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Key Topics
Prepare a portfolio on Mobile health, areas in which sensor network systems are being commercially explored include: Aged Care (e.g. assisted independent living); Chronic disease prevention and management; Acute Care (e.g. women wellness after cancer); and Mental Wellbeing (e.g. mood shifting using music).
Introduction
The growth of human population increases gradually so the medical expenses also get increased. To solve this issue mobile IOT health care is introduced. This system helps the person to identify the health condition of aged people and to take care of themselves. It helps to check the health condition of the person frequently. In day to day life, it is very difficult to take care of the aged people by an individual. Monitoring the health condition of the aged people is a challenging task for the medical system. This helps to prevent the health condition of the aged people from the serious condition (Farah Nasri, Tunisia 2017). Research has been identified that when a person works more, it mostly leads to fatigue. So if this problem is continued without any treatment then more complications like heart disease occur. Nowadays most of the people lose their life due to increase in heartbeat rate. They should know when the heartbeat is raised and they should take remedies at the emergency situation. Majority of them remains inside the home without anyone’s care. If the person is taken care by the other person then there will not be any problem in saving one’s life. But if there is nobody to take care of them then mobile IOT helps the person to know their body condition by using some of the wearable devices and suggest the solution for the problem. Wearable’s like Mi Band is used for sensing the heart rate of the person. Some of the signs are measured such as body temperature, heart rate, blood pressure etc are used for detecting the body conditions of the patients. To solve this issue IOT health care system finds a better way of communicating the health condition to one another by wireless connective devices (Vijayakannan Sermakani, 2014).
IOT is a communication of sensors or physical objects that help to sense, report and control the emergency situation remotely and take necessary decisions to solve the problem. This can be connected via network devices and the embedded system. IOT helps the people to communicate from anywhere, anytime and anyone via an internet connection. The development of IoT is due to the growth of the Smartphones. Since smartphones can perform various tasks IOT is evolved with this greatest feature. For example, when the patients cannot meet the doctor during the vacation, he should wait until the doctor arrive the hospital. In case of any emergency situation, the patient cannot be monitored by the other doctor since the entire details of one patient is known only to the doctor who examine him frequently. So it is difficult to give treatment to the patient in the absence of doctor. But when IOT is evolved it can be done easily by examining the patient’s condition and sending the data to the doctor. Without the physical presence of the doctor, the treatment is given to the patient through the technology. In current technology, the medical devices in the hospital are connected to the doctor and it is accessed through the medical application using the network connection (Nasri, F., Moussa, N., Mtibaa, A., 2014).
IOT is a communication of sensors or physical objects that help to sense, report and control the emergency situation remotely and take necessary decisions to solve the problem. This can be connected via network devices and the embedded system. IOT helps the people to communicate from anywhere, anytime and anyone via an internet connection. The development of IoT is due to the growth of the Smartphones. Since smartphones can perform various tasks IOT is evolved with this greatest feature. For example, when the patients cannot meet the doctor during the vacation, he should wait until the doctor arrive the hospital. In case of any emergency situation, the patient cannot be monitored by the other doctor since the entire details of one patient is known only to the doctor who examine him frequently. So it is difficult to give treatment to the patient in the absence of doctor. But when IOT is evolved it can be done easily by examining the patient’s condition and sending the data to the doctor. Without the physical presence of the doctor, the treatment is given to the patient through the technology. In current technology, the medical devices in the hospital are connected to the doctor and it is accessed through the medical application using the network connection (Nasri, F., Moussa, N., Mtibaa, A., 2014).
Problem Defination
The patient’s health is monitored by using the sensors which helps to sense the health condition of them. So there should not be any problem with the sensors and the mobile devices which is used for the communication. If there is any problem with the sensors then the patient’s conditions cannot be monitored. Then it results in the serious condition of the patients. The main challenge in IOT is frequent monitoring of sensor and mobile devices with the normal internet connection. The IOT devices should be monitored to avoid any critical situations of the patients thereby to increase the use of IoT device in the medical system (L. Adori, A. Iera, and G. Morabito, 2010).
Architecture of Mobile Healthcare System
The IOT devices such as sensor consist of connectivity such as Bluetooth or any wireless connection which helps to communicate the conditions of the patients. IOT device sense the patient's health and the readings or measurements are taken. After measuring the data it is communicated to the smart app where the doctor and patient can know the condition of the patient. The data is stored in the database for the future reference of the patient health history. With the virtual presence of the doctor, the treatment can be given to the patients (Nasri, F.; Moussa, N.; Mtibaa, A., 2013).
Sensor Description
The device used to monitor the fitness of the person is Mi Band. It is a wearable device which can be perfectly wearer to the wrist. It helps to track the sleep, monitors the heart rate and calculates the difference between the various sleep modes. The patient has to wear the Mi Band throughout the monitoring. The sensor present in the devices detects the motion of the patient. It consists of the accelerometer to track every moment of the patient during the sleeping. To measure the rotation gyroscope is used (Al-Fuqaha, A., Guizani, M., Mohammadi, M., and et al. (2015). To know the altitude of the patient’s location, it is sensed by using an altimeter. By sensing the motion of the patient the heart rate is measured and the data is collected. By analyzing the data, the result is shown whether the patient had a deep or quality sleep or any problem occurs during sleeping. Patients are monitored during sleep. The sleep feature of Mi Band doesn’t fail until the band is removed from the wrist.
The sleep tracking feature in Mi band acquires most accurate data. The reason for getting accurate data is using proximity sensors and Actigraphy.
PROXIMITY SENSORS:
Proximity sensors are used for identifying the presence of object nearby the wearable devices. If an object is available within the range of proximity sensor it sends the infrared beam to the object and monitors the reflection from the object. When the reflection is received then the sensor identifies that the object is present within the range. Proximity sensors are used in iPhones to turn off the display screen when the call is connected. It detects the presence of the object. The same way Mi Band also has a touch capacitive proximity sensor which can detect the presence of skin (Simran Singh, 2013).
There are different types of proximity sensors. They are an inductive proximity sensor, capacitive proximity sensor and magnetic proximity sensor. In Mi Band, touch capacitive proximity sensors are used.
The sleep tracking feature in Mi band acquires most accurate data. The reason for getting accurate data is using proximity sensors and Actigraphy.
PROXIMITY SENSORS:
Proximity sensors are used for identifying the presence of object nearby the wearable devices. If an object is available within the range of proximity sensor it sends the infrared beam to the object and monitors the reflection from the object. When the reflection is received then the sensor identifies that the object is present within the range. Proximity sensors are used in iPhones to turn off the display screen when the call is connected. It detects the presence of the object. The same way Mi Band also has a touch capacitive proximity sensor which can detect the presence of skin (Simran Singh, 2013).
There are different types of proximity sensors. They are an inductive proximity sensor, capacitive proximity sensor and magnetic proximity sensor. In Mi Band, touch capacitive proximity sensors are used.
CAPACITIVE PROXIMITY SENSORS:
A capacitive proximity sensor is used for sensing the metal object that is inside or near the range of the sensing area from any direction and also to detect resins, liquids, powders etc. It consists of oscillator, coil, detector, cable, output circuit and connector. The oscillator is used for generating the sine ways in a fixed range of frequency (Simran Singh, 2013). The coil induces an electromagnetic field. When the electromagnetic field is interrupted by an object an oscillator is reduced which is proportional to the size and the distance of the object from the coil. It also senses under various factors like temperature, sensing object and surrounding objects.
A capacitive proximity sensor is used for sensing the metal object that is inside or near the range of the sensing area from any direction and also to detect resins, liquids, powders etc. It consists of oscillator, coil, detector, cable, output circuit and connector. The oscillator is used for generating the sine ways in a fixed range of frequency (Simran Singh, 2013). The coil induces an electromagnetic field. When the electromagnetic field is interrupted by an object an oscillator is reduced which is proportional to the size and the distance of the object from the coil. It also senses under various factors like temperature, sensing object and surrounding objects.
| MATERIAL | DIELECTRIC CONSTANT |
| Glass | 5 |
| Mica | 6 |
| Paper | 2.3 |
| Wood | 2.7 |
| Petroleum | 2.2 |
| Water | 80 |
| Celluloid | 3 |
| Teflon | 2 |
Table 1: Capacitive proximity sensor measurement on material
ACTIGRAPHY:
Actigraphy is a technique which is used by smartwatches, fitness trackers to count the sleep time of the person such as deep sleep and light sleep. It is used to monitor the sleep or wake patterns of the patients. The advantage of using this actigraphy is it can continuously record the data for 24 hours or a day or a week. The actimetric sensor is used for sensing the movement of the body to determine the type of sleep (Zhe Yang, Qihao Zhou, Lei Lei, Kan Zheng, 2016).
Actigraphs are generally placed on the wrist of the person to record the data of the patient. The collected data are stored in a computer for display and analyze the data in the form of a graph. Nowadays actigraphs are used to detect the motion of the person and have enough storage to record the data. The data collected is generated in the form of a graph with the help of a Mi-Fit app or any other third party services.
Figure 2: Patient record
ACTIGRAPHY:
Actigraphy is a technique which is used by smartwatches, fitness trackers to count the sleep time of the person such as deep sleep and light sleep. It is used to monitor the sleep or wake patterns of the patients. The advantage of using this actigraphy is it can continuously record the data for 24 hours or a day or a week. The actimetric sensor is used for sensing the movement of the body to determine the type of sleep (Zhe Yang, Qihao Zhou, Lei Lei, Kan Zheng, 2016).
Actigraphs are generally placed on the wrist of the person to record the data of the patient. The collected data are stored in a computer for display and analyze the data in the form of a graph. Nowadays actigraphs are used to detect the motion of the person and have enough storage to record the data. The data collected is generated in the form of a graph with the help of a Mi-Fit app or any other third party services.
Figure 2: Patient record
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Mi Band has different versions of Mi Band 1, Mi Band 2 etc. Mi Band 2 is better than the Mi Band 2 since it provides an accurate value of the fitness than the Mi Band 1. It looks simple and durable which is very comfortable to wear and corrosion resistant. But it is not water resistant so by wearing this band we cannot swim. There are many colours in this band like orange, green and blue. The wristband is not expensive so it can be bought through online at low cost (Quora, 2016).
It does sensing the heart rate, battery life, distance, time and calories. However, there is no touchscreen there is only touch sensitive button which helps to control the screen. This button helps to provide a faster way of communication. So it is very easy to use by self.
The Xiaomi Mi band 2 is a device which is used for tracking fitness, sleep rate and heart rate. This fitness tracker gives an alert message when the patient has to do something based on their physical fitness. Suppose if the person is sitting for a long time, it gives an alert to go for a walk for 10 minutes. Likewise, IoT device monitors the health condition of the person and provides the instruction based on that (Inkin, 2018).
The patient has to wear the Mi Band all time. So the sensors in the band will do their job perfectly. The sleep tracker senses us and it knows when we fall asleep and awake so it detects that our sleep is deep sleep or light sleep. The data is recorded using the app Mi-Fit app or Google Fit app it is based on the usage of the Smartphones. Using Mi-Fit app we can know how our sleeping was whether it is deep sleep or light sleep and for how many hours we slept for deep and light sleep. If we were sleeping for a long time then it gives an alarm sound to wake up. The optical heart rate monitor is found on the bottom of the band which helps to track the heart rate, heart beat per minute during hiking, sprinting or jogging. But for doing all those activities the battery might not be there. So it should be charged when the battery is down (Inkin, 2018).
Mi-Fit App is used to know the performance and the synchronizing problems. It is installed in the Smartphone’s and it is used by the internet connection. The Mi Band and the app is connected via Bluetooth connection. When the device and the app are connected the readings are communicated to it. So when you open the Mi-Fit app dashboard is opened. There you can find the type of sleep, heart rate measurement etc. The readings are displayed in the form of a graph which shows the difference between the sleep, activity and walks. To know the daily activities go into behaviour tag menu. There will a description of daily activities like brushing, playing, driving etc (Quora, 2016).
There is a special feature in this Mi Band 2 to give a suggestion based on the heartbeat rate. What should be done when the heart rate is low and what to be done when the heart rate is high? So for aged people, this will be more comfortable to manage themselves without any others helps. The battery life of Mi Band 2 last up to 20 days. So after that is should be charged again (Quora, 2016).
Fog Computing Analysis
Fog computing platform brings the power to the user at a reduced utilization cost because of the region of edges of the sensor and the data which are collected are pushed to the cloud server. The collected data is transmitted by the smart hub devices which send the information that is necessary and needs to be processed in and out scenario. The medical data EMR and real-time data of the patients could be processed in the remote areas that are powered. The cloud server processed intelligent algorithm that is implemented in the smart hub device. For example, if a pacemaker sensor is attached to the patients in a hospital the doctor can remotely monitor the patient by transferring the data of the patients from the remote region to the cloud servers and again that data could be accessed from the cloud server to the remote region. All the computing is processing through a web API. This web API is used to act as the gateway for the user to access the data. The data is transmitted in a lightweight format called JSON data. The data which is recorded in the smart device is trained in the way to give the exact thing we need by implanting of the machine learning and deep learning of the hardware. In healthcare the machine interacts with each other to give the required output that we require in order to achieve such a state the machine learning is trained for a particular pattern. The pattern will hold the exact response for the client and the insights that are to be generated to the end user. Even the sensor should be monitored because each sensor might give false information at a peak working of the sensor node. The data collected by the sensors are analyzed and checked for pattern matching in the fog layer. After completing the pattern matching process the analyzed data is sent to the Google Fit API. The data is again analyzed in the data analysis layer to generate the report according to the condition of the patients. (Marc-Florian Uth, Jochim Koch, Frank Sattler,2016).
Now a day’s every thing are connected to the internet so they require a high bandwidth for the computing to happen smoothly. These computing powers should be effective and should have any loss of the data. Fog computing analysis generally focuses on end user and sensors which are connected to the computing platform. They are located in the remote area. By this way, better QOS can be achieved because of the efficient utilization of the resources. Health care is an evolutionary process. The system keeps on learning like the humans but in order to give the insight to the people at the required time is the greatest challenge if the developed system could not meet the standards and the expectation of the people requirement. There is no use of the data gathered and sending to the end user. As the power of the cloud computing works through around the globe the computing power can be efficiently utilized for the people who seriously need the computing power (IERC, 2011).
Now a day’s every thing are connected to the internet so they require a high bandwidth for the computing to happen smoothly. These computing powers should be effective and should have any loss of the data. Fog computing analysis generally focuses on end user and sensors which are connected to the computing platform. They are located in the remote area. By this way, better QOS can be achieved because of the efficient utilization of the resources. Health care is an evolutionary process. The system keeps on learning like the humans but in order to give the insight to the people at the required time is the greatest challenge if the developed system could not meet the standards and the expectation of the people requirement. There is no use of the data gathered and sending to the end user. As the power of the cloud computing works through around the globe the computing power can be efficiently utilized for the people who seriously need the computing power (IERC, 2011).
Security Analysis
Mi Band is visible to the mobile devices via Bluetooth connection. When the Bluetooth it is on then the device is visible. Otherwise, it is not visible to others. It supports Bluetooth Low Energy where the device repeatedly generates different MAC address on every new connection and actual address is not shown so this device is not traceable. Mi Band does not have a screen to display the users who are trying to connect with it. It just vibrates when a user tries to pair with the device (D. Christin, A. Reinhardt, P. Mogre and R. Steinmed, 2009). It is easy for the third party member to make the wristband vibrate. With the weak authentication, the mi band can be tracked. The data is accessed by the hacker and he can make any changes like changing the alarm setting or factory restoring. So there is a chance for the hacker to access the device if the device is paired with the weak authentication. When creating the password it should be confidential and unique that hackers should not guess or try it. This is the only way to protect the device from the attackers.
Conclusion
The aged people are alone to take care of themselves that is assisted independent living. To take care of them a device called Mi Band is used to monitor their fitness, heart rate, sleep rate and calories. If these factors are monitored then the patients can manage themselves without anybody’s help. It is also possible to access the device by their family members to know their health condition. The basis measures are given by the Mi Band to do what should be done under the critical conditions. So they can take care of their health using the mobile smart application.
References
Farah Nasri, Tunisia (2017) “Abdellatif Mtibaa Smart Mobile Healthcare System based on WBSN and 5G” International Journal of Advanced Computer Science and Applications.
Vijayakannan Sermakani (2014),“Transforming health care through internet of things”, Project Management practitioners conference.
Nasri, F., Moussa, N., Mtibaa, A., (2014),”Intelligent Mobile System for Healthcare Based on WSN and Android”, International Journal of Computer Trends and Technology (IJCTT)
Nasri, F.; Moussa, N.; Mtibaa, A., (2013) "Smart mobile system for health parameters follow ship based on WSN and android," in Computer and Information Technology (WCCIT).
Al-Fuqaha, A., Guizani, M., Mohammadi, M., and et al. (2015), “Internet of things: A survey on enabling technologies, protocols, and applications”. IEEE Communication survey.
Zhe Yang, Qihao Zhou, Lei Lei, Kan Zheng (2016),“An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare”, Journal of Medical Systems.
Miao, F., Cheng, Y., He, Y., and et al. (2015),“A wearable context-aware ECG monitoring system integrated with built-in kinematic sensors of the Smartphone”.
Marc-Florian Uth, Jochim Koch, Frank Sattler (2016), “Body Core Temperature Sensing: Challenges and New Sensor Technologies”. Procedia Engineering.
Nimish sawant (2016), “pebble time and Microsoft band 2 are the most secure wearables: AV Test report”, Retrieved from http://www.firstpost.com/tech/news-analysis/pebble-time-and-microsoft-band-2-are-the-most-secure-wearables-av-test-report-3685725.html [Accessed 19th march 2018]
Simran Singh(2013), ”what are proximity sensor? How they work and types?” Retrieved from http://thegadgetsquare.com/what-are-proximity-sensors-types-and-how-it-works/ [Accessed 19th march 2018]
Quora (2016), “how Mi Band works” Retrieved from https://www.quora.com/How-does-Mi-band-work [Accessed 19th march 2018]
Inkin (2018),”Wearables” Retrieved from https://www.inkin.com/blog/en/All-You-Need--and-Want--To-Know-About-The-Xiaomi-Mi-Band-2 [Accessed 19th march 2018]
D. Christin, A. Reinhardt, P. Mogre and R. Steinmed (2009), “Wireless Sensor Networks and the Internet of Things: Selected Challenges,” in Proceedings of the 8th GI/ITG.
IERC (2011) – European Research Cluster on the Internet of Things, “Internet of Things - Pan European Research and Innovation Vision”.
L. Adori, A. Iera, and G. Morabito (2010), “The Internet of Things: A survey,” in ScienceDirect: Computer Networks.
Vijayakannan Sermakani (2014),“Transforming health care through internet of things”, Project Management practitioners conference.
Nasri, F., Moussa, N., Mtibaa, A., (2014),”Intelligent Mobile System for Healthcare Based on WSN and Android”, International Journal of Computer Trends and Technology (IJCTT)
Nasri, F.; Moussa, N.; Mtibaa, A., (2013) "Smart mobile system for health parameters follow ship based on WSN and android," in Computer and Information Technology (WCCIT).
Al-Fuqaha, A., Guizani, M., Mohammadi, M., and et al. (2015), “Internet of things: A survey on enabling technologies, protocols, and applications”. IEEE Communication survey.
Zhe Yang, Qihao Zhou, Lei Lei, Kan Zheng (2016),“An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare”, Journal of Medical Systems.
Miao, F., Cheng, Y., He, Y., and et al. (2015),“A wearable context-aware ECG monitoring system integrated with built-in kinematic sensors of the Smartphone”.
Marc-Florian Uth, Jochim Koch, Frank Sattler (2016), “Body Core Temperature Sensing: Challenges and New Sensor Technologies”. Procedia Engineering.
Nimish sawant (2016), “pebble time and Microsoft band 2 are the most secure wearables: AV Test report”, Retrieved from http://www.firstpost.com/tech/news-analysis/pebble-time-and-microsoft-band-2-are-the-most-secure-wearables-av-test-report-3685725.html [Accessed 19th march 2018]
Simran Singh(2013), ”what are proximity sensor? How they work and types?” Retrieved from http://thegadgetsquare.com/what-are-proximity-sensors-types-and-how-it-works/ [Accessed 19th march 2018]
Quora (2016), “how Mi Band works” Retrieved from https://www.quora.com/How-does-Mi-band-work [Accessed 19th march 2018]
Inkin (2018),”Wearables” Retrieved from https://www.inkin.com/blog/en/All-You-Need--and-Want--To-Know-About-The-Xiaomi-Mi-Band-2 [Accessed 19th march 2018]
D. Christin, A. Reinhardt, P. Mogre and R. Steinmed (2009), “Wireless Sensor Networks and the Internet of Things: Selected Challenges,” in Proceedings of the 8th GI/ITG.
IERC (2011) – European Research Cluster on the Internet of Things, “Internet of Things - Pan European Research and Innovation Vision”.
L. Adori, A. Iera, and G. Morabito (2010), “The Internet of Things: A survey,” in ScienceDirect: Computer Networks.
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