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Convolutional and LSTM networks to classify human activity
| Date | Stars |
|---|---|
| 2026-07-31 | 463 |
| 2026-08-03 | 463 |
| 2026-08-06 | 463 |
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# Human Activity Recognition (HAR) In this part of the repo, we discuss the human activity recognition problem using deep learning algorithms and compare the results with standard machine learning algorithms that use engineered features. The data can be downloaded from the [UCI repository](https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones). Also see G.Chevalier's [repo](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition) and A. Saeed's [blog](https://aqibsaeed.github.io/2016-11-04-human-activity-recognition-cnn/) where I have got lots of inspiration. ## Contents The `utils` folder contains the code for reading and processing the data into a tensor form. The generated tensors have the dimensions ``` (batch, seq_len, n_channels) ``` where `batch` is the number of training examples in each batch, `seq_len` is the number of steps in the time series (128) and `n_channels` is the number of channels where observations are made (9). The aim is to clasify the activities correctly, which are ``` 1 WALKING 2 WALKING_UPSTAIRS 3 WALKING_DOWNSTAIRS 4 SITTING 5 STANDING 6 LAYING ``` Below are the architectures used for training Notebook | Description -------- | ------ [explore_data](https://github.com/bhimmetoglu/seizure-forecast/blob/master/HAR/explore_data.ipynb) | Data exploration [HAR-LSTM](https://github.com/bhimmetoglu/seizure-forecast/blob/master/HAR/HAR-LSTM.ipynb) | LSTM network [HAR-CNN](https://github.com/bhimmetoglu/seizure-forecast/blob/master/HAR/HAR-CNN.ipynb) | Convolutional neural network(CNN) [HAR-CNN-LSTM](https://github.com/bhimmetoglu/seizure-forecast/blob/master/HAR/HAR-CNN-LSTM.ipynb) | CNN + LSTM hybrid [HAR-CNN-Inception](https://github.com/bhimmetoglu/seizure-forecast/blob/master/HAR/HAR-CNN-Inception.ipynb) | CNN with inception module ## Results Method | Test accuracy ------ | ------------- CNN | 93% LSTM | 88% CNN+LSTM | 88% CNN+Inception | 89% [Xgboost](https://rpubs.com/burakh/har_xgb) | 96% ## CNN architecture  ## LSTM architecture 
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:056e7793487097ad, llm:Description: "Convolutional and LSTM networks to classify human activity"; repository name includes 'deep-learning-HAR' (Human Activity Recognition). Language: Jupyter Notebook.
matched fp:056e7793487097ad, llm:Description: "Convolutional and LSTM networks to classify human activity"; repository name includes 'deep-learning-HAR' (Human Activity Recognition). Language: Jupyter Notebook.
matched fp:056e7793487097ad, llm:Description: "Convolutional and LSTM networks to classify human activity"; repository name includes 'deep-learning-HAR' (Human Activity Recognition). Language: Jupyter Notebook.