Enabling Person-Centric Healthcare Using Ambient Assistive Technology

Sensor Datasets for Human Daily Safety and Well-being.- Habitpad: A Habit-Change Person-Centric Healthcare Mobile Application with Machine Leaning and Gamification Features for Obesity.- Human centered Mathematics: a framework for medical applications based on Extended Reality and Artificial Intelligence.- Attentive Vision-Based Model for Sarcopenia Screening by Automating Timed Up-and-Go (TUG) Test.- AAL with Deep Learning to classify the Diseases remotely from the image data.- Heart Failure Prediction Using Radial Basis with Metaheuristic Optimization.- Healthcare Management And Prediction Of Future Illness Through Autonomous Intelligent Advisory System Using Aat Computational Framework.- ResNet-50-CNN and LSTM based Arrhythmia detection model based on ECG dataset.- A Review of Brain-Computer Interface (BCI) System: Advancement and Applications.- Optimized TSA ResNet architecture with TSH -discriminatory features for kidney stone classification from QUS Images.- Ambient Healthcare: A new Paradigm in Medical Zone.- Illuminating Unexplored Corners in Healthcare Space using Ambience Intelligence.- Depression Assessment in Youths using an Enhanced Deep Learning Approach.- Telemedicine Enabled Remote Digital Healthcare System.

Paolo Barsocchi is a senior researcher at the Information Science and Technologies Institute of the National Research Council in Pisa, Italy. In 2008, he was a visiting researcher at the Universitat Autònoma de Barcelona, Spain. Since 2017, he has been the Head of the Wireless Networks Research Laboratory. He is included in the World's Top 2% Scientists according to the Stanford University List in 2020 and 2021. His research interests are in the areas of the Internet of things, wireless sensor networks, cyber-physical systems, machine learning and data analysis techniques, smart environments, ambient assisted living, activity recognition, and indoor localization. He has been nominated as a regional competence reference person for advanced manufacturing solutions in Industry 4.0 in 2017, and as a contact person in the Cluster-PON call in 2017 for the CNR Department DIITET. He has been (and currently is) involved in several European projects, national and regional projects, in the following listed. The overall amount of attracted and managed funds both at European and national level is about EUR4M.

P Naga Srinivasu is an associate professor in the computer science and engineering department at Prasad V. Potluri Siddhartha Institute of Technology, India. He obtained his Bachelor's degree in computer science engineering from SSIET, JNTU Kakinada (2011), and a Masters's degree in Computer Science Technology from GITAM University, Visakhapatnam (2013). He was awarded a doctoral degree by GITAM university for his thesis on Automatic Segmentation Methods for Volumetric Estimate of damaged Areas in Astrocytoma instances Identified from the 2D Brain MR Imaging. His fields of study include biomedical imaging, soft computing, explainable AI, and healthcare informatics. He has published numerous publications in reputed peer-reviewed journals and has edited book volumes with various publishers like Springer, Elsevier, IGI Global, and Bentham Science. He was an active