5. Sensors and Actuators

5.2. Medical Sensors

The Internet of Things can be really beneficial for health care applications. We can use sensors, which can measure and monitor various medical parameters in the human body. These applications can aim at monitoring a patient's health when they are not in hospital or when they are alone. Subsequently, they can provide real time feedback to the doctor, relatives, or the patient. McGrath and Scanaill have described in detail the different sensors that can be worn on the body for monitoring a person's health.

There are many wearable sensing devices available in the market. They are equipped with medical sensors that are capable of measuring different parameters such as the heart rate, pulse, blood pressure, body temperature, respiration rate, and blood glucose levels. These wearables include smart watches, wristbands, monitoring patches, and smart textiles.

Moreover, smart watches and fitness trackers are becoming fairly popular in the market as companies such as Apple, Samsung, and Sony are coming up with very innovative features. For example, a smart watch includes features such as connectivity with a smartphone, sensors such as an accelerometer, and a heart rate monitor (see Figure 4).


Figure 4 

Smart watches and fitness trackers


Another novel IoT device, which has a lot of promise are monitoring patches that are pasted on the skin. Monitoring patches are like tattoos. They are stretchable and disposable and are very cheap. These patches are supposed to be worn by the patient for a few days to monitor a vital health parameter continuously. All the electronic components are embedded in these rubbery structures. They can even transmit the sensed data wirelessly. Just like a tattoo, these patches can be applied on the skin as shown in Figure 5. One of the most common applications of such patches is to monitor blood pressure.


Figure 5 

Embedded skin patches


A very important consideration here is the context. The data collected by the medical sensors must be combined with contextual information such as physical activity. For example, the heart rate depends on the context. It increases when we exercise. In that case, we cannot infer abnormal heart rate. Therefore, we need to combine data from different sensors for making the correct inference.