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A heart abnormality detection and emergency medical response system for senior citizens


KardiaCare system showing app prototype (left to right) screens for login, current vitals and alert, and wearable screens prototype on the extreme right (top to bottom) showing current vitals, alert and voice alert


Team Lead


September 2017 - March 2018,

September - December 2020

(total 12 months)


Dyuti Dave, Suman Daryani

Faqia Iqbal, Katra Farah, Kim Correa, Rubia Guerra


Adobe Illustrator, Procreate, Balsamiq, Adobe XD, Arduino, Java


User Interviews, Persona Building, Sketching, Wireframing, Prototyping, Affinity Diagramming, Usability Testing, Requirement Gathering, App Development, Cognitive Walkthroughs, Storyboarding


We designed and developed a heart abnormality detection and response system called KardiaCare, comprising of a wearable sensor & mobile application that can detect abnormalities in heart rhythms and inform emergency contacts as well as request emergency medical help from the nearest hospital.

This project was my undergraduate thesis where we did the major app design and development. The design was later iterated upon as a graduate course project.

As the project lead during my undergraduate study, I led the design and app development, including the development of our algorithm for anomaly detection. During the graduate course, I conducted and analyzed user interviews, and walkthroughs, and co-created the low and medium fidelity prototypes.

Can I jump to the Results? Sure, click here!

So, what was the problem?

For senior citizens globally, cardiovascular diseases are a leading cause of death, often due to delayed timely help (especially in India due to lack of available medical resources), more so if the adults live alone, leading to avoidable loss of lives.


Existing emergency applications often rely on user intervention to request for help but in the case of a medical emergency, users may be incapacitated to an extent of not being able to request for help. Thus, there is a need of removing the dependency on the user, using software that can make reliable and accurate decisions. 

Who was our target demographic?

Since cardiovascular diseases are most prevalent in women above 60 years of age, we limited our target demographic to  older women, to ensure we could create an accurate diagnostic tool.

What was our plan?

We followed the design thinking process of Empathize - Define - Ideate - Prototype - Test. 


Initiated as an undergraduate thesis project, the idea for KardiaCare came to me from personal experience - my grandmother (aged 93 in 2023) lived alone independently ~400km away from us, and with a history of heart disease in the family, we were constantly worried about the lack of monitoring or assistance available. 


To understand the prevalence of heart disease in senior citizens so we could define our scope and research questions, we did a preliminary literature review, interviewed senior citizens, and also spoke with clinicians to understand how timely medical help could be delivered.

We learnt heart disease was more prevalent in women above 45 years of age, and senior citizens above the age of 60 were most likely to live alone. 


We thus defined our problem statement as creating a monitoring and response application for women above the age of 60 years living alone, so we could incorporate accurate diagnostic measures. The application would monitor heart rate and blood oxygenation, and also maintain a list of emergency contacts and nearby hospitals to be informed in case of an emergency.

We then developed personas for our user. Our persona was a 60-year-old woman in Mumbai, India, who was familiar with mobile applications but had not used any health tracking technology before, similar to our interview participants:

User Persona for a 60-year-old woman, showing target user goals and expectations (click to zoom)

We also created a storyboard, visualizing a possible scenario where our system could be used, which would further help refine our requirements:


Storyboard showing a possible usecase for KardiaCare (click to zoom)


We ran a questionnaire to reach a broader audience with questions relating to the usability, individual preferences, and feasibility of a monitoring wearable application. We then affinity diagrammed the responses:

Perceptions around heart health data tracking 2.png

A part of the affinity diagram we ended up with, showing various user preferences for the wearable (pink notes are categories, blue notes are user responses)

Some of our key findings:

Willingness to use: The majority of our interview and questionnaire respondents were willing to wear a device that tracks their heart health.

Comfort with and use of existing technology: While the majority of our respondents did not use any health tracking technology at the time, they were prepared to learn and adapt to a new one. 

Preferred appearance of the wearable: Participants mentioned they would like it to be unobtrusive and lightweight, and preferably look like an accessory to blend into their daily lives.

In terms of features, interview participants mentioned they would like the wearable to

  • Be personalized to their heart rate

  • Have a long battery life

  • Use haptics and visual modalities for alerts instead of relying solely on the mobile application

Prototype and Test:
Development of the algorithm and wearable:

Our wearable comprised of the SparkFun MAX30105 Pulse Oximetry sensor which was lightweight and accurate. We coupled that with an Arduino Uno which could be replaced with the Adafruit Flora, a wearable microcontroller. 

We then moved on to developing the algorithm, which can be found here:

It tracks the user’s heart rate and blood oxygen saturation level at fixed intervals (two indicators that in combination can be used to predict a heart attack), and in the case of an anomaly, alerts emergency contacts with the user’s location. The algorithm also locates the nearest hospital to request ambulance services through text message. It then also sends the information of the hospital to the emergency contacts, ensuring they are aware of the patient’s location at all times.


The KardiaCare algorithm to monitor and detect heart rhythm abnormalities

We tested the accuracy of the algorithm and wearable against industry-standard wearables, and found 98% accuracy in heart rate and oximetry measurements. Our algorithm was also able to accurately locate the nearest hospitals in under 10 seconds and send messages accordingly in under a minute, thus far surpassing manual actions. However, given limited access to the target population, we tested these amongst the research team in a limited geographical area, so our findings are not generalizable. Nonetheless, they do speak to the feasibility and accuracy of such an application.

Wearable and application design:

We then went on to create a low-fidelity hand-drawn prototype of what the application and wearable screens would look like:


Phone screens showing the users vitals and the alert cancelled screen; Wearable screens showing the current vitals and alert screen

We ran a cognitive walkthrough and usability test, with 7 participants. Our tasks were primarily centered around making sure the application was intuitive for users, from creating an account to viewing and sharing their data.


Our findings from the walkthrough and testing:

  • All of our users agreed that it would be easy for someone with minimal tech skills to navigate the app and the instructions are easy to follow.

  • Being able to fetch medical history for a customized period of time was appreciated.

  • Seeing their vitals in real-time on the dashboard was a major plus for all the users. 

  • 3 users commented on how the wearable was convenient and discreet.

  • Some of the labels were confusing, so we decided to reword them more simply in our next iteration. 

Based on this feedback, we went ahead and created our medium fidelity prototype in Balsamiq:

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Phone screens showing the users vitals and the alert screen; Wearable screens showing the current vitals and alert screen

The full medium-fidelity prototype can be seen here: 

Mobile app screens

Wearable screens

Given a limited scope and timeline, we did not evaluate the medium fidelity prototype. Below, we show the final working application, showing the setting up screens, the monitoring screen, and fetching nearest hospitals screen:


Design of the KardiaCare phone application - the login screen, emergency contact setting up screen, user vitals display screen, and locating nearest hospital screen (from left to right)


How did that turn out?

Our final result was a working prototype of the application:

Who else knows?

KardiaCare was presented as a Poster at the Grace Hopper Celebration India conference in 2021, and won Best Student Poster! 

The poster can be viewed here

Challenges we faced

A challenge we faced was the lack of access to the target population. This meant we were unable to interview and evaluate our application with sufficient target users to gain an accurate understanding of its impact and potential.

We were also geographically limited by testing it in a city with a large number of available hospitals nearby and constant cellular service. Most Indian cities and villages would not have the same, thus we would need to test the application in a broader geographic range to gauge efficiency.

What did we learn?

One of our biggest takeaways was the enthusiasm from our target user group - we began the project hesitant if older adults would be willing to wear a band that monitors their activity 24x7, as well as learn to use a mobile application. Our interview responses were enlightening in that aspect since participants said they’d be more than happy to use the wearable and app if it could be potentially life-saving.


We also realized that our system needed to factor in personalization - not only do people have varying heart rhythms, but we would also need the system to adapt to people with tachycardia/bradycardia or other medical conditions that would cause them to have different-than-usual normal heart rates.

What’s gonna happen next?

At the time we started this project (September 2017), the ECG feature on the Apple Watch was still a year away. Wearables were all the hype but very few talked about actively monitoring and looking for abnormalities in users’ heart rates. 


However, since then, wearable technology has made tremendous progress and with the rollout of the Apple ECG feature as well as other wearables that have now caught on (Fitbit, etc), we are glad to see the core idea of our project already be out in the world (and with better accuracy!).


Feel free to get in touch to discuss more about this project if you’d like!

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