Innovative Smart Edge Solutions: Video Tutorials on Improving Cities, Health, and Factories
Explore how smart edge technology is transforming traffic management, digital rehabilitation, and manufacturing. Three YouTube tutorial videos showcase innovative solutions from experts, emphasizing the groundbreaking impact of these advancements on cities, healthcare, and industry.
SmartEdge tutorials highlight the transformative potential of smart edge computing technologies “Smart City and SmartEdge”, “Edge Intelligence in Digital Rehabilitation” and “Low-Code Edge Intelligence in Smart Factories”.
Smart City and SmartEdge
In this tutorial video, Kari Koskinen (Conveqs) presents a use case focusing on traffic management within smart cities. The discussion highlights industry challenges, the role of swarm intelligence, and smart edge solutions. The distinction between public and private spaces in cities complicates traffic management. Public spaces, like streets, are accessible to everyone, while private spaces, like homes, are not. Also, existing traffic management systems are outdated, relying on simple, isolated logic without understanding real-time traffic conditions.
City officials are often conservative due to the high risk and low reward associated with implementing new traffic solutions. Advances in sensor technology and AI offer new possibilities for traffic management. Cars are becoming more intelligent and capable of sharing data with city infrastructure. Smart edge solutions address some technical challenges, but market penetration remains difficult.
Improved traffic management can lead to significant socioeconomic benefits, such as reduced travel times and increased safety. However, these benefits primarily accrue to road users, not the city itself. Convex has established a test area in Helsinki with 17 radars, cameras, and seven traffic light controllers. The smart traffic management discussed in the tutorial highlights the complexities and potential benefits of integrating new technologies. While challenges remain, the advancements in smart edge solutions and AI offer promising opportunities for the future.
Edge Intelligence and Digital Rehabilitation
In this presentation, Berk Buzcu (Hes-so) discusses the advancements made in digital rehabilitation using smart edge technology. The primary objective is to create a prototype with dynamic and self-organizing wearables to support head and neck rehabilitation. This involves integrating data from various sensors placed on different body parts to facilitate continuous home-based physiotherapy through near real-time feedback and analytics. Traditional physiotherapy relies heavily on manual skills and episodic observation by therapists. However, this method can lead to delayed healing and extra pain if exercises are not performed correctly at home. Digital rehabilitation aims to address this by providing a tool that can detect and correct mistakes during exercises, especially for individuals in pain who are more likely to make errors.
The system being developed includes a mobile application that shows progress on a 3D model, applying sensor data to visualize the user’s movements and identify errors. The architecture involves using sensors on the head and shoulders to model motion, with the orchestrator holding the knowledge of therapies and exercises. The coordinator dynamically arranges sensor placement and data collection. Challenges include achieving near real-time analysis and utilizing semantic-based orchestration to handle heterogeneous sensors. The system uses sensors which include accelerometers, gyroscopes, and compasses to model motion. Data is streamed continuously using Bluetooth low energy technology.
SmartEdge collaborates with physiotherapists to ensure the exercises are effective and correctly modelled. The ultimate goal is to provide real-time feedback and utilize centralized knowledge in edge nodes to combine various sensor capabilities. Future developments may include incorporating cameras and other sensors for even more complex scenarios.
Low-code Edge Intelligence in Smart Factories
In this video, Kirill Dorofeev (Siemens) discusses the application of low-code edge intelligence in smart factories, emphasizing the need for more flexible manufacturing processes to meet the demand for individualized products. This flexibility requires reconfigurable manufacturing systems and adaptable control software. Kirill highlights the importance of edge intelligence and low-code applications for managing control software in smart factories. The goal is to develop recipes that define capabilities, skills, and workflows, allowing for quick adaptation of both hardware and software to accommodate changing orders.
The presentation describes the challenges faced in traditional factories, where systems often date back to the 1980s or earlier. In such environments, any downtime can result in significant financial losses, making it crucial to thoroughly test new solutions before implementation. The tutorial also introduces the laboratory at Siemens, which serves as a testing ground for these concepts. The lab features a manufacturing line with a hexagon-shaped conveyor and standalone robotic cells, each responsible for specific production tasks. The fundamental communication protocol used in the lab is OPC UA, which supports platform-independent, secure, and real-time communication. OPC UA also offers robust information modelling capabilities, allowing for the creation of standardized and custom data models.
The presentation concludes with a discussion on defining capabilities and skills using OPC UA. Capabilities are abstract, implementation-independent functions like transport or pick-and-place, while skills are the specific implementations of these capabilities. The goal is to create a unified skill interface for consistent access across all devices.
Links:
Video Smart City and SmartEdge [Automotive Use Case]
Video Edge Intelligence in Digital Rehabilitation [Health Use Case]
Video Low-Code Edge Intelligence in Smart Factories [Manufacturing Use Case]