Behavior Analysis through Multimodal Sensing for Care of Parkinson's and Alzheimer's Patients
| Autor: | Federico Alvarez, Dario Dotti, V. Solachidis, Mirela Popa, Marcos Quintana, Gustavo Hernandez-Penaloza, Stylianos Asteriadis, Petros Daras, Thomas Theodoridis, Nicholas Vretos, Alberto Belmonte-Hernandez |
|---|---|
| Přispěvatelé: | DKE Scientific staff, RS: FSE DACS |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
IoT
Multimodal fusion Remote patient monitoring Computer science PREDICTION Internet of Things Behavioural sciences Context (language use) 02 engineering and technology depth cues DISEASE Quality of life (healthcare) Biomedical imaging Parkinsons disease Human–computer interaction Health care 0202 electrical engineering electronic engineering information engineering Media Technology multimedia in healthcare Patient monitoring sensor fusion multimedia Sensors business.industry RECOGNITION healthcare 020206 networking & telecommunications Alzheimer's disease Sensor fusion Wireless sensor networks 3. Good health Computer Science Applications Behavioral sciences sensing in healthcare Alzheimers disease Hardware and Architecture Calibration Signal Processing Feature extraction 020201 artificial intelligence & image processing business Wireless sensor network GAIT Software |
| Zdroj: | Ieee Multimedia, 25(1), 14-25. IEEE Computer Society IEEE MultiMedia |
| ISSN: | 1070-986X |
| Popis: | 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 toextract relevant features, analyze data, and provide useful recommendations. The system gathers signalsfrom diverse sources in health monitoringenvironments, understands the user behavior and context, and triggers proper actions for improving thepatient’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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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