TY - JOUR
T1 - Behavior Analysis through Multimodal Sensing for Care of Parkinson's and Alzheimer's Patients
AU - Alvarez, Federico
AU - Popa, Mirela
AU - Solachidis, Vassilios
AU - Hernandez-Penaloza, Gustavo
AU - Belmonte-Hernandez, Alberto
AU - Asteriadis, Stylianos
AU - Vretos, Nicholas
AU - Quintana, Marcos
AU - Theodoridis, Thomas
AU - Dotti, Dario
AU - Daras, Petros
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - GAIT
KW - RECOGNITION
KW - PREDICTION
KW - DISEASE
U2 - 10.1109/MMUL.2018.011921232
DO - 10.1109/MMUL.2018.011921232
M3 - Article
SN - 1070-986X
VL - 25
SP - 14
EP - 25
JO - Ieee Multimedia
JF - Ieee Multimedia
IS - 1
ER -