Abstract

This paper describes the design and implementation of a prototype of IoT based heart rhythm monitoring system in order to get the wave pattern of irregular and normal sinus rhythm of a person that needs a diagnose of having a heart problem and alert the personnel of unusual behavioral reading of beats per minutes (BPM). The system will sense the cardiac rhythm and also identify the user their pulse rate reading in BPM. The system is connected to IoT cloud based where the data is stored and notifies any unusual behavior.

Introduction

Heart rhythm is the signal of heartbeat where it can be view in waveform signal. A normal heart rhythm is called normal sinus rhythm (NSR). An NSR will have a heart rate (pulse) between 50 and 100 beats per minute (BPM) and a normal impulse formation from the SA node (P wave)[1]. Each individual’s normal resting heart rate will vary and can range from 40 to 120, higher in young children. A certain amount of variation in heart rate throughout the day is normal as activity levels vary. Even though an individual’s regular rhythm may not exactly fit, into the category of normal sinus rhythm, it does not necessarily suggest that a problem exists. In addition, even if an individual’s rhythm is normal, it does not preclude underlying heart disease[1].

Design and Implementation

This section details the implementation system. The system runs in a sequential form: patient – device – server – web application – personnel (doctor).

The system aimed to read the blood flow volume inside the user to read the human pulse or heart rhythm and beat per minute. The sensor used the PPG system where it used reflective type PPG system where the intensity of reflective light received inside the photoresistor is converted into a waveform signal. Thus, the first step is the pulse sensor wrap to the index finger of a user. Secondly, the data transfer from the pulse sensor will undergo algorithm calculation to display the Beat per Minute. In another way, the signal also transfers to the IoT platform for storage and retrieve to the web application to view the displayed of heart rhythm signal.

Preliminary Result

This section shows the preliminary result from the prototype testing for waveform pattern for normal people that don’t have a heart problem.

The device is connected to the Favoriot platform to store the BPM reading as it will monitor and notify the personnel is unusual BPM reading reads.

An alert message will be received when the BPM reading exceeds the threshold value stated.

Conclusion

The prototype is a success in monitoring and notifies the respective personnel in sending an alert when unusual behavior reading is recorded in IoT platform, thus can conclude almost 70% of the project is done.

Future Works

The next step to complete the system is the monitoring of the waveform pattern on the real-time monitor and a comparison between the irregular heartbeat and normal heartbeat. Thus, a web-based application will be created with the help of SQL database to store and compare the waveform pattern with the database of irregular heartbeat get from trusted resources.

References

[1] “Normal Heart Rhythm _ Heart Rhythms _EquiMed Corporation.”
[2] A. Sparshott, “A quick guide to ECG,” IV line, no. 52. pp. 1–2, 2009.
[3] “Remote Heartbeat Monitor [Analog Devices Wiki].” [Online]. Available: https://wiki.analog.com/university/contest/design/sub missions/the_sentinels

[Note: This project is being done by UTHM, our FAVORIOT’s University’s collaborator. Article was written by Siti Nur Hanisah Binti Hamidon, Supervisor: Dr. Ansar Bin Jamil, FKEE UTHM]

You can check out the whole LIST of IOT PROJECTS by our University Collaborators.

Abstract

The Smart Bracelet for Elderly People is an innovative solution that aims to detect and locate people especially for elderly people with a health problem. It is designed as a remote alert system for people at risk of stroke, sudden heart attack, and seizures. The smart bracelet provides fall detection system, GPS tracker to trace the current location and a medicine reminder setting to remind pill intake on time. This device is synchronized with the FAVORIOT platform which stores GPS data and fall detection indicator.

Result

This section shows the preliminary result from the prototype testing for fall detection, GPS tracker and Medicine Reminder coding.

[Note: This project is being done by UTHM, our FAVORIOT’s University’s collaborator. Article was written by Siti Nur Hanisah Binti Hamidon, Siti Khadijah Binti Narudin, Siti Norliani Binti Ahmad Basni Supervisor: Dr. Ansar Bin Jamil, FKEE UTHM]

You can check out the whole LIST of IOT PROJECTS by our University Collaborators.

In the quest to generate 100K IoT Professionals (or so-called the new Generation-IoT) in the country, FAVORIOT, the latest IoT Startup in Malaysia partnered with 15 Malaysian Universities to review, refresh and include not only IoT elements in the curriculum and syllabus but also introduce a more practical method for the University students to be familiar in IoT middleware and how to create their first IoT application and project.

The list of the Universities who joined the program are listed below (the first 10 Universities joined the FAVORIOT-University Program since May 31, 2017):

1. Universiti Teknologi Malaysia (UTM)
2. Universiti Tun Hussein Onn Malaysia (UTHM)
3. Universiti Kuala Lumpur (UniKL)
4. Universiti Malaysia Sarawak (UNIMAS)
5. Universiti Sultan Zainal Abidin (UniSZA)
6. Universiti Tenaga Malaysia (UNITEN)
7. Universiti Teknikal Malaysia Melaka (UTeM)
8. Universiti Sains Islam Malaysia (USIM)
9. Universiti Malaysia Perlis (UniMAP)
10. Asia Pacific University (APU)
11. Taylor’s University
12. Universiti Teknologi Petronas (UTP)
13. International Islamic University Malaysia (IIUM)
14. Universiti Utara Malaysia (UUM)
15. Universiti Teknologi Mara (UiTM)

UPDATED (Sept. 22, 2017)

Two more Universities in Malaysia joined the other 15 Malaysian Universities:

1. Universiti Putra Malaysia (UPM)
2. Universiti Malaysia Terengganu (UMT)

UPDATED (Dec. 27, 2017)

Only 16 Universities decided to adopt the Favoriot platform. See below Diagram.

It is expected about 450 graduates every year will be utilizing FAVORIOT platform for their Lab experiments, Final Year or Research projects. The program will allow more than 2250 devices to be connected to the platform.

About the Author

Dr. Mazlan Abbas is currently the Co-Founder and CEO of FAVORIOT Sdn Bhd. He is an IOT Evangelist and a Thought Leader. He received an award as 50 Most Impactful Smart Cities Leaders by World CSR 2017. He is ranked Top 10 in IoT Top 100 Influencers by Postscapes 2016/2017, ranked Top 100 in Smart Cities Top Experts by Agilience Authority Index May 2016, No. 20th Thought Leader in IOT by 2014 Onalytics Report – “The Internet of Things – Top 100 Thought Leaders”. He is also a Global Vision Board Member. You can reach him on LinkedIn or Twitter. Check all his presentation slides HERE.

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