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Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.
| Date | Stars |
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| 2026-07-24 | 323 |
| 2026-07-25 | 323 |
| 2026-07-28 | 323 |
| 2026-07-30 | 323 |
| 2026-08-06 | 323 |
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# HAR-stacked-residual-bidir-LSTM The project is based on [this repository](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition) which is presented as a tutorial. It consists of Human Activity Recognition (HAR) using stacked residual bidirectional-LSTM cells (RNN) with TensorFlow. It resembles to the architecture used in "[Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://arxiv.org/pdf/1609.08144.pdf)" without an attention mechanism and with just the encoder part. In fact, we started coding while thinking about applying residual connections to LSTMs - and it is only afterwards that we saw that such a deep LSTM architecture was already being used. Here, we improve accuracy on the previously used dataset from 91% to 94% and we push the subject further by trying our architecture on another dataset. Our neural network has been coded to be easy to adapt to new datasets (assuming it is given a fixed, non-dynamic, window of signal for every prediction) and to use different breadth, depth and length by using a new configuration file. Here is a simplified overview of our architecture: ### Simplified view of a "2x2" architecture. We obtain best results with a "3x3" architecture (details below figure). <p align="center"> <img src="architecture_example_2x2.jpg" /> </p> Bear in mind that the time steps expands to the left for the whole sequence length and that this architecture example is what we call a "2x2" architecture: 2 residual cells as a block stacked 2 times for a total of 4 bidirectional cells, which is in reality 8 unidirectional LSTM cells. We obtain best results with a 3x3 architecture, consisting of 18 LSTM cells. ## Neural network's architecture Mainly, the number of stacked and residual layers can be parametrized easily as well as whether or not bidirectional LSTM cells are to be used. Input data needs to be windowed to an array with one more dimension: the training and testing is never done on full signal lengths and use shuffling with resets of the hidden cells' states. We are using a deep neural network with stacked LSTM cells as well as residual (highway) LSTM cells for every stacked layer, a little bit like in [ResNet](https://research.googleblog.com/2016/08/improving-inception-and-image.html), but for RNNs. Our LSTM cells are also bidirectional in term of how they pass trough the time axis, but differ from classic bidirectional LSTMs by the fact we concatenate their output features rather than adding them in an element-wise fashion. A simple hidden ReLU layer then lowers the dimension of those concatenated features for sending them to the next stacked layer. Bidirectionality can be disabled easily. ## Setup We used `TensorFlow 0.11` and `Python 2`. `Sklearn` is also used. The two datasets can be loaded by running `python download_datasets.py` in the `data/` folder. To preprocess the second dataset (opportunity challenge dataset), the `signal` submodule of scipy is needed, as well as `pandas`. ## Results using the previous public domain HAR dataset This [dataset](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) named `A Public Domain Dataset for Human Activity Recognition Using Smartphones` is about classifying the type of movement amongst six categories: (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING). The bests results for a **test accuracy of 94%** are achieved with the 3x3 bidirectional architecture with a learning rate of `0.001` and an L2 regularization multiplier (weight decay) of `0.005`, as seen in the `3x3_result_HAR_6.txt` file. Training and testing can be launched by running the config: `python config_dataset_HAR_6_classes.py`. ## Results from the Opportunity dataset The neural network has also been tried on the [Opportunity dataset](https://archive.ics.uci.edu/ml/datasets/OPPORTUNITY+Activity+Recognition) to see if the architecture could be
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