A smart context-aware mobile monitoring system for heart patients

Context-aware system for cardiac condition monitoring and management: a survey

“…As indicated in table 1, researchers used different parameters for cardiac condition monitoring. Only in (Sannino and De Pietro 2011) that involved cardiologists stated the reasons behind choosing those parameters. Considering the limited battery capacity of mobile devices, and given that patients monitoring involves continuous context acquisition from sensors, a context-aware system for cardiac monitoring should consider minimal number of possible parameters without putting the subject in danger.…”

“…This device could only recognize steps of the subject and cannot show specific activity performed such as walking, running and sitting. There was improvement in (Sannino and De Pietro 2011) as the system is intended to recognize specific activity, however, applying threshold method to detect different activities might impose serious issues, as there are wide-range of physical activities, coupled with the disparity in how a particular activity is to be carried out. To complement this approach, Miao et al (2015) applied machine learning techniques to recognize human activities.…”

“…Using the stored context history and personal profile, the system can detect smoking habit, caffeine intake, alcohol consumption of the user and activities such as stress, anxiety, and hypertension using different parameters from the patient. Sannino and De Pietro (2011), introduced "intelligent mobile system based on rule decision support system for cardiac patients". The system correlates data from the ECG sensor with physical activities such as walking, running and body posture.…”

A Novel O ( n ) Numerical Scheme for ECG Signal Denoising

“…[22]), or for highlighting of correlations between health and a correct lifestyle (e.g. [23]). The ECG biomedical signal (see Figure 1) is composed of weak nonstationary data which are affected by various types of noises: power line interference, baseline drift, electromyography interference and sensor contact noise.…”

A generic model for smart house remote control systems with software and hardware simulators

“…It is a difficult job to join the different platforms and models so that a comprehensive and general model can be developed for remote control of smart digital homes. For example, in [5][6][7][8], some remote monitoring systems are designed and implemented to allow remote access through Internet, while in [9][10][11][12][13][14] some mobile applications are presented for remote access and monitoring systems. None of the systems mentioned earlier follow a comprehensive model so that other possible design and implementation of additional modules can be added to the general system.…”