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Real-time Exercise Posture Correction Using Human Pose Detection Technique
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Adriamanalimanana, Bruno Dr., Spetka, Scott Dr., Chiang, Chen-Fu Dr.
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Fall 2021
Publication Date
2021-12
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Abstract
Human pose detection is one of the fascinating research areas
in the field of computer vision that has many unsolved
challenges. Detecting and capturing human activity is
advantageous in many fields like sport analysis, human coordination tracking, public surveillance etc. Due to the
COVID-19 driven pandemic, it became hard for society to
access exercising hubs and newbies into the fitness industry
were left blank with almost no personal guidance that they
usually get in gym in terms of exercising in the right way
through one-on-one interactions. As these resources are not
always available, human pose detection can be a medium to
replace a human personal trainer by developing a real-time
exercise posture correction system on recorded videos or realtime image stream that allows people to safely exercise at
home avoiding injuries. This project uses a pre-trained
OpenPose Caffe model with two datasets i.e., COCO and MPII
to correct one of the exercises in the fitness industry. This
project also discusses various pose estimation and key point
detection techniques in detail and different deep learning
models used for pose classification.