Projects

Protecting underwater infrastructure and ensuring marine environmental safety in the Baltic Sea are crucial due to rising threats such as subsea cable damage, anchor incidents, and shipwreck-related pollution. This research integrates autonomous underwater sensor networks, advanced acoustic sensing, and artificial intelligence for enhanced maritime security and environmental monitoring. Acoustic arrays assess shipwreck conditions, including hull integrity, corrosion, and fuel presence, using advanced signal processing. Sensor nodes equipped with hydrophones continuously monitor underwater acoustic signals, detecting and classifying anthropogenic and natural sources. Machine learning-driven signal processing enables real-time risk assessment, anomaly detection, and communication with surface units. Expected outcomes include improved hazard detection, more efficient pollution monitoring, and autonomous decision-making, strengthening marine conservation and security in the Baltic Sea.

The general goal is the development of electronic devices for clinical measurement of soft tissues. Specific result is a device for continuous monitoring of the condition of the heart muscle during heart surgery. Heart disease is the most common cause of death (WHO 2019, https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death). Surgery plays an important role in the treatment of advanced heart diseases, with a risk of damage to the muscle (of a stopped heart!) during surgery. Current heart protection methods are poorly controlled, as based on schemes developed by trial and error. The unique solution being created will ensure the real-time usage of protective solutions based on objective heart muscle indicators. The technology being created, also allows measuring of various other muscle, fat and connective tissues, and could also distinguish benign and malignant tissues. The solutions created use inventive electrical impedance spectroscopy technologies of TalTech.

There has been a fast development of new technological devices to monitor glucose levels, matching the ubiquitous dissemination of digital connectivity and social networking. The increase in social, educational, and age disparities among patients, more information on the web and social networks, and the development of better technological devices created a new environment characterized by several challenges. Unfortunately, the ecosystem for treating patients within this new reality has not changed, and there is a need to develop a better understanding of the real value generated by technologies in terms of the patient's behaviors and their decision processes to achieve higher levels of patient empowerment for Type1 and Type 2 diabetes prevention since they have related to different methods of risk perception. These challenges highlight the need to estimate the value offered to each patient by technology to achieve patient empowerment for more efficient prevention. NEODIPPE is born to pursue a crystal clear mission: to explore and identify the best use of technological developments to empower patients to prevent and treat diabetes, which implies innovating the traditional paradigms of health services from occasional appointments to networking monitoring and empowerment, supporting the processes of decision making and proposing new approaches to clinical practices, health organizations, and public financing in terms of the latest needs, resources, and preferences of the patients.

The goal is to study new solutions & principles for electrical impedance spectroscopy (EIS) with significantly improved metrological and functional characteristics, like higher measurement accuracy, resolution and speed, lower power consumption and wider frequency and dynamic ranges. New solutions enhance the existing and enable new applications of EIS in healthcare, biology and industries. The principles & solutions to measure biological & physiological properties of organs, tissues and microorganisms/pathogens, as well as of composites, alloys etc. are the subjects of the research. Unique low-cost low-power miniaturized high-resolution and flexible measurement components with various connectivity (IoT, BAN etc) will be created by new EIS groundings. An important R&D aspect is synchronous signal processing and communication in EIS sensor-arrays. Research aspects: sampling theory AI/ML) and metrology (eg novel calibration techniques, methods of implementation in biology and medicine.

A platform for monitoring energy usage and operational state properties of distribution networks is elaborated, enabling users of distribution networks to access relevant and better time-resolution data to support green energy usage, energy efficiency and the flexibility technologies. The platform being developed will provide extensive support for decision-making and calculation algorithms, enabling efficient availability and management of local area operational data.

"EXCITE brings together the topranked ICT research groups Estonia to work jointly on a focussed, yet broad and extendable, research programme. It will capitalize on the existing expertise to create synergies on the rich but fragmented landscape of the Estonian ICT research. The consortium will advance foundational theories of model verification and data analysis. On this groundwork, it will develop methods and tools for sound practices of designing and analyzing reliable and secure ICT systems processing large data volumes, as demanded by applications to domains of high socioeconomic relevance (cyberphysical and robotic systems, ehealth and biomedical systems). We will start with 10 cooperation themes with clearly defined objectives, methodology and expected results. These themes will be refined and redefined after 3 years. EXCITE will support research sustainability and provide a development opportunity for young researchers by financing 20-30 PhD students and postdocs.

The most significant findings are related to development of the novel wireless closed-loop patient support system i) energy-efficient protocols for body area networks and ii) fast computing methods for the real-time gait analysis. These novel contributions are relevant for the electrical stimulation of the muscles of the patients suffering from neurological diseases (e.g., multiple sclerosis), in particular assisting them with daily life activities. The outcomes of the project are significantly relevant to society because they directly enhance the state-of-the-art assistive devices. The project has created a wider impact in the following ways: 1- ETSI SmartBAN Standard: “Applying SmartBAN MAC (ETSI TS 103 325) for various use-cases”, Work Item Reference, DTR/SmartBAN-0014, Rapporteur, Muhammad Mahtab Alam, -- ETSI SmartBAN is a specific standard for the low-power body area networks and this project usecase is introduced in this workitem which motivated the needs for the future standard compliant devices. 2- Invention: System and method for self-assessment of physical capabilities and condition changes; Owners: Tallinn University of Technology, Motionmon OÜ; Authors: Alar Kuusik, Katrin Gross-Paju, Muhammad Mahtab Alam; Priority number: US16/268134; Priority date: 5.02.2019. 3- Collaboration with Hospitals: We had a positive collaboration with i) East Tallinn Central hospital and ii) West Tallinn Central hospital with specific neurological departments. The methods developed in this project and future devices (beyond this project) comply with the needs of the patients and this will enable enhanced comfortable and effective non-invasive electrical stimulation devices to be used by the patients. Overall, the project yielded significant research output in terms of numbers of scientific publications including 9 Journal and 13 conferences of high quality and impact. So far, these papers are strongly cited by the relevant research community (h-index=9, h-10=9).

Wireless biomedical sensors should dramatically reduce the costs and risks associated with personal health care while being more and more exploited by telemedicine and efficient e-health systems. However, because of the large power consumption of continuous wireless transmission, the battery life of the sensors is reduced for long-term use. Sub-Nyquist continuous-time discrete-amplitude (CTDA) sampling approaches using level-crossing analogto- digital converters (ADCs) have been developed to reduce the sampling rate and energy consumption of the sensors. However, traditional machine learning techniques and architectures are not compatible with the non-uniform sampled data obtained from levelcrossing ADCs. This project aims to develop analog algorithms, circuits, and systems for the implementation of machine learning techniques in CTDA sampled data in wireless biomedical sensors. This “near-sensor computing” approach, will help reduce the wireless transmission rate and therefore the power consumption of the sensor. The output rate of the CTDA is directly proportional to the activity of the analog signal at the input of the sensor. Therefore, artificial intelligence hardware that processes CTDA data should consume significantly less energy. For demonstration purposes, a prototype biomedical sensor for the detection and classification of sleep apnea will be developed using integrated circuit prototypes and a commercially available analog front-end interface. The sensor will acquire electrocardiogram and bioimpedance signals from the subject and will use data fusion techniques and machine learning techniques to achieve high accuracy.

R&D work is preliminary study for industrial measurement techniques