## 5 Applications of BSNs
### State-of-the-Art Research on BSN Applications
#### 5.1.1 Medical Field
The application achieves the prediction of pulmonary edema and prevents lung disease from becoming worse. Nowadays, mobile communication devices such as mobile phones and tablet computers are playing more and more important roles in people's daily lives. Some studies have introduced mobile devices into the medical field and realized real-time monitoring of patients with the help of the embedded sensors, data processing and wireless communication modules of mobile devices. This kind of technique is named mobile health (m-health) which is a new direction of BSNs in the medical field in recent years. Because of the adoption of ready-made mobile devices, m-health techniques do not need to develop additional dedicated devices, which effectively reduces system costs and increases wearing convenience. In some m-health applications, mobile phones are deployed as data aggregators or intermediate servers for lightweight processing. In Reference [136], researchers collect physiological signals from ECG sensors, accelerometers and temperature sensors by an Android smartphone, which works as an aggregator. The signals are shown on the screen of smartphone in the form of either graphical or text notations. At the same time, data streams are transmitted to a central server with Bluetooth for storage and analysis. Reference [137] presents a preventive healthcare system called myHealthAssistant, which also uses a mobile phone as an aggregator. The presented system can capture heart rates and calorie expenditures by accelerometers and heart rate sensors, helping to control daily activities as well as specific gym exercises. Moreover, the system can record people's daily activities into a SQLite database and generate a fitness diary. In Reference [138], researchers present a system called Intelligent Mobile Health Monitoring System (IMHMS), which can provide medical feedback through mobile devices based on biomedical and environmental data. The system collects temperature, blood pressure, glucose and other signals with a variety of sensors. All the signals are transmitted to a mobile device which is in charge of sending data streams to a medical server. After that, a patient's health status is predicted intelligently based on data analysis and then transmitted back to the patients by a medical server. Finally patients can take necessary actions depending on the feedback. In some other researches, mobile phones can be used as a platform which concentrates sensors and processor. In other words, researchers capture signals by sensors in the mobile phone and carry out signal processing with the processor of the mobile phone. Reference [139] presents a method for accurate activity recognition that only uses one phone with an accelerometer in it. Different from other activity recognition methods, the phone-based method removes additional accelerometers deployed on different locations and avoids analyzing data by a personal computer, which is convenient for users. Previous phone-based methods assumed that the acceleration signals are collected from a known fixed location and orientation. However, varying the location and orientation can affect the recognition effect in a real world situation. For dealing with the issue, a project-based method for device coordinate system estimation is proposed. Researchers make a detailed comparison of experiments with different device locations, and determine the best location of a mobile phone, which can achieve a recognition accuracy rate of about 90%.