PRE2017 4 Groep8: Difference between revisions

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URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6521045&isnumber=6521004 </ref>
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6521045&isnumber=6521004 </ref>


Lastly, the state of the art of thermostats and of measurements of oxygen.
Lastly, the state of the art of thermostats and of measurements of oxygen. (state of the environment)
* Neofox oxygen sensors: precise oxygen sensors for both liquids and gasses. <ref> Neofox oxygen</ref>
* Neofox oxygen sensors: precise oxygen sensors for both liquids and gasses. <ref> Neofox oxygen</ref>
* PhO2: Smartphone based Blood Oxygen Level Measurement Systems using Near-IR and RED Wave-guided Light <ref> Bui N., Nguyen A., Nguyen P., Truong H., Ashok A., Dinh T., Deterding R., Vu T. (2017) PhO2: Smartphone based Blood Oxygen Level Measurement Systems using Near-IR and RED Wave-guided Light. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. [online] Available at: http://mnslab.org/tamvu/paper/2017%20PhO2%20Nam%20Bui.pdf </ref>
* PhO2: Smartphone based Blood Oxygen Level Measurement Systems using Near-IR and RED Wave-guided Light <ref> Bui N., Nguyen A., Nguyen P., Truong H., Ashok A., Dinh T., Deterding R., Vu T. (2017) PhO2: Smartphone based Blood Oxygen Level Measurement Systems using Near-IR and RED Wave-guided Light. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. [online] Available at: http://mnslab.org/tamvu/paper/2017%20PhO2%20Nam%20Bui.pdf </ref>
 
* Smart device for gas range <ref> https://patents.google.com/patent/US9752783B2/en </ref>
 
 


==Summaries of literature==
==Summaries of literature==

Revision as of 21:19, 26 April 2018

Group members

  • Isabelle Cooijmans | 1014516 | i.h.m.cooijmans@student.tue.nl
  • Ramon Hameleers | 0998964 | r.j.e.hameleers@student.tue.nl
  • Angelique Husson | 0956648 | a.c.e.husson@student.tue.nl
  • Marrit Jen Hong Li | 0963568 | m.i.jen.hong.li@student.tue.nl
  • Dana de Vreede | 1020836 | d.d.vreede@student.tue.nl

Problem statement

More elderly have to live at home because there is not enough personal staff in elderly homes and the number of elderly grows every year. Moreover, there is a trend of elderly who want to live independently. However not all elderly can live independently, they depend on home care, but home care cannot keep an eye on the elderly all the time.

Objectives

Our objectives are to help the elderly to live more independently at home and to give the home care a better overview of what is going on in the house of the elderly. Moreover, to give the family more comfort about how the elderly are going.

Users

First, the primary users, which are:

  • the elderly
  • the home care

Secondly, the secondary users, which are:

  • the family
  • the friends
  • the government

thirdly, the tertiary users, which are:

  • the technicians

Requirements

Requirements of primary users:

  • The elderly need a system that helps them to be more independent.
  • The elderly need to be reminded to take their medicine.
  • The elderly need to be reminded to eat when they skipped a meal.
  • The elderly need to be reminded to water the plants and keep busy.
  • The home care needs to get a lower work pressure and a better overview of the state of the house of elderly.

Requirements of the secondary users:

  • The secondary users need their elderly to be independent.

Requirements of the tertiary users:

  • The tertiary users need a system that is easy to install and easy to maintain.
  • The tertiary users need a system that gives a notification in case of an error or when it needs maintenance.

Approach, milestones and deliverables

Our approach will be based on a divide and conquer strategy. We will divide the big problem into subproblems, which we will solve independently and then put them together to get the big solution. The problem is to make some kind of smart house for a home living (forgetful) elderly, which need home care. Elderly live (better?) when they take care of things and keep busy. So having plans and hence responsibility to take care of the plans. Furthermore, most elderly have to take medication. However, they can forget to take those medications in time. Moreover, some elderly sometimes forget whether they have already eaten a meal or not. Furthermore, the temperature and oxygen levels are important to keep an eye on, whether the elderly take good care of themselves. So some milestones are:

  • A system that recognizes that plans need watering and a system to notify the user that the plant needs care.
  • A system that registers whether the user has eaten a meal and notify the user if the skipped a meal.
  • A system that measures the temperature in the house and the oxygen levels.
  • A system that helps the user to be reminded that medications need to be taken.
  • A system that will keep the home care up to date about what is happening in the house.

For these subsystems, we need to build prototypes and test these. But for this, we first need to investigate the state of the art. The deliverables of this project are the systems who should work together as a whole and the prototypes of these systems and hence a prototype of the whole.

Literature study

The literature study can be divided into a few subjects.

First of all a broad study of smart homes.

  • Architecture of small assistance device for elderly [1]
  • Monotoring and assistance system [2]
  • Source connecting people with dementia to smart homes [3]
  • [4]
  • Smart home for elder care using wireless sensors [5]
  • Wireless sensing network for monitoring elderly [6]
  • Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology [7]
  • Matching technologies of home automation, robotics, assistance, geriatric telecare and telemedicine [8]

Secondly the state of the art of medication reminders.

  • (fill in source)
  • Smart home medication reminder system [9]
  • Smart medicine Cabinet [10]
  • [11]
  • Medicine Reminder and Monitoring System for Secure Health Using IOT [12]
  • Message ina bottle [13]


Thirdly the state of the art of robots who take care of plants.

  • The Pet Plant [14]
  • Low cost colour sensors for monitoring plant growth in laboratory [15]
  • Plant growth monitoring system, with dynamic user-interface [16]
  • Robotics in protected cultivation [17]

Fourthly the state of the art of robots who look at the eating of their users.

  • An Intelligent Food-Intake Monitoring System Using Wearable Sensors. [18]
  • Big fridge is watching you [19]

Lastly, the state of the art of thermostats and of measurements of oxygen. (state of the environment)

  • Neofox oxygen sensors: precise oxygen sensors for both liquids and gasses. [20]
  • PhO2: Smartphone based Blood Oxygen Level Measurement Systems using Near-IR and RED Wave-guided Light [21]
  • Smart device for gas range [22]

Summaries of literature

Smart Homes

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

The paper develops a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, AI and sensor fusion technology. It uses three systems to create a smart home environment:

  • A wearable motion sensing device for on the residents' wrists and its corresponding 3D gesture recognition algorithm
  • A wearable motion sensing device for on the residents' feet and its indoor positioning algorithm.
  • Multisensor circuit module to realize home safety.

An intelligent monitoring interface provided real-time information about the smart home system (including but not limited to temperatures, CO concentrations, household appliance status, human motion signals and the results of gesture recognition and indoor positioning)

The architecture of the smart home system consists of a wearable inertial sensing module, a multisensor circuit module, an information processing module (in this case an Arduino MEGA mictrocontroller), a decision-making module (such as a PC), an intelligent monitoring interface and a household appliances plant (with devices such as the air conditioning and the television). The wearable inertial sensing module is used to detect motion signals generated by hand an foot movements for remote control of household appliances and smart energy management functions. The multisensor circuit module measures environmental parameters including temperature and CO concentrations. The Arduino is used to collect all data and send it to the decision-making module. The decision-making module then generates commands for the devices in the household appliances plant.

Matching technologies of home automation, robotics, assistance, geriatric telecare and telemedicine

With the increase of elderly people there is in increase in the need for home automation. This is necessary to decrease the healthcare burden which is now only getting larger. At this time most of the helping systems are stand-alone instead of working together and dependent on each other. To get a more efficient system technological integration is desired. Standalone systems for elderly care can even be more of a problem. The elderly can have problems with using multiple different interfaces. With integrated systems resources can be shared, which can’t be done with stand-alone systems. With shared systems also hardware can be used for multiple reasons, this decreases cost. Connection to a centralized host can be the solution to the problems of stand-alone systems. Howerver a centralized host could mean a decrease in robustness of the system. The use of intelligent software agents to communicate and mediate with other parts of the systems increases the efficiency of the system. This can be seen as an alternative for the centralized host and therefore avoid loss of robustness. To even further increase efficiency the communication protocol should be independent of the subsystems. A used network must also support multiple forms of communications.

Robots who take care of plants

Low cost colour sensors for monitoring plant growth

A relatively new area of research is that of non-destructive methods to measure the health status of plants. Such as looking at subtle changes in the colour of the leaves. This paper mainly focuses on low cost colour sensor for monitoring leaf colour of plant tissues.

To implement this system a autonomous robotic arm containing RGB colour, environmental and proximity sensors are used, as well as a camera. The robotic arm uses five stepper motors controlled through a motor controller and a micro-step driver. Their system was either compatible with the ColorPAI or the TCS 3200 colour sensor and both were controlled with a Basic Stamp microcontroller. The ColorPai uses a RGB LED light source and records the quantity of light reflected back from the object to determine the color. The TCS3200 illuminates the object with two white LEDs and interprets the colour of the object by producing a square wave with a frequency proportional to the light reflected by the object. A completely different way of determing plant leaf colour is by using an image captured by the camera and determining through software the colour of each pixel. This may however lead to false colour. For example when shadows from overlapping leaves are misinterpreted as colours of the leaves itself.

They only tested the TCS3200 and calibrated it across a broad range of green and yellow colours. In the end it had an average error around the 4% when determining an RGB color in the range of green and yellow colours.

Plant growth monitoring system, with dynamic user-interface

The paper develops a prototype for an efficient plant growth monitoring system. It provides data about the environmental parameters surrounding the plant and maps the changes in plant growth to the inputs given by person caring for the plants, such as the quantity of water or fertilizers. It can also measure the plants height.

The system consists of a Raspberry Pi which analysis the output of multiple sensors and sends this data via Bluetooth to someone's phone. They also developed an user-friendly app which displays all this data. The system does not take care of the plants itself, it only monitors a variety of environmental parameters

Robotics in protected cultivation

Right now there is a lot of research to increase automation in protected cultivation. Production of high value plants is facing an increase in problems such as increased costs for employees and decrease in skills. Robotics can help decrease these problems. The problems can be sorted into different groups. For seeding, grafting and cutting there are already products which can do this just as there are for transporting, sorting, packaging and cleaning. However there is still a lot to win in the department of weeding, thinning, leaf picking, protection and harvesting. The technical challenges in robotics are the fact that technologies have to deal with complex environments. There are two solutions which can be used in this case, advancing technology or modifying the environment.


Robots who look at the eating of their users

An Intelligent Food-Intake Monitoring System Using Wearable Sensors

Researches are looking for accurate methods requiring less user-involvement to assess general food-intake. The paper proposes an intelligent foot-intake monitoring system that can automatically detect eating activities. An in-ear microphone with a miniature camera are combined in a light-weight wearable headset. The sound from the microphone is classified into different eating activities and the camera takes pictures of the food if a chewing activity is detected. The key images of food are then selected sequentially and a dietary assessment log is generated to reflect a user's dietary behaviour. Novelties in this design were:

  • Developing a noise-resilient sound activity detection method suitable for daily use
  • Introducing food images to improve assessment accuracy
  • Selecting key images automatically to minimize the size of the food-intake assessement log

Big fridge is wathching you

The smart kitchen is becoming reality. With the enormous increase of technology in everyday things one cannot deny the probability of a smart fridge. This fridge could use information from your wearables, agenda and smart devices to recommend meals. The used data can also be relayed to doctors or caregivers, for instance when an elderly is not eating enough. Furthermore it can help people who tend to forget what is in the fridge with keeping an inventory and tracking expiration dates. This way an order could be placed for what is needed from the supermarket.


Thermostats and measurements of oxygen

PhO2: Smartphone based Blood Oxygen Level Measurement Systems using Near-IR and RED Wave-guided Light

Measuring a patient's blood oxygen level plays a critical role in healthcare practices. In the paper they develop a phone-based oxygen level estimation system which uses the camera and flashlight functions of a smartphone. Since the the camera and the flashlight of a smartphone aren't made for this purpose, they encountered many challenges in using them for this purpose.

Blood oxygen level is often indicated through oxygen saturation measurement (SpO2). Accurately measuring SpO2 with a high frequency is critical in monitoring the well being of key organs and also to provide warning signs of abnormalities. One of the most common ways to measure SpO2 is by using a pulse oximetry system. This system comprises dedicated hardware and software. It works by project light beams at specific wavelengts deep into the users' finger, toe, earlobe, or other location. This light hits dedicated photo electrodes and the intensity of the light received carries information that can be used to determine the users' SpO2. This way of measuring SpO2 is very reliable but it requires the user to purchase one and to carry it with them during the day. Patients often forget to take the device with them or forget to charge it. Also different patients have very different finger and earlobe sizes and the devices are thus not always a good fit.

There are already many applications for smartphone out there that use the flashlight and camera to determine various blood properties. However, none have the ability to accurately estimate SpO2. This low accuracy is a result of the fundamental challenges when one tries to repurpose the camera and flahslight for SpO2 measurements. Examples of these challenges are:

  • One needs an IR wavelength
  • One needs to work around the varying movement, pressure, contacting area of the users' finger

In the paper they develop a hardware add-on (PhO2) designed to be snapped on the phone as a phone case which has optical filters of different wavelengths. The add-on helps stabilize the users' finger. Due to the limitations of the phone's hardware the reflected light captured by the PhO2 is further processed by dedicated algorithms.

Medication reminders

Medicine Reminder and Monitoring System for Secure Health Using IOT

Elderly people sometimes tend to forget doing basic things among daily routine. This can lead to them forgetting to take their medicine at the right time of forgetting it entirely. Using the Internet of Things (IoT) network low cost medical sensing can be produced. There have been multiple test with technologies to find a way to decrease this problem. A monitoring system and sensor can send information using a wireless module. The information can be shared using IoT, however since health information can be very personal an encryption or decryption purpose is recommended.

MESSAGE INA BOTTLE

Medication reminders in the form of pill bottles are already available. The bottle can notify patients when they need to take their medication or missed a dose as well as seeing that the pills are almost out and notifying a pharmacy. This technology can not only increase the effectiveness of certain drugs but also reduce the readmission rates at hospitals. The pill bottle uses a sensor to detect opening and closing of the lit and compare its content. This is then send to the startups server which can analyze the data. Using different colors of light the user is given an indication of when taking medication is necessary.

References

  1. Tsung-Yen Chen ; Pei-Hsuan Tsai ; Ting-Shuo Chou ; Chi-Sheng Shih ; Tei-Wei Kuo ; Jane W.-S. Liu ; Anitha Thamizhmani (2008) Component Model and Architecture of Smart Devices for Elderly, IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/4459143/
  2. Hongwei Huo ; Youzhi Xu ; Hairong Yan ; Saad Mubeen ; Hongke Zhang (2009). An Elderly Health Care System Using Wireless Sensor Networks at Home. IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/5210943/
  3. Demir, E., Köseoğlu, E., Sokullu, R. and Şeker, B. (2017). Smart Home Assistant for Ambient Assisted Living of Elderly People with Dementia. Procedia Computer Science, [online] 113, pp.609-614. DOI: http://10.1016/j.procs.2017.08.302
  4. https://www.hifi.nl/artikel/24133/Het-slimme-zorghuis.html
  5. Prathiba Udupa, Siva S. Yellampalli, (2018) "Smart home for elder care using wireless sensor", Circuit World, Vol. 44 Issue: 2, pp.69-77, https://doi-org.dianus.libr.tue.nl/10.1108/CW-12-2017-0072
  6. Nagender Kumar Suryadevara ; Subhas Chandra Mukhopadhyay, (2012). Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly. IEEE Xplore, https://ieeexplore.ieee.org/abstract/document/6122042/
  7. Hsu Y., Chou P., Chang H. (2017) Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology. [online] Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539810/
  8. Franchimon, F., & Brink, M. (2009). Matching technologies of home automation, robotics, assistance, geriatric telecare and telemedicine. Gerontechnology, 8(2), 88-93. DOI: 10.4017/gt.2009.08.02.007.00
  9. Ramljak, M. (2017). Smart home medication reminder system. 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM). [online] Available at: https://ieeexplore.ieee.org/document/8115585/
  10. Hsu, W., Kuo, C., Chang, W., Chang, J., Hou, Y., Lan, Y., Sung, T. and Yang, Y. (2010). A WSN smart medication system. Procedia Engineering, [online] 5, pp.588-591. Available at: https://doi.org/10.1016/j.proeng.2010.09.178
  11. Ishak, S.A., Zainol Abidin, H. and Muhamad, M. (2017). Improving medical adherence using smart medicine cabinet monitoring System. Indonesian Journal of Electrical Engineering and computer science. [online] Available at: https://pdfs.semanticscholar.org/2f4e/b089c34e049e24c572dedd6140c8ec3b97fa.pdf
  12. Samir V. Zanjal, Girish. R. Talmale, Medicine Reminder and Monitoring System for Secure Health Using IOT, Procedia Computer Science, Volume 78, 2016, Pages 471-476, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2016.02.090. (http://www.sciencedirect.com/science/article/pii/S1877050916000922)
  13. Page, D. (2013). MESSAGE INA BOTTLE. Hospitals & Health Networks, 87(12), 16. Retrieved from https://search.proquest.com/docview/1470884471?accountid=27128
  14. McCalley T., Mertens A. (2007) The Pet Plant: Developing an Inanimate Emotionally Interactive Tool for the Elderly. In: de Kort Y., IJsselsteijn W., Midden C., Eggen B., Fogg B.J. (eds) Persuasive Technology. PERSUASIVE 2007. Lecture Notes in Computer Science, vol 4744. Springer, Berlin, Heidelberg
  15. Seeyle M., Sen Gupta G., Bailey D. (2011) Low cost colour sensor for monitoring plant growth in laboratory. Instrumentation and Measurement Technology Conference. [online] Available at: https://ieeexplore.ieee.org/document/5944221/
  16. James J., Manu Maheshwar P. (2017) Plant growth monitoring system, with dynanic user-interface. Humanitarian Technology Conference. [online] Available at: https://ieeexplore.ieee.org/document/7906781/
  17. E.J. van Henten, C.W. Bac, J. Hemming, Y. Edan, Robotics in protected cultivation, IFAC Proceedings Volumes, Volume 46, Issue 18, 2013, Pages 170-177, ISSN 1474-6670, ISBN 9783902823441, https://doi.org/10.3182/20130828-2-SF-3019.00070. (http://www.sciencedirect.com/science/article/pii/S147466701534979X)
  18. Liu J., Johns E. Atallah L. (2012) An Intelligent Food-Intake Monitoring System Using Wearable Sensors. Wearable and Implantable Body Sensors Networks. [online] Available at: https://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6200559
  19. S. Cass, "Big fridge is watching you [smart technologies monitoring food from production to consuption]," in IEEE Spectrum, vol. 50, no. 6, pp. 88-88, June 2013. doi: 10.1109/MSPEC.2013.6521045 keywords: {Food manufacturing;Food production;Food technology;Foreacasting;Internet;Technology forecasting;Waste management}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6521045&isnumber=6521004
  20. Neofox oxygen
  21. Bui N., Nguyen A., Nguyen P., Truong H., Ashok A., Dinh T., Deterding R., Vu T. (2017) PhO2: Smartphone based Blood Oxygen Level Measurement Systems using Near-IR and RED Wave-guided Light. Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems. [online] Available at: http://mnslab.org/tamvu/paper/2017%20PhO2%20Nam%20Bui.pdf
  22. https://patents.google.com/patent/US9752783B2/en