Project acronym ALGOA
Project Novel algorithm for treatment planning of patients with osteoarthritis
Researcher (PI) Rami Kristian KORHONEN
Host Institution (HI) ITA-SUOMEN YLIOPISTO
Call Details Proof of Concept (PoC), PC1, ERC-2016-PoC
Summary Osteoarthritis (OA) is a common joint disease affecting over 40 million Europeans. Most common consequences of OA are pain, disability and social isolation. What is alarming, the number of patients will increase 50% in developed countries during the next 20 years. Moreover, the economic costs of OA are considerable since 1) direct healthcare (hospital admissions, medical examinations, drug therapy, etc.) and 2) productivity costs due to reduced performance while at work and absence from work have been estimated to be between 1% and 2.5% of the gross domestic product (GDP) in Western countries.
We have developed an algorithm that is able to predict the progression of OA for overweight subjects while healthy subjects do not develop OA. When employed in clinical use, preventive and personalised treatments can be started before clinically significant symptoms are observed. This marks a major breakthrough in improving the life quality of OA patients and patients prone to OA. Our discovery will directly lead to longer working careers and lesser absence from work, and will result subsequently increased productivity. Moreover, the patients are expected to live longer due to reduced disability and social isolation.
Moreover, the discovery provides economic long-term relief for the health care system, which is burdened by increasing geriatric population and stringent economic environment. With our tool, as an example, by eliminating 25% of medical examinations annually due to overweight or obesity in Finland (150.000 patients), we estimate to decrease annual direct costs by 140M€ and indirect costs by 185M€.
In the PoC project we will carry out technical proof-of-concept and perform pre-commercialisation actions to shorten the time to market. The ultimate goal after the project is to develop our innovation towards a software product, aiding the OA diagnostics in hospitals and having commercialisation potential amongst medical device companies.
Summary
Osteoarthritis (OA) is a common joint disease affecting over 40 million Europeans. Most common consequences of OA are pain, disability and social isolation. What is alarming, the number of patients will increase 50% in developed countries during the next 20 years. Moreover, the economic costs of OA are considerable since 1) direct healthcare (hospital admissions, medical examinations, drug therapy, etc.) and 2) productivity costs due to reduced performance while at work and absence from work have been estimated to be between 1% and 2.5% of the gross domestic product (GDP) in Western countries.
We have developed an algorithm that is able to predict the progression of OA for overweight subjects while healthy subjects do not develop OA. When employed in clinical use, preventive and personalised treatments can be started before clinically significant symptoms are observed. This marks a major breakthrough in improving the life quality of OA patients and patients prone to OA. Our discovery will directly lead to longer working careers and lesser absence from work, and will result subsequently increased productivity. Moreover, the patients are expected to live longer due to reduced disability and social isolation.
Moreover, the discovery provides economic long-term relief for the health care system, which is burdened by increasing geriatric population and stringent economic environment. With our tool, as an example, by eliminating 25% of medical examinations annually due to overweight or obesity in Finland (150.000 patients), we estimate to decrease annual direct costs by 140M€ and indirect costs by 185M€.
In the PoC project we will carry out technical proof-of-concept and perform pre-commercialisation actions to shorten the time to market. The ultimate goal after the project is to develop our innovation towards a software product, aiding the OA diagnostics in hospitals and having commercialisation potential amongst medical device companies.
Max ERC Funding
150 000 €
Duration
Start date: 2018-01-01, End date: 2019-06-30
Project acronym GraTA
Project Graphene Tunneling Accelerometer
Researcher (PI) Pertti Hakonen
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Call Details Proof of Concept (PoC), ERC-2016-PoC, ERC-2016-PoC
Summary The commercialization of existing tunneling accelerometers is hindered by their complex fabrication and long-term instability. Based on our results and expertise from the currently running ERC project, we propose an innovative design using graphene as the game-changing material for tunneling accelerometers.
The proposed graphene tunneling accelerometer has clear technical advantages, such as smaller size, wider bandwidth, simpler fabrication, and natural stability. A careful patent search indicates that the concept is novel for patent protection. Our industrial contacts have clear interest in this innovation, detailed user requirements on the accelerometer specifications have been recently mapped up. We have a capable team and a feasible plan towards the real-life commercialization of the proof-of-concept innovation. The output of the project is expected to be a long-pursued efficient application of graphene in high-end sensors. Continuity of the development and commercialization is strongly considered also after the project funding.
Summary
The commercialization of existing tunneling accelerometers is hindered by their complex fabrication and long-term instability. Based on our results and expertise from the currently running ERC project, we propose an innovative design using graphene as the game-changing material for tunneling accelerometers.
The proposed graphene tunneling accelerometer has clear technical advantages, such as smaller size, wider bandwidth, simpler fabrication, and natural stability. A careful patent search indicates that the concept is novel for patent protection. Our industrial contacts have clear interest in this innovation, detailed user requirements on the accelerometer specifications have been recently mapped up. We have a capable team and a feasible plan towards the real-life commercialization of the proof-of-concept innovation. The output of the project is expected to be a long-pursued efficient application of graphene in high-end sensors. Continuity of the development and commercialization is strongly considered also after the project funding.
Max ERC Funding
149 858 €
Duration
Start date: 2017-08-01, End date: 2018-07-31
Project acronym SMARTSOUND
Project Pre-Commercialisation of Sound Recognition for Surveillance Applications
Researcher (PI) Tuomas Oskari VIRTANEN
Host Institution (HI) TAMPEREEN KORKEAKOULUSAATIO SR
Call Details Proof of Concept (PoC), PC1, ERC-2016-PoC
Summary Audio communication is a major tool for businesses to maintain their competitiveness in the global market. This market is expected to treble by 2020 to $2.145 billion and creates a great demand for novel ideas, such as acoustic pattern recognition technologies. Similarly, the explosion in big data is calling for new data classification methods for improved data indexation and real-time monitoring of the data streams.
We have developed acoustic pattern classification methods that are able to detect and recognise a large number of different types of sounds in various everyday contexts. Current acoustic monitoring solutions are able to detect only a small number of very prominent sound events (e.g. baby crying, doorbell), and are not able to operate in realistic environments, where other interfering sounds and reverberation is present. In real life sound recognition recordings, we have recently advanced the deep neural network state-of-the-art by a large margin.
The advancement of the methods has enabled recognition of new types of sounds in realistic environments, which were considered infeasible just a few years ago, allowing development of novel applications of sound analysis. We’d expect our technology find its way to various applications, such as 1) surveillance of homes and other buildings for threat detection, 2) navigation, interaction and self-awareness of robots as well as interconnected smart devices, and 3) data indexation operations in video management, just to name few.
Within this PoC project, we will prove the efficiency of our technology in real life setting. The goals of the PoC project are to establish the technical feasibility of our idea, implement a commercial prototype of the proposed software and establish its commercialisation potential via various activities.
Summary
Audio communication is a major tool for businesses to maintain their competitiveness in the global market. This market is expected to treble by 2020 to $2.145 billion and creates a great demand for novel ideas, such as acoustic pattern recognition technologies. Similarly, the explosion in big data is calling for new data classification methods for improved data indexation and real-time monitoring of the data streams.
We have developed acoustic pattern classification methods that are able to detect and recognise a large number of different types of sounds in various everyday contexts. Current acoustic monitoring solutions are able to detect only a small number of very prominent sound events (e.g. baby crying, doorbell), and are not able to operate in realistic environments, where other interfering sounds and reverberation is present. In real life sound recognition recordings, we have recently advanced the deep neural network state-of-the-art by a large margin.
The advancement of the methods has enabled recognition of new types of sounds in realistic environments, which were considered infeasible just a few years ago, allowing development of novel applications of sound analysis. We’d expect our technology find its way to various applications, such as 1) surveillance of homes and other buildings for threat detection, 2) navigation, interaction and self-awareness of robots as well as interconnected smart devices, and 3) data indexation operations in video management, just to name few.
Within this PoC project, we will prove the efficiency of our technology in real life setting. The goals of the PoC project are to establish the technical feasibility of our idea, implement a commercial prototype of the proposed software and establish its commercialisation potential via various activities.
Max ERC Funding
150 000 €
Duration
Start date: 2017-09-01, End date: 2019-02-28
Project acronym SNABO
Project Self-calibrating nanobolometer based on superconductor–normal-metal hybrids
Researcher (PI) Mikko Pentti Matias Möttönen
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Call Details Proof of Concept (PoC), ERC-2016-PoC, ERC-2016-PoC
Summary In this project, we develop a microwave nanobolometer invented in the ERC Starting Grant ”Single-Photon Microwave Devices: era of quantum optics outside cavities (SINGLEOUT)” into a proof of concept and carry out a market analysis and partnering with the relevant industrial players in the field.
For a successful proof of concept, a self-calibrating function will be implemented into the nanobolometer, providing us with an extremely sensitive and easy-to-use detector for microwave radiation. The new device will be designed, fabricated, and measured.
Due to the lack of spectrum analysers operating at cryogenic temperatures, our self-calibrating nanobolometer will provide a must-to-have piece of equipment for R&D facilities working on cryoelectronics and quantum technology. Thus licensing agreements with companies working on cryostats and cryosystems will be pursued.
The key performance indicators of our nanobolometer such as detection bandwidth, dynamics range, and sensitivity will be compared with the existing room-temperature technology and a potential market share for our nanobolometer will be mapped. Especially, the opportunities for commercial systems already utilizing cryogenic components such as superconducting filters at cellular phone base stations will be investigated.
During this ERC PoC project, also other business opportunities will be studied and possibilities for founding a spin-out company will be actively sought for.
Summary
In this project, we develop a microwave nanobolometer invented in the ERC Starting Grant ”Single-Photon Microwave Devices: era of quantum optics outside cavities (SINGLEOUT)” into a proof of concept and carry out a market analysis and partnering with the relevant industrial players in the field.
For a successful proof of concept, a self-calibrating function will be implemented into the nanobolometer, providing us with an extremely sensitive and easy-to-use detector for microwave radiation. The new device will be designed, fabricated, and measured.
Due to the lack of spectrum analysers operating at cryogenic temperatures, our self-calibrating nanobolometer will provide a must-to-have piece of equipment for R&D facilities working on cryoelectronics and quantum technology. Thus licensing agreements with companies working on cryostats and cryosystems will be pursued.
The key performance indicators of our nanobolometer such as detection bandwidth, dynamics range, and sensitivity will be compared with the existing room-temperature technology and a potential market share for our nanobolometer will be mapped. Especially, the opportunities for commercial systems already utilizing cryogenic components such as superconducting filters at cellular phone base stations will be investigated.
During this ERC PoC project, also other business opportunities will be studied and possibilities for founding a spin-out company will be actively sought for.
Max ERC Funding
149 838 €
Duration
Start date: 2016-12-01, End date: 2018-05-31
