Human_Activity_Recognition

Info

This is the main branch

How to run the code

Configure the config.gin file in /config:

1. Setup the dataset under 'Input pipeline'/'DataLoader':

   • 'dataset_name': Choose between 'hapt' and 'rw' for HAPT and RealWorld.
   • If RealWorld is chosen then 'sensor_position' can also be selected.
   • 'dataset_directory': The absolute path to a directory containing the dataset files.
   • 'tfrecords_directory': The absolute path to a directory which is the destination of the created dataset tfrecord files. Also is where the dataset files are loaded from in the future.
   • 'create_tfrecords': This dis-/enables the dataset creation, True leads to the creation new tfrecords files, False skips the creation.
   • 'sequence_to_label': Select which classification method to use, sequence-to-label or sequence-to-sequence.

2. Setup the training under 'Train': There exists a basic and early-stopping section.

3. Setup the models under 'Main': Create models in 'models', examples and descriptions explain how.

Start by running: python3 main.py in the directory. Available flags are '--train'/'--notrain', '--eval'/'--noeval' and '--ensem'/'--noensem'. These flags select training, evaluation ensemble learning, the defaults are True/True/False.

Results

This section captures some results for the project 'Human Activity Recognition'

Baseline Evaluation

• Dataset - HAPT, Window Size - 250, Windown Shift - 125
• Optimizer - Adam, Learning Rate - 0.001, Epochs - 30k
• Batch - 32

Model	# of Parameters	Sequence to Label Accuracy	Sequence to Sequence Accuracy
simple_rnn-10-relu	313	95.15%	94.42%
stack_rnn-10_10-relu	523	94.26%	94.28%
stack_rnn-10_10_10-relu	733	94.19%	94.86%
bidirectional_rnn-10-relu	613	No Run	95.32%
simple_lstm-10-relu	823	96.2%	95.12%
simple_lstm-10-10-relu	1663	92.27%	90.08%
simple_lstm-10_10_10-relu	2503	88.34%	88.33%
bidirectional_lstm-10-relu	1633	No Run	88.33%
simple_gru-10-relu	683	97.68%	96.75%
stack_gru-10_10-relu	1343	98.37%	96.51%
stack_gru-10_10_10-relu	2003	97.59%	97.22%
bidirectional_gru-10-relu	1353	No Run	97.74%

Hyperparameter Evaluation

Sequence to Sequence

Evaluated on Simple GRU model on HAPT Dataset -

Best Configuration for Accuracy = 97.67% -

• Dataset Window Size - 350
• Dataset Window Shift Ratio - 50% of 350
• Units - 21
• Activation - tanh

Sequence to Label

Evaluated on 2 Layer Stacked LSTM model on HAPT Dataset -

Best Configuration for Accuracy = 98.93% -

• Dataset Window Size - 350
• Dataset Window Shift Ratio - 10% of 350
• Units - 28 and 10
• Activation - tanh

Ensemble Learning Evaluation

• HAPT Dataset
• Window Size - 250, Windown Shift - 125
• Level 0 models : Optimizer - Adam, Learning Rate - 0.001, Early stopping with metric 'Accuracy', patience 2000, delta 0.0001
• Ensemble model : Optimizer - Adam, Learning Rate - 0.001, Epoch 30k

	simple_rnn-5-relu	simple_lstm-5-relu	simple_gru-5-relu	Ensemble
Sequence to Sequence	92.0%	93.2%	95.5%	96.4%
Sequence to Label	92.4%	95.8%	97.6%	97.9%

	simple_rnn-5-relu	simple_rnn-10-relu	simple_rnn-15-relu	Ensemble
Sequence to Sequence	93.8%	94.7%	94.9%	96.3%
Sequence to Label	93.1%	94.1%	94.7%	96.7%

Knowledge Distillation Evaluation

• HAPT Dataset
• Window Size - 250, Windown Shift - 125, Optimizer - Adam, Learning Rate - 0.001
• Teacher model simple_gru-50-relu : Epoch 15k
• Student model simple_rnn-5-relu : Epoch 1k
• For Sequence to Sequence : Softmax Temperature - 10, Alpha (for Soft Label DistillationLoss) - 0.8, Beta (for Hard Label Student Loss) - 0.2
• For Sequence to Label : Softmax Temperature - 10, Alpha (for Soft Label DistillationLoss) - 0.1, Beta (for Hard Label Student Loss) - 0.9
• Parameter reduction = 9,363 to 138

	Teacher Mode	Student Model from Scratch	Student Model
Sequence to Sequence	97.6%	90.4%	91.0%
Sequence to Label	98.2%	95.8%	90.3%