As engineering students, we have always found ourselves spending countless hours on our devices, much like this Hackathon. As a result of this, many of us, at a young age have begun to face numerous issues such as back-aches, fatigue, poor blood circulation and breathing difficulties.
What it does
Pose-itive is a posture correcting IoT System with haptic, visual and tactile feedback. In addition, this information is also displayed on a webapp that visualises key data points collected by the wearable. By presenting these in an easily digestible format, Pose-itive aims to reinforce good posture habits in people of all demographics by repeatedly offering a multitude of sensory feedback, motivating statistics, and recommendations.
How we built it
The wearable device consists of a 6-axis IMU placed on the nape of the user's neck which measures the tilt and hence the amount of slouching. There are 4 different categories namely: no slouch, low slouch, medium slouch, and high slouch. Depending on the level of slouching, a haptic vibration motor vibrates with different intensities.
Furthermore, an external device offers additional stimuli to alert the user to correct their posture. The Slapper moves back and forth and the buzzer plays a tune to provide tactile and auditory feedback.
Challenges we ran into
- Setup communication between the webapp and the hardware server
- Soldering minute electronic components in order to make a compact wearable device
- Parallelising and synchronising hardware actuation events
Accomplishments that we're proud of
- Creating a working pipeline for the proposed IoT system
- Successfully integrating a multitude of sensors, actuators and datastreams into a proof-of-concept system
- Actually making an annoying product that qualifies us for the "Most Annoying Hack" spot prize
What we learned
- Team work makes the dream work
- Integrating hardware and software seamlessly is difficult but not impossible
What's next for the Slap Slap team and Pose-itive
- Cloud deployment of web-app with extended user base
- Machine Learning capabilities fused with demographic user data to provide personalised recommendations
- Better parallelised hardware using RTOS for seamless real-time operation and improved accuracy