the consumer in using those product at ease. In challenging times, such as the current pandemic, the need for a framework that can help in reducing social interaction is particularly significant. In this paper, we discuss an interactive framework with the help of a smart camera and smart speakers that can help seniors to manage their essential resources at ease. This Internet of Things based framework can help seniors in having an organized inventory without ever needing to leave their home. 

Anxiety disorders affect more than 18 percent of the population and is the most common mental illnesses in the US. There is a great demand to address this emerging epidemic with tools to differentiate and diagnose such disorders, and to create awareness especially in places like NorthEast Texas which is home to 1.5 million people with 58 percent of them living in rural areas. The goal of the proposed device is to diagnose as many anxiety disorders as possible, in real-time using the diagnosing wearable framework, SolicitudeSavvy, which uses technology such as the Internet of Things (IoT), a network of interconnected devices, to accomplish such a task. The proposed IoT-based device has two components: a custom-built wearable necklace that contains sensors to collect data about the user as they go about their day and a low-cost portable system that monitors Electrooculography (EoG) signals using a camera. The partial necklace attaches to the shirt and opens halfway around the wearer's neck and the EoG can be attached to any eyewear. The device monitors the user throughout the day, and even as they go to bed at night. This information is accumulated in the IoT cloud and analyzed to see exactly what type of disorder(s) the patient may suffer from. The authorized personnel i.e. doctor or therapist can use this pattern to find a treatment that best suits them and is most likely to resolve their affliction. 

A learning disorder is associated with the ability of the child to process the information effectively. The purpose of special education is to provide equal access to education for all children to help them succeed in the regular curriculum through specialized services. Children with anxiety, hyperactive, or attention-deficit disorders require special assistance to help them stay at their normal level and thus effectively suit in a classroom setting. With the advancement in technology, the landscape of special education is rapidly changing. The motivation for this research is to develop an Internet of Things-based affective computing framework, Affect-learn, that can help teachers in identifying the hyperactivity or inattentiveness in children, and help them improve the overall learning outcomes. The proposed research is validated with the help of commercially available off the shelf components. The measure of success in this research is the response time of the proposed framework and the efficiency of emotion elicitation.