# PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables
## Related Work
### e-Health Platforms  
LifeGuard is an important and well-known e-health monitoring system whose principal element is a wearable monitor and the Crew Physiological Observation Device (CPOD). This system has the ability to continuously monitor and report blood pressure, heart and respiration rates, oxygen saturation, and temperature, as well as body position and electrocardiogram data. This system uses Bluetooth to stream the data to a fixed base station. LifeGuard employs a buzzer to alert the user if a sensor records parameters outside the preset limits or if battery power is low [9]. CodeBlue is another hardware and software platform used to monitor and report physiological parameters (blood oxygen, heart rate, electrocardiogram and electromyography) [10]. This platform monitors and transmits patient blood oxygen and heart rate, as well as electrocardiogram and electromyography data. CodeBlue implements a wireless sensor network with a multi-hop routing protocol. This system can locate the patient indoor location using a RF-based localization system. A computer is used to display all information [11]. CodeBlue has a variety of characteristics [12]:  
1. Discover and naming: communication pathways between sensors and receiving devices are established;
2. Robust routing: a sensor node reports its data to multiple receiving nodes, thus providing system redundancy;Prioritization of critical data: critical and time sensitive data have priority over other control traffic;
* Tracking device location: radio frequency (RF) signals are used to track patient and rescue device locations. Smartvest is comprised of an array of wearable sensors that monitor blood pressure, electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), temperature, respiration rate, skin response, oxygen saturation and heart rate. The measurements and alarms are sent to a remote monitoring station through a wireless sensor network and all information is displayed at a remote monitoring station [13]. Human++ is a research program whose goal is to develop diverse health applications. A prototype of this program has been created to monitor and visualize electrocardiogram, electroencephalogram and electromyography signals. This prototype uses ultra-wide band to send all information to a computer and personal digital assistant (PDA) device [14]. MEDiSN is a platform that consists of a wireless sensor network with a multi-hop wireless backbone that transmits measurements employing a 128-bit AES encryption scheme to the principal server. This platform monitors heart rate, temperature, blood oxygen, respiration rate, galvanic resistance and electrocardiogram [15]. Signal Processing in a Node Environment (SPINE) is a framework for body sensor network (BSN) applications [16]. The principal structure of this framework employs a coordinator and sensors to monitor patient health [17]. Several methods to detect the users' physiological state are compatible with the SPINE Project. [PERSON] and [PERSON] proposed a basic physiological response using an ECG signal. This project has an algorithm to search for the presence of typical cardiac defense responses inside the generated ECG signal [18]. SPINE2, an evolved version of SPINE, is a framework that offers a task-oriented model to program network sensor nodes. All nodes in the WSN can be dynamically created, discovered and activated by the coordinator on each sensor node [19]. Actually, SPINE2 supports two software sensor platforms: TinyOS2.1 and Texas Instruments Z-Stack. This framework has attained a high independence and programming abstraction level [20]. Researchers at the University of Calabria have developed technologies based on photoplethysmographic (PPG) and ECG signals to estimate patient blood pressure. These technologies help prevent or improve the effects of cardiac diseases caused by blood pressure [21]. The Fall-Mobile Guard project is a real-time fall detection system. This project implements a tri-axial accelerometer to detect falls and determine their severity [22].