Mirela Popa

Unsupervised discovery of normal and abnormal activity patterns in indoor and outdoor environments

In this paper we propose an adaptive system for monitoring indoor and outdoor environments using movement patterns. Our system is able to discover normal and abnormal activity patterns in absence of any prior knowledge. We employ several feature descriptors, by extracting both spatial and temporal cues from trajectories over a spatial grid. Moreover, we improve the initial feature vectors by applying sparse autoencoders, which help at obtaining optimized and compact representations and improved accuracy. Next, activity models are learnt in an unsupervised manner using clustering techniques. The experiments are performed on both indoor and outdoor datasets. The obtained results prove the suitability of the proposed system, achieving an accuracy of over 98% in classifying normal vs. abnormal activity patterns for both scenarios. Furthermore, a semantic interpretation of the most important regions of the scene is obtained without the need of human labels, highlighting the flexibility of our method.

A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

To identify, predict and validate distinct glycaemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient‐centred care.

Multimodal monitoring of Parkinson's and Alzheimer's patients using the ICT4LIFE platform

The analysis of multimodal data collected by innovative imaging sensors, Internet of Things (IoT) devices and user interactions, can provide smart and automatic distant monitoring of patients and reveal valuable insights for early detection and/or prevention of events related to their health situation. In this paper, we present a platform called ICT4LIFE which starting from low-level data capturing and performing multimodal fusion to extract relevant features, can perform high-level reasoning to provide relevant data on monitoring and evolution of the patient, and trigger proper actions for improving the quality of life of the patient.

A Novel Approach for Movement Evolution Tracking in Parkinson's Disease using Data Analysis and Fuzzy Logic

In this paper, a novel approach for the analysis of the movement evolution in patients with Parkinsons disease is presented. The system offers the capabilities of detecting significant degradations in the motor-skills of the patients according to the physiotherapy evaluations, where seven items are measured, including: posture, balance, walking, postural changes, involuntary movements, movement coordination and rigidity. To assess their evolution, two modules are employed: a data analysis module, which uses a clustering algorithm to distribute patients according to their skills and analyses their evolution based on the last three evaluations, and a Decision Support Tool based on a Fuzzy-Logic system, which measures the state of the patient according to the results from the mentioned data analysis module and generates a report per patient including his/her state at each item as well as recommendations considering convenient exercises to be practiced. Thus, the system provides meaningful information to physiotherapists, to support them in the decision-making process.

Behavior Analysis through Multimodal Sensing for Care of Parkinson’s and Alzheimer’s Patients

The analysis of multimodal data collected by innovative imaging sensors, Internet of Things devices, and user interactions can provide smart and automatic distant monitoring of Parkinsons and Alzheimers patients and reveal valuable insights for early detection and/or prevention of events related to their health. This article describes a novel system that involves data capturing and multimodal fusion to extract relevant features, analyze data, and provide useful recommendations. The system gathers signals from diverse sources in health monitoring environments, understands the user behavior and context, and triggers proper actions for improving the patients quality of life. The system offers a multimodal, multi-patient, versatile approach not present in current developments. It also offers comparable or improved results for detection of abnormal behavior in daily motion. The system was implemented and tested during 10 weeks in real environments involving 18 patients.

ICT Services for Life Improvement for the Elderly.

Integrated care ICT Platform to support patients, care-givers and health/social professionals in the care of dementia and Parkinsons disease with training, empowerment, sensor-based data analysis and cooperation services based on user-friendly interfaces.