MYOWNJAM

Inspiration

Excitement from attending our first hackathon. We were attracted to idea of using the Myo because of the great potential that lies in proper analysis of the EMG signal.

What it does

We use the EMG signal to give a more accurate prediction of how many steps completed (Yes, We are using the Myo on the leg). We also use the real time data to track the change in number of steps per minute and play music with a beat that matches it.

How we built it

We were actually looking for trends in EMG signals to determine muscle fatigue to turn Myo into a weight training buddy that looks out for muscle strain and gives you a warning.

Challenges we ran into

We could not change our matlab code into a platform suitable for an app because of a lack of computer science background in the team.

Accomplishments that we're proud of

The fact that we could translate our idea into something to show for.

What we learned

We learnt a lot about processing EMG signals. Also learnt that we should do a lot more research on what is feasible before confirming an idea.

What's next for MYOWNJAM

Coupling the EMG data with heartrate tracking could really give a better estimate of energy expenditure than current methods. With a little more time, interpreting muscle fatigue out of the EMG data should be possible and we would like to figure that out. Also, translating what we've done so far into an app with a bigger music library.

Built With

• matlab
• matmyo
• myo