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2023-09-08 00:22| 来源: 网络整理| 查看: 265

Inertial-based Activity Recognition with Transformers

This repository provide the official PyTorch implementation of the method described in the paper: "Boosting Inertial-based Human Activity Recognition with Transformers" (Shavit and Klein, 2021, IEEE Open Access).

We propose a general framework for inertial-based activity recognition with Transformers. Samples collected over time with inertial sensors on mobile devices are provided to a Transformer Encoder architecture for learning smartphone location recognition (SLR) and human activity recognition (HAR) tasks.

The proposed approach is the first to employ Transformers for this task and is shown to provide a consistent improvement, across multiple datasets and scenarios.

Our model architecture (IMU-Transformer) is shown below:

Training and Testing for Intertial-based Activity Recognition

This repository supports training and testing of deep learning models for inertial-based activity recognition. Specifically, these models take IMU data and classify the type of activity. We support two models:

A Transformer-based classifier (IMU-Transformer) A CNN-based (IMU-CNN) classifier, provided for comparison purposes

The entry point to our framework is the script main.py The frameworks takes in a .csv file, where each row includes a single sample and its associated class. The samples are then aggregated according to the window size specified in the configuration file.

In order to train a model run:

main.py train

During training, the models and log file will be saved to a dedicated output folder (created if does not exist)

In order to test a trained model:

main.py train --checkpoint_path

To see the full options, run:

main.py -h

The different hyper-parameters, including whether to run the Transformer/CNN architectures are controlled by the configuration file (config.json)

Configuration Parameters Parameter Name Description n_freq_print How often to print the loss to the log file n_freq_checkpoint How often to save a checkpoint n_workers Number of workers device_id The identifier of the torch device (e.g., cuda:0) input_dim The dimension of the input IMU data, e.g., 6 when using accelerometers and gyros window_size The size of the time window (i.e. how many samples in a window) num_classes Number of classes window_shift The window shift, put here the window_size to avoid window overlap use_baseline Whether to use the CNN-baseline or not (setting this to False will train the Transformer-based model) encode_position Whether to encode positions for IMU-Transformer transformer_dim The latent dimension of the Transformer Encoder nhead Number of heads for the MHA operation num_encoder_layers Number of encoder layers (Transformer) dim_feedforward: The latent dimension of the Encoder's MLP transformer_dropout The dropout applied to the Transformer transformer_activation Activation function used for the Transformer (gelu/relu/elu) head_activation Activation function used for the MLP classifier head baseline_dropout Dropout applied for the CNN baseline model batch_size The batch size lr The learning rate weight_decay The weight decay eps epsilon for Adam optimizer lr_scheduler_step_size How often to decay the learning rate lr_scheduler_gamma By what factor to decay the learning rate n_epochs Number of epochs

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