To help machines learn what we human beings are doing via a camera is important. Once it comes true, machines can make different responses to all kinds of human's postures. But the process is very difficult as well, because usually it is very slow and power-consuming, and requires a very large memory space. Here we focus on real-time posture recognition, and try to make the machine "know" what posture we make. The posture recognition system is consisted of DE10-Nano SoC FPGA Kit, a camera, and an HDMI monitor. SoC FPGA captures video streams from the camera, recognizes human postures with a CNN model, and finally shows the original video and classification result (standing, walking, waving, etc.) via HDMI interface.

The objectives of the project was to make a dataset for posture recognition and use that dataset to create a CNN classifier.

Human Activity Recognition interprets and classifies human movements, postures and activities using CNN

Static hand gesture recognition, using NUS-II hand posture dataset (Python, Tensorflow, CNN)

Using CNN to recognize three diffrent gestures. (HW2 from NDHU Introductory of Deep Learning)

Deep learning system for classifying driver sitting postures from seat pressure sensor images in automotive environments.

this is another assignments on NDHU CSIE - Fundamental Deep Learning Assignments 2019

3D-CNN and Head posture recognition

Implementation of the CNN + LSTM + Attention deep learning model for human posture classification using eight-channel pressure sensor signals.

Reproducibility code, data, and results for the paper “Enhanced human posture recognition using multi-stream deep CNN with blended ensemble classification”.

Pattern Recognition project implementing an animal welfare and stress detection system using CNN-based species classification, posture indicators, meta-learning, and rule-based reasoning on the Animals-10 dataset.

wearable sign language recognition system enabled by a convolutional neural network (CNN) that integrates stretchable strain sensors and inertial measurement units attached to the body to perceive hand postures and movement trajectories.

This project develops a real-time sign language recognition system translating American Sign Language (ASL) gestures into text for seamless communication. It utilizes Convolutional Neural Networks (CNNs) to accurately recognize hand postures from video input. The project focuses on real-time processing for facilitating smooth conversations.

Yoga Pose Classification is a deep learning project using CNNs on the Yoga-82 dataset to recognize multiple yoga poses. It applies preprocessing, augmentation, and feature extraction to improve accuracy. Built with TensorFlow/Keras, it supports applications in fitness, health, and posture correction through automated pose recognition.