Paper Info
Title
HeartHealth: A cardiovascular disease home-based rehabilitation system
Abstract
The increasing pressure on medical institutions around the world requires health care professionals to be prescribing home- based exercise rehabilitation treatments to empower patients to self-monitor their rehabilitation journey. Home-based exercise rehabilitation has shown to be highly effective in treating conditions such as Cardiovascular Disease (CVD). However, adherence to home-based exercise rehabilitation remains low. Possible causes for this are that patients are not monitored, they cannot be con- fident that they are performing the exercise correctly or accurately and they receive no feedback. This paper proposes HeartHealth, a novel patient-centric gamified exercise rehabilitation platform that can help address the issue of adherence to these programmes. The key functionality is the ability to record the patient movements and compare them against the exercises that have been pre- scribed in order to return feedback to the patient and to the health care professional, as well. In order to synthesize a compact fully operational system able to work in real life scenarios, tools and services from FI-PPP projects, FIWARE 1 and FI-STAR 2, were exploited and a new FI-STAR component, Motion Evaluation Specific Enabler (SE), was designed and developed. The HeartHealth system brings together real-time cloud-based motion evaluation coupled with accurate low-cost motion capture, a personalised ex- ercise rehabilitation programme and an intuitive and fun serious game interface, designed specifically with a Cardiac Rehabilitation population in mind.
Year
DOI
Venue
2015
10.1016/j.procs.2015.08.352
Procedia Computer Science
Keywords
Field
DocType
home-based rehabilitation platform,rehabilitation,CVD patients,CVD exercises,motion capture,serious games ,
Rehabilitation,Population,Motion capture,Computer science,Operational system,Artificial intelligence,Physical medicine and rehabilitation,Health care,Enabling,Visualization,Simulation,Machine learning,Cloud computing
Conference
Volume
ISSN
Citations 
63
1877-0509
4
PageRank 
References 
Authors
0.39
4
7
Name
Order
Citations
PageRank
Anargyros Chatzitofis1648.74
David S. Monaghan28511.48
Edmond Mitchell3454.60
Freddie Honohan440.73
Dimitrios Zarpalas530333.96
Noel E. O'Connor62137223.20
Petros Daras71129131.72