Recent years have seen growing concerns arise about the issues of deteriorating physical and mental health, especially among youth. This alarming trend stems from the increasing sedentary lifestyles led by these so-called 'screenagers', social media pressure, academic stress, and a lack of access to appropriate resources. We students are all too aware of the crippling conditions that face many all accross the world.
Introducing "MindfulMotion: Your Personalized Wellness Companion" – a groundbreaking fitness, yoga, and mindfulness application that's set to revolutionize your holistic wellness routine. A powerful tool condensed to even the most enclosed enviroments, MindfulMotion will lead you towards self-imporvement. With advanced motion detection technology, this app offers three incredible features:
Say goodbye to manual rep counting. MindfulMotion uses precise motion detection to automatically count your reps during strength and cardio workouts. Whether you're doing push-ups, squats, or any other exercise, the app tracks your movements, ensuring you get accurate rep counts and helping you stay motivated.
Achieve yoga mastery with MindfulMotion's real-time pose detection. It analyzes your form during yoga sessions and alerts you if you deviate from the correct posture. Say farewell to imperfect asanas – now, you can hold your yoga poses with confidence and precision.
Embark on your mindfulness journey with MindfulMotion's guided meditations. Our app uses motion detection to gently remind you if your posture or focus wavers during meditation sessions. Stay centered and aligned as you experience the benefits of guided mindfulness practices.
MindfulMotion is your all-in-one companion for physical fitness, yoga, and mindfulness, ensuring every aspect of your wellness journey is supported with precision and care.
Python: The project is developed using the Python programming language, serving as the foundation for implementing various functionalities, including video capture, motion detection, and data manipulation.
OpenCV: This project utilizes OpenCV for video capture, motion detection, video frame processing. To detect movement and track objects, we calculate the phase difference between a base frame and a delta frame. We do this by applying image processing techniques such as contouring, dilation, and shadow removal to a threshold frame.
Pandas: This project utilizes the Pandas library for keeping track of motion events by recording timestamps.
Flask and Flask-SocketIO: Flask and Flask-SocketIO collectively allow the project to create a web-based user interface in python that provides real-time updates to the user, such as notifications about motion detection events and the current motion event count.
HTML/CSS: This project uses HTML/css to create a user friendly web interface to display movement data and an interactive wave which moves when a rep is complete.