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Detects falling motion
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Able to provide high classification despite being the size of two 10cent coins
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Arduino board
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Wearable device on the arm for least obstruction
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Web client to see fall history
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Push notification when fall detected
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Notification to Telegram when fall detected
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Project architecture & tech stack
Inspiration
- 23.8% of Singaporeans will be aged 65 and above by 2030
- More than 1 in 4 elderly experiences a fall annually
- Falls are especially dangerous for the elderly
- Timely assistance could prevent the exacerbation of injuries
- It would be useful to see who is more prone to falls
What it does
- SafeSteps is a wearable IoT device that can be strapped to the arm
- Detects when the wearer falls
- Immediately sends a Telegram message to family members, alerting them
- Also displays falls in a dashboard to see if the elderly are very prone to falling, thus requiring more care
- Allows doctors/nurses to also monitor all patients under their care
How we built it
- We used edge computing to perform computation on a low-performant device using embedded-C.
- We used an Arduino board and used Tensorflow-lite to deploy a Machine Learning model in it.
- Fall detection is performed within the Arduino itself.
- We wrote python scripts for an RPi server. Arduino sends data to RPi through serial communication.
- RPi server is connected to the internet and updates the Firebase real-time db.
- RPi also calls the Telegram API to send a text to the family member.
- Family members/doctors can monitor falls through a web client.
- They can see past history of falls to see if the elderly are more prone to falls, thus getting more attention.
- Web client was built using React, Typescript, Tailwindcss.
- When a fall occurs, the client gets a push notification as well.
Challenges we ran into
- There were a lot of problems with the Arduino communication to the server and integrating the components into a single functional service.
Accomplishments that we're proud of
- Working together as a team and under pressure to build a successful project that can be used by the public to monitor their safety.
What we learned
- Hardware development
- Machine Learning in edge computing devices
- Front End Web Development
- Server Side Development
What's next for SafeSteps
- Bring this awesome product to the market! 🚀




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