This co-innovation project aims to revolutionize fieldwork operations by advancing artificial intelligence (AI) technologies and enhancing human-AI collaboration to address the challenges faced by manufacturing, construction and industrial design companies. These challenges include a shortage of skilled workers, insufficient process documentation and analysis, and inefficient troubleshooting and remote collaboration. The consortium will research technical solutions, develop proof-of-concepts for real-world applications, and evaluate the effectiveness of these solutions through user tests in real-world settings. These innovative solutions will collectively empower fieldworkers and industrial designers with a transformative toolset that improves productivity, safety and scalability of fieldwork operations, revolutionizing fieldwork operations across industries.
Projects
Ongoing 1 projects
Completed 16 projects
In this project, our team aims at developing smart garment for virtual sauna experience.
The project aims at finding the most suitable commercialization path for an augmented reality (AR) based real-estate listing and advertising solution that works in outdoor environments. The solution will combine the advantages of digital and physical advertising. More specifically, it can be considered as a digital version of physical banners that would provide as rich information as web portals while remaining effective for capturing people’s attention, informing precise locations of the properties of interest, and revealing hidden opportunities. The project team at Aalto University plans to develop prototypes and conduct pilots with the collaboration with Finnish real-estate agents. The team will also create business models and IPR portfolio based on thorough market and competitor analysis.
This is a joint project between School of Science, School of Chemical Engineering and School of Electrical Engineering. The project aims at developing a solution for detecting and predicting early signs of pressure ulcer development, based on advanced e-textiles-based sensing and AI technologies.
The goal of the project is to further develop and evaluate a virtual stroke rehabilitation solution that combines task-specific and object-related VR exergames with AI-driven and eTextile-based personalised wearable interfaces to improve the effectiveness of post-stroke rehabilitation, and to prepare its commercialization. The project team, including both technical and commercialization experts, plan to develop and evaluate prototypes and business models through close collaboration with professionals of neurologic therapy.
DataFog aims at developing big data-supported methodologies for forecasting the computing and communication capacity demanded by future vehicular applications in urban areas, and providing an intelligent platform that provides data-driven mathematical models, tested methods and execution environments for capacity and resource management in vehicular fog computing.
This is part of ATTRACT that has received funding from the European Union's Horizon 2020 Research and Innovation Programme (grant number: 777222). DEBARE researches novel gesture recognition using distributed multimodal deep learning and smart sensors. We aim at developing a groundbreaking intelligent sensing platform for intuitive interaction with physical objects. Here you can find HitSeed presentation about the pipeline of running deep learning algorithms on the HitSeed SC2 platform.
5G-MOBIX aims at executing CCAM trials along x-border and urban corridors using 5G core technological innovations to qualify the 5G infrastructure and evaluate its benefits in the CCAM context as well as defining deployment scenarios and identifying and responding to standardisation and spectrum gaps.
The existing assembly and maintenance assistance systems are limited to AR based information visualization and remote communications. They are not intelligent enough to learn and adapt to new situations (e.g. workflows, operating environments, mechanical components, etc.), and are lacking scalability due to the intensive labour work involved in the application development and deployment. CEAMA solves these challenges by enabling automatic knowledge sensing, characterization and transfer. Knowledge here refers primarily to the instructions of assembly and maintenance tasks.
The aim of this project is to demonstrate an end-to-end 5G system providing immersive video services for moving objects such as cars and drones. This will be done by cross-continental testbeds that integrate radio access and core networks developed by different project partners.
(joint work with Department of Civil Engineering) The project aims to automatically monitor the progress of construction sites. The resulting situational awareness can be used for decision making and visualization using Augmented Reality. With better situational awareness, it is possible to decrease waste and improve productivity of construction industry.
The project creates a generic connectivity and middle-ware platform including security to support application developers for different segments without having to redo the whole stack. The platform will be applied and verified in the transport and health sectors; aircraft (and railway) to deliver mobile broadband services as well as support the management of transportation vehicles through sensor and capillary nets and similar for wearable and body sensors in the health area.
This project aims to develop the concept of an intelligent construction site which utilizes and combines data of all production system entities. We investigate what kind of data can be collected in real time from construction sites and what kind of IT systems are required to fully utilize the potential of production control.
2016 - ... Enabling indoor augmented intelligence by recognizing the world.
The project aims at providing models for understanding and evaluating networking performance in ubiquitous cloud computing, and based on the models to develop new solutions that improve the networking performance for mobile cloud applications.