| Summary: | The integration of Artificial Intelligence (AI) in wearable technology has transformed
fitness tracking, enabling automatic monitoring of activities such as running and cycling.
However, traditional fitness trackers struggle with detecting anaerobic exercises
like weightlifting, which involve complex and non-repetitive motions. This project addresses
this limitation by developing an AI-powered workout detection device capable
of recognizing weightlifting exercises, specifically bicep curls, lateral raises, and tricep
extensions. Inertial measurement data, including acceleration and angular velocity,
were captured using the MPU6050 sensor, which combines an accelerometer and gyroscope.
The MAX78000FTHR microcontroller, equipped with a CNN accelerator,
was used to classify these movements, enabling efficient workout detection despite
hardware limitations. This embedded AI solution enhances fitness tracking, providing
more comprehensive monitoring for individuals engaged in strength training.
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