Augmented Reality for Health Monitoring Laboratory

  Application fields Biomedical Engineering, Neurology, Human-Computer Interaction Technologies eXtended Reality (Hololens 2) Goals Parkinson’s Disease (PD) is a neurodegenerative disorder of the central nervous system. PD is the most frequent of movement disorders, resulting chronic and slowly progressive, involving different motor, behavioural and cognitive functions, affecting consequently patient’s quality life. In the last few years, technological progress has provided several extended reality (XR) applications for PD, both in diagnostic and rehabilitation fields. In this scenario, the main aim of the present study is to investigate the use of an XR-based system such as a support diagnostic tool to assess bradykinesia, referring to finger-tapping gestures, in parkinsonian patients. Bradykinesia is the most common symptom of PD. It is characterized by extreme difficulty and slowness on the part of the patient in performing even the simplest movements, resulting in a general sense of...

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    Application field Laparoscopic surgery for colorectal cancer Technology Artificial Intelligence (AI), Computer Vision, Data Analysis Goals Development of artificial intelligence-based analysis method in indocyanine green (ICG) angiography to perform real-time perfusion analysis: Identification of the optimal line for cancer resection   Assessment of the goodness of the vascularization The AI techniques applied to near-infrared fluorescence angiography with indocyanine green is proposed as objective method to support surgeons in their decision-making processes, improving treatment and post-operative complications prevention. Activities Identification of appropriate tracker algorithms to enable fast and accurate tracking of the selected region of interest. Data pre-processing (e.g., appropriate color space for analysis) to make it suitable for the machine learning system. Design and implementation of AI systems for the evaluation of bowel perfusion during colorectal surgery. Challenges...

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Application field Cybersecurity, IoT Technology Cyberattacks, Machine Learning Goals Development of methodologies for assessing the performance of cyberattacks, along with its uncertainty. Profiled attacks relying on machine learning are mainly considered. Methods like cross-validation and Monte Carlo can be exploited to assess the uncertainty and the final aim is to evaluate the vulnerability of an embedded device from the hardware point of view. In this process, measurements are strongly involved with reference to the acquisition of power traces or other leakages during the operation of the device under test. Then, data processing has a crucial role in the vulnerability assessment in case of presence or absence of countermeasures. Furthermore, analysis of a sensor network behavior in the event of cyberattacks, such as Denial of Service or Man-In-The-Middle is critical for development of first reaction behavior methodologies. Development of methodologies for anomaly detection,...

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  Application fields Psychology, Psychiatry, Wellbeing Technologies eXtended Reality (XR), Electroencephalography (EEG), Machine Learning (ML) Goals In psychometric measurements, the measurand is a 'latent variable' of a given subject, accessible through the perception of the participant in the experimental activity. Mental states comprise a heterogeneous class that includes perception, pain experience, belief, desire, intention, emotion and memory. EEG-based measurements of mental states constitute a new approach that can provide real-time monitoring. eXtended Reality (XR) is increasingly being used as an umbrella term that includes virtual reality (VR), augmented reality (AR) and mixed reality (MR) technologies. Especially with reference to VR, the high sense of presence offered, the possibility of immersion of the user in realistic scenarios, and the multi-sensory and multi-perceptual stimulation offer the subject extremely evocative experiences. The following research goals are...

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Application field Rehabilitation, gaming, control Technology Electroencephalography (EEG), artificial intelligence, extended reality Goals Development of a fully wearable brain-computer interface system based on motor imagery that leverages multiple modalities of extended reality delivered neurofeedback. The aims are to enhance the detection of the user's sensorimotor rhythms and to speed up motor imagery skills acquisition. Different types of feedback are delivered both as standalone or jointly by relying on the results of online processing of brain signal. Activities Development of artifact removal technique for low density EEG. Evaluation of motor imagery EEG feature extraction and classification algorithms exploiting both classic machine learning and deep learning approaches. Classification of multiple motor imagery tasks. Development of extended reality applications to deliver neurofeedback. Design and implementation of experimental campaigns. Metrological analysis of the...

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