H2TRAIN proposal is funded on the sixth edition of the Electronic Components and Systems (ECS) Strategic Research and Innovation Agenda (ECS-SRIA) topics and major challenges for enabling digital technologies in holistic health-lifestyle supported by artificial intelligence (AI) networks. Biosensors for e-health and smart tracking of sport and fitness are a class of devices that is dominating the consumer and professional market with an unprecedented growth. Despite the impressive capabilities of recent approaches, several prospective revolutionary improvements are still open points, mainly in relationship with four factors: sensing new biosignals and tracking new activity patterns; improving battery lifetime and energy management for continuous use; and secure, reliable and efficient data analysis with AI algorithms and connectivity with the IoT. H2TRAIN aims at advancing the state of the art in this respect, taking profit from the remarkable properties and synergistic potential of one-dimensional (1D) and two-dimensional (2D) materials (1DM and 2DM), enabling more sensitive, efficient, and miniaturized biosensing capabilities within the established CMOS technology framework. This will contribute to the growth of e-health services assisted by AI and will fortify the development of Internet of Things (IoT) applications in health & wellbeing and digital society. H2TRAIN not only facilitates digital technology but also involves the development of new 1DM and 2DM-based devices for sensing, energy harvesting and supercapacitor storage. These innovations serve to integrate sport and health activities into IoT applications, making them accessible as wearable technology. H2TRAIN combines mature CMOS technology products for health and sport sensing with embedded intelligence as a cross-sectional technology. This combination offers a broad spectrum of technology demonstrators (TD) based on advanced sensors, such as tattoo sweat, C-reactive protein, cortisol and lactate.

Integrated digital diagnostics can support complex surgeries in many anatomies where brain tumour surgery is one of the most complex cases. Neurosurgeons face several challenges during brain tumour surgeries, such as critical tissue and brain tumour margins differentiation or the interpretation of large amount of data available provided by several independent devices. To overcome these challenges, STRATUM will develop a 3D Decision Support Tool for brain surgery guidance and diagnostics (reaching TRL7) based on multimodal data processing through Artificial Intelligence (AI) algorithms that will be integrated as an energy-efficient Point-of-Care computing tool. It will be developed following a co-creation methodology involving key stakeholders and end-users. STRATUM will pursue the following objectives: 1) To foster advances in personalized medicine based on multimodal data (including the emerging hyperspectral imaging modality) and AI. 2) To increase the intraoperative diagnostic accuracy of brain tumours, improving surgical outcomes and patients’ quality of life. 3) To reduce surgery time with respect to current neurosurgical operation durations. 4) To improve current cost- and energy-efficiency of neurosurgical workflows. 5) To demonstrate the prototype in a two-year clinical study in 3 clinical sites, including an early health technology assessment. 6) To prepare the preliminary business plan and the TRL9 roadmap after the project ending. An optimized integration and processing of available and new emerging data sources would aid surgeons in timely efficient and correct decision-making in tissue removal. This would maximize the degree of resection while simultaneously minimize the risk of neurological deficits. Moreover, time efficient surgical procedures not only benefit the patients directly by minimizing anaesthesia time and risks of e.g. postoperative infections, but also indirectly by optimizing resources of the health care system.