Real-time weight training counting and correction using MediaPipe

• Published in: Advances in computational intelligence Vol. 4; no. 2; p. 3
• Main Authors: Luangaphirom, Thananan, Lueprasert, Sirirat, Kaewvichit, Phopthorn, Boonphotsiri, Siraphong, Burapasikarin, Tanakorn, Siriborvornratanakul, Thitirat
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Applications programs; Artificial Intelligence; Body parts; Computational Intelligence; Deep learning; Empowerment; Engineering; Exercise; Feedback; Luminous intensity; Machine Learning; Objectives; Original Article; Physical fitness; Pose estimation; Posture; Real time; Repetition; Sports training; Usability; Weightlifting; Yoga; Weight training; Pose estimation; MediaPipe
• ISSN: 2730-7794; 2730-7808
• DOI: 10.1007/s43674-024-00070-w

This study introduces a web application designed to address the challenge of ensuring correct posture and performance in weightlifting exercises, with a particular focus on fundamental bodyweight movements targeting various body parts. The problem at hand primarily concerns beginners who require guidance for accurate exercise execution. To tackle this issue, the tool leverages a live camera in conjunction with the MediaPipe and OpenCV frameworks to extract key points from the user's body. It concentrates on seven core exercise postures, using these key points to calculate numerical values and angles. Users are required to adjust their view angles to activate the tool's pose estimation functions. An algorithm, based on predefined rules that determine posture thresholds and angles between three key points, is employed to detect incorrect postures, provide real-time feedback, and track repetition counts. The completion of all required stages is necessary to count a repetition as correct. Additionally, in this study, we have expanded the algorithm to include three new exercise postures: Bent over Dumbbell Row, Seated Triceps Press, and Dumbbell Fly. We have also adapted the system to detect the lying down view, which is essential for the Dumbbell Fly posture. The results of testing this application demonstrate further development potential, particularly in enhancing the model’s framework to accommodate challenges such as high light intensity, pale skin tones, and instances when a body part is obscured by an object.