Developing an Autonomous Mobile Robotic Device for Monitoring and Assisting Older People

A progressive increase of the elderly population in the world has required technological solutions capable of improving the life prospects of people suffering from senile dementias such as Alzheimer's. Socially Assistive Robotics (SAR) in the research field of elderly care is a solution that can ensure, through observation and monitoring of behaviors, their safety and improve their physical and cognitive health. A social robot can autonomously and tirelessly monitor a person daily by providing assistive tasks such as remembering to take medication and suggesting activities to keep the assisted active both physically and cognitively. However, many projects in this area have not considered the preferences, needs, personality, and cognitive profiles of older people. Moreover, other projects have developed specific robotic applications making it difficult to reuse and adapt them on other hardware devices and for other different functional contexts. This thesis presents the development of a scalable, modular, multi-tenant robotic application and its testing in real-world environments. This work is part of the UPA4SAR project ``User-centered Profiling and Adaptation for Socially Assistive Robotics''. The UPA4SAR project aimed to develop a low-cost robotic application for faster deployment among the elderly population. The architecture of the proposed robotic system is modular, robust, and scalable due to the development of functionality in microservices with event-based communication. To improve robot acceptance the functionalities, enjoyed through microservices, adapt the robot's behaviors based on the preferences and personality of the assisted person. A key part of the assistance is the monitoring of activities that are recognized through deep neural network models proposed in this work. The final experimentation of the project carried out in the homes of elderly volunteers was performed with complete autonomy of the robotic system. Daily care plans customized to the person's needs and preferences were executed. These included notification tasks to remember when to take medication, tasks to check if basic nutrition activities were accomplished, entertainment and companionship tasks with games, videos, music for cognitive and physical stimulation of the patient

Deep learning for health outcome prediction

Modern medical data contains rich information that allows us to make new types of inferences to predict health outcomes. However, the complexity of modern medical data has rendered many classical analysis approaches insufficient. Machine learning with deep neural networks enables computational models to process raw data and learn useful representations with multiple levels of abstraction. In this thesis, I present novel deep learning methods for health outcome prediction from brain MRI and genomic data. I show that a deep neural network can learn a biomarker from structural brain MRI and that this biomarker provides a useful measure for investigating brain and systemic health, can augment neuroradiological research and potentially serve as a decision-support tool in clinical environments. I also develop two tensor methods for deep neural networks: the first, tensor dropout, for improving the robustness of deep neural networks, and the second, Kronecker machines, for combining multiple sources of data to improve prediction accuracy. Finally, I present a novel deep learning method for predicting polygenic risk scores from genome sequences by leveraging both local and global interactions between genetic variants. These contributions demonstrate the benefits of using deep learning for health outcome prediction in both research and clinical settings.Open Acces

Collaborative and Cooperative Robotics Applications using Visual Perception

The objective of this Thesis is to develop novel integrated strategies for collaborative and cooperative robotic applications. Commonly, industrial robots operate in structured environments and in work-cell separated from human operators. Nowadays, collaborative robots have the capacity of sharing the workspace and collaborate with humans or other robots to perform complex tasks. These robots often operate in an unstructured environment, whereby they need sensors and algorithms to get information about environment changes. Advanced vision and control techniques have been analyzed to evaluate their performance and their applicability to industrial tasks. Then, some selected techniques have been applied for the first time to an industrial context. A Peg-in-Hole task has been chosen as first case study, since it has been extensively studied but still remains challenging: it requires accuracy both in the determination of the hole poses and in the robot positioning. Two solutions have been developed and tested. Experimental results have been discussed to highlight the advantages and disadvantages of each technique. Grasping partially known objects in unstructured environments is one of the most challenging issues in robotics. It is a complex task and requires to address multiple subproblems, in order to be accomplished, including object localization and grasp pose detection. Also for this class of issues some vision techniques have been analyzed. One of these has been adapted to be used in industrial scenarios. Moreover, as a second case study, a robot-to-robot object handover task in a partially structured environment and in the absence of explicit communication between the robots has been developed and validated. Finally, the two case studies have been integrated in two real industrial setups to demonstrate the applicability of the strategies to solving industrial problems