I've often heard of the importance of good posture and lifting technique, but it's easy to lose focus and use incorrect form when moving large or heavy items. Persistent use of incorrect lifting technique across decades of employment can lead to serious medical problems and cost companies greatly in increased health insurance premiums, workers compensation, and decreased productivity.
What it does
Liftr alleviates this issue by giving employees real-time feedback on their lifting technique. Because Liftr communicates with users using pleasant, low volume tones, it will quickly quickly transform into an "invisible" sixth sense, providing constructive feedback on lifting technique in an unobtrusive way.
For supervisors and managers, Liftr aggregates lift quality data into beautiful, intuitive reports. Are training sessions having a significant impact? Liftr can tell you! Are specific employees struggling with their technique? See it at a glance with Liftr!
How I built it
Liftr uses Inertial Measurement Units (IMUs) to measure the angle of specific joints. by comparing these angles, Liftr calculates a "score" for various lift-quality statistics, such as "back bent" or "back twisted." Once a score passes a threshold, a tone is played with intensity proportional to the severity of the issue. By using distinct tones for different statistics, Liftr can provide maximum information to the user.
Challenges I ran into
The most difficult challenge we encountered this weekend was a hardware issue with our IMUs. For comparative analysis, Liftr needs data from multiple IMUs in different positions on a user's body. However, the IMUs available for the prototype cannot be used together on the same communication bus. This necessitated difficult updates to the primary controller to support multiple communication buses.
Accomplishments that I'm proud of
What I learned
What's next for Liftr
In the short term, we're looking to collaborate with an ergonomics and human factors engineer to improve the algorithms used to calculate lift-quality scores. After that, the next step would be to manufacture production prototypes and partner with a potential client to conduct live beta testing. After ironing out any issues that come up in live testing, we would be ready to begin manufacturing and distribution.
Long term, once established in the market we would look to expand into other sectors. Similar systems could be adapted for use in athletics, fitness, and physical therapy. Another promising prospect is to use machine learning to improve our algorithm accuracy.