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

Federico Alvarez*, Mirela Popa, Vassilios Solachidis, Gustavo Hernandez-Penaloza, Alberto Belmonte-Hernandez, Stylianos Asteriadis, Nicholas Vretos, Marcos Quintana, Thomas Theodoridis, Dario Dotti, Petros Daras

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

21 Citations (Web of Science)


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 Parkinson's and Alzheimer's 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 patient's 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.
Original languageEnglish
Pages (from-to)14-25
Number of pages12
JournalIeee Multimedia
Issue number1
Publication statusPublished - 1 Jan 2018


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