Dual-mode exercise pose recognition using video and pressure sensing Matrix Mat

Name: Mya, Nyan Lin

keyword: Exercise recognition

; Pose estimation

; Piezoresistive

; Pressure sensing Matrix Mat

; Machine learning

; Machine learning

; Principal component analysis (PCA)

; Random Forest

; Interactive fitness technologies

Abstract: This thesis project presents an advanced pose recognition system that functions effectively both with and without a vision-based system by employing piezoresistive materials. Designed to accurately identify various human poses such as standing, cross-legged sitting, and one-leg standing, this system integrates real-time predictions using either camera inputs by applying pose estimation method or an 8x8 pressure sensing matrix mat with Convolutional Neural Network (CNN). We have enhanced the robustness of the system through the implementation of centering and scaling techniques in the pose-estimation model. Furthermore, by applying Principal Component Analysis (PCA), we have successfully reduced the computational complexity of the data points by half while achieving the desired accuracy for the model. Low burden pressure sensing matrix mat was developed for the system as well. This research holds significant potential for applications in diverse fields including domestic exercise routines, sports performance analysis, physical rehabilitation, and interactive fitness technologies, all with a focus on safeguarding user privacy

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Somrudee Deepaisarn

Created: 2024

Modified: 2024-11-29

Issued: 2024-11-29

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2023.633

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Artificial Intelligence and Internet of Things

Grantor: Thammasat University

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