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Life-long-logging: Data analysis from wearable and nearable technologies for ADL classification and Digital Twin representation
This project aims to develop a novel approach for tracking a person's health condition changes using daily life data, biosignals, and nearable information. The Life-long-logging system provides meaningful data for medical staff and directly engages patients and their caregivers. We integrate people's health status within a sensory system encompassing wearables and nearables embedded within the person's life without being obtrusive. This enables continuous tracking of users' health states. To obtain reliable data, we integrate tested medical class biosensors and validate the data with specific patient populations, caregivers, doctors, and robot experts. Additionally, we develop a graphical model to visualize the relationship between clinical information, remote sensing information, environmental factors, and robot-acquired data. This model will predict health status change from robot interventions and generate a meaningful inference about a person's state. A digital representation - above all, the digital twin - bridges the gap between the physical and virtual system, improving the interpretation of reality using sound data collection and interpretation.
The main goal of this project is to analyze data acquired from a network of wearable and nearable devices for the automatic classification of ADL (Activities of Daily Living). The second aim is to provide a score representing the subjects' ability to perform those activities and correlate the measurements with the ICF (International Classification of Functioning, Disability and Health) scores provided from the clinics. The third endpoint is to be able to use those data for the representation of a digital twin.
The main goal of this project is to analyze data acquired from a network of wearable and nearable devices for the automatic classification of ADL (Activities of Daily Living). The second aim is to provide a score representing the subjects' ability to perform those activities and correlate the measurements with the ICF (International Classification of Functioning, Disability and Health) scores provided from the clinics. The third endpoint is to be able to use those data for the representation of a digital twin.
1. Data Exploration and Analysis: You will explore a multimodal dataset collected with patients with neurological disorders and healthy individuals. Mixed-type variables include demographic features, lab testing values, health conditions observed during consecutive hours, days, or weeks, and multivariate time series extracted from wearable and nearables devices.
2. Methodology Development and Deployment: You will help define what data to use, design the experiments, and develop and validate both the modeling foundation and implementation pipeline for the automatic classification of ADL and the representation of the Digital Twin.
3. Presentation and Documentation: You will prepare a written report highlighting the background and the state of the art together with the implemented novelty of your work. Methodology, results, and discussion will be reported, and the code will be documented. If you have good results, you will have the opportunity to prepare a high-quality manuscript for publication.
1. Data Exploration and Analysis: You will explore a multimodal dataset collected with patients with neurological disorders and healthy individuals. Mixed-type variables include demographic features, lab testing values, health conditions observed during consecutive hours, days, or weeks, and multivariate time series extracted from wearable and nearables devices. 2. Methodology Development and Deployment: You will help define what data to use, design the experiments, and develop and validate both the modeling foundation and implementation pipeline for the automatic classification of ADL and the representation of the Digital Twin. 3. Presentation and Documentation: You will prepare a written report highlighting the background and the state of the art together with the implemented novelty of your work. Methodology, results, and discussion will be reported, and the code will be documented. If you have good results, you will have the opportunity to prepare a high-quality manuscript for publication.
Thesis-proposal-Lifelogging-SCAI-Lab.docx
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