A digital twin facilitates the means to monitor, understand, and optimize the functions of the physical entity and provides continuous feedback to improve quality of life, and Medical Cyber-Physical Systems (MCPS) is an integral part of this vision. Many studies focus on human motion to digitize data for further analysis. The literature review presented here emphasizes on gait analysis and gait events detection using wearable devices, which compare results by testing on different groups of individuals. Amongst those, there is a focus on digitizing activities for athletes and sports activities. However, there is a lack of research that address configurability for this type of MCPS. Adding new physical devices to an established MCPS requires manual configuration. Recent studies either solve the issue of users’ mobility by providing a wireless solution with local storage, or sacrifice mobility in order to provide real-time information through wired communication. However, group physical activity applications, such as sports coaching and group physiotherapy, use customized devices that need to be automatically configured in the system. In addition, these systems need to support mobility and real-time data presentation. To solve this problem, a framework is proposed to design a wellbeing Cyber-Physical System (CPS) that focuses on system configurability, providing real-time data of body sensor networks while supporting wireless and mobile communication. A communication protocol is proposed to allow seamless integration and communication of system components, and to enable bandwidth-conscious data transmission. As a proof of concept, a configurable CPS for gait activities monitoring is designed to read, visualize, and backup spatiotemporal data from one or more multi-sensory physical devices over conventional Wi-Fi and in real-time. Two experiments were performed using the implemented CPS. The first experiment was performed outdoors and tested if the CPS components would recognize each other and work seamlessly over foreign networks while providing usable information. The second experiment was performed in collaboration with the Health Sciences Department using our system and the Tekscan Strideway gait mat simultaneously to compare results and to ensure accuracy. In addition, this experiment tested configurability of the system by using different measurement devices for different users.

The emergence of the Internet of Things (IoT) is introducing a new era to the realm of computing and technology. The proliferation of sensors and actuators that are embedded in things enables these devices to understand the environments and respond accordingly more than ever before. Additionally, it opens the space to unlimited possibilities for building applications that turn this sensation into big benefits, and within various domains. From smart cities to smart transportation and smart environment and the list is quite long. However, this revolutionary spread of IoT devices and technologies rises big challenges. One major challenge is the diversity in IoT vendors that results in data heterogeneity. This research tackles this problem by developing a data management tool that normalizes IoT data. Another important challenge is the lack of practical IoT technology with low cost and low maintenance. That has often limited large-scale deployments and mainstream adoption. This work utilizes open-source data analytics in one unified IoT framework in order to address this challenge. What is more, billions of connected things are generating unprecedented amounts of data from which intelligence must be derived in real-time. This unified framework processes real-time streams of data from IoT. A questionnaire that involved participants with background knowledge in IoT was conducted in order to collect feedback about the proposed framework. The aspects of the framework were presented to the participants in a form of demonstration video describing the work that has been done. Finally, using the participants’ feedback, the contribution of the developed framework to the IoT was discussed and presented.

Indiana University-Purdue University Indianapolis (IUPUI) / This project aims to develop a cloud-based platform that integrates sensors with business intelligence for real-time energy management at the plant level. It provides facility managers, an energy management platform that allows them to monitor equipment and plant-level energy consumption remotely, receive a warning, identify energy loss due to malfunction, present options with quantifiable effects for decision-making, and take actions, and assess the outcomes. The objectives consist of: 1. Developing a generic platform for the monitoring energy consumption of industrial equipment using sensors 2. Control the connected equipment using an actuator 3. Integrating hardware, cloud, and application algorithms into the platform 4. Validating the system using an Energy Consumption Forecast scenario A Demo station was created for testing the system. The demo station consists of equip- ment such as air compressor, motor and light bulb. The current usage of these equipment is measured using current sensors. Apart from current sensors, temperature sensor, pres- sure sensor and CO2 sensor were also used. Current consumption of these equipment was measured over a couple of days. The control system was tested randomly by turning on equipment at random times. Turning on the equipment resulted in current consumption which ensured that the system is running. Thus, the system worked as expected and user could monitor and control the connected equipment remotely.

<div><div><div><p>This project aims to develop a cloud-based platform that integrates sensors with business intelligence for real-time energy management at the plant level. It provides facility managers, an energy management platform that allows them to monitor equipment and plant-level energy consumption remotely, receive a warning, identify energy loss due to malfunction, present options with quantifiable effects for decision-making, and take actions, and assess the outcomes. The objectives consist of:</p><ol><li><p>Developing a generic platform for the monitoring energy consumption of industrial equipment using sensors</p></li><li><p>Control the connected equipment using an actuator</p></li><li><p>Integrating hardware, cloud, and application algorithms into the platform</p></li><li><p>Validating the system using an Energy Consumption Forecast scenario</p></li></ol><p>A Demo station was created for testing the system. The demo station consists of equipment such as air compressor, motor and light bulb. The current usage of these equipment is measured using current sensors. Apart from current sensors, temperature sensor, pres- sure sensor and CO2 sensor were also used. Current consumption of these equipment was measured over a couple of days. The control system was tested randomly by turning on equipment at random times. Turning on the equipment resulted in current consumption which ensured that the system is running. Thus, the system worked as expected and user could monitor and control the connected equipment remotely.</p></div></div></div>