Building a Deadlift Spot using Machine Learning on Raspberry Pi

TLDRIn this video, we build a deadlift spot using machine learning on a Raspberry Pi. We use Python, OpenCV, and scikit-learn to count reps and improve form. We train a machine learning model using pose estimation and track different positions and form during the deadlift. We also attempt to 3D print a custom case for the Raspberry Pi, LCD screen, and camera. The final result is a device that can analyze deadlifts in real-time and provide feedback on form.

Key insights

⚙️We use Python, OpenCV, and scikit-learn to build a deadlift spot that counts reps and improves form.

🖥️We access a video camera using Python and OpenCV to capture the deadlifts in real-time.

🏋️‍♂️We use pose estimation with a deep learning model called PoseNet to extract body coordinates during the deadlift.

🔁We build a machine learning model using scikit-learn to track different positions and form during the deadlift.

🖨️We attempt to 3D print a custom case for the Raspberry Pi, LCD screen, and camera to create a compact and portable device.

Q&A

What programming languages and libraries do you use?

—We use Python and various libraries, including OpenCV, scikit-learn, and TensorFlow, to build our deadlift spot.

How does the device count reps and improve form?

—The device uses pose estimation to track body coordinates during the deadlift and a machine learning model to analyze different positions and form. It can count reps and provide feedback on form in real-time.

Can the device be used for other exercises?

—The device can be modified to track and analyze other exercises by training the machine learning model on the specific movements and poses of those exercises.

Is the 3D-printed case necessary?

—The 3D-printed case is not necessary for the functioning of the device but provides added protection and portability for the Raspberry Pi, LCD screen, and camera.

Are there any improvements planned for the device?

—There are ongoing improvements planned for the device, including optimizing the machine learning models, refining the 3D-printed case design, and adding additional features such as data logging and analysis.

Timestamped Summary

00:00Introduction to building a deadlift spot using machine learning on a Raspberry Pi.

02:30Using Python, OpenCV, and scikit-learn to build the deadlift spot and access the video camera.

05:00Using pose estimation with PoseNet to extract body coordinates during the deadlift.

08:30Building a machine learning model to track positions and form during the deadlift.

10:00Attempting to 3D print a custom case for the Raspberry Pi, LCD screen, and camera.

14:00Testing the deadlift spot at the gym and evaluating its performance.

16:00Final thoughts and future improvements for the device.

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