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Master Thesis: Data Analysis of Wearable and Nearable Sensors Data for Classification of Activities of Daily Living
This project aims to develop a novel algorithm for tracking a person's health condition changes using daily life wearable sensor data, biosignals, and information from nearable sensors. With the Life-long-logging system, we want to provide meaningful data for medical staff and directly engage patients and their caregivers.
Keywords: Data analysis, Machine Learning, Wearable Sensors
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 with the ICF (International Classification of Functioning, Disability, and Health) scores provided by the clinics. The third endpoint is to find correlations between the day and night sensor data.
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 with the ICF (International Classification of Functioning, Disability, and Health) scores provided by the clinics. The third endpoint is to find correlations between the day and night sensor data.
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, health conditions observed during consecutive days, and multivariate time series extracted from wearable and nearables sensors.
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, correlation with ICF scoring and correlation among day and night sensors data.
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.
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, health conditions observed during consecutive days, and multivariate time series extracted from wearable and nearables sensors. 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, correlation with ICF scoring and correlation among day and night sensors data. 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.