Oct 1, 2024 · In this tutorial, we’ll learn to implement human action recognition on videos using a Convolutional Neural Network combined with a Long-Short Term Memory Network. We’ll actually be using two different architectures and approaches in TensorFlow to do this.

Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories: LAYING. Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering.

Jul 8, 2022 · In this article, we are using raw data produced by Mobile Sensors for recognizing human activity. The activities that we will be predicting are: Downstairs; Jogging; Sitting; Standing;...

Mar 9, 2024 · This Colab demonstrates recognizing actions in video data using the tfhub.dev/deepmind/i3d-kinetics-400/1 module. More models to detect actions in videos can be found here. The underlying model is described in the paper "Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset" by Joao Carreira and Andrew Zisserman.

Sep 24, 2021 · In this post, you’ll learn to implement human activity recognition on videos using a Convolutional Neural Network combined with a Long-Short Term Memory Network, we’ll be using two different...

This repository contains keras (tensorflow.keras) implementation of Convolutional Neural Network (CNN) [1], Deep Convolutional LSTM (DeepConvLSTM) [1], Stacked Denoising AutoEncoder (SDAE) [2], and Light GBM for human activity recognition (HAR) using smartphones sensor dataset, UCI smartphone [3].

Python notebook for blog post Implementing a CNN for Human Activity Recognition in Tensorflow.

Jun 3, 2017 · In this part of the series, we will train an LSTM Neural Network (implemented in TensorFlow) for Human Activity Recognition (HAR) from accelerometer data. The trained model will be...

In this post, we will see how to employ Convolutional Neural Network (CNN) for HAR, that will learn complex features automatically from the raw accelerometer signal to differentiate between different activities of daily life. By Aaqib Saeed, University of Twente.

May 13, 2021 · Recently, I’d been working on a human activity recognition research project, trying to classify the postures of lower-limb amputees using accelerometer data from their shank. The traditional method is to use acceleration data from the thigh.