SWL-Adapt (AAAI 2023)

This is the official repository for our paper: SWL-Adapt: An Unsupervised Domain Adaptation Model with SampleWeight Learning for Cross-UserWearable Human Activity Recognition.

Dependencies

• python 3.7
• torch == 1.8.0 (with suitable CUDA and CuDNN version)
• higher (https://pypi.org/project/higher/)
• numpy, torchmetrics, scipy, pandas, argparse, sklearn

Datasets

Dataset	Download Link
RealWorld	https://www.uni-mannheim.de/dws/research/projects/activity-recognition/dataset/dataset-realworld/
OPPORTUNITY	https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recognition
SBHAR	http://archive.ics.uci.edu/ml/datasets/Smartphone-Based+Recognition+of+Human+Activities+and+Postural+Transitions

Quick Start

Data preprocessing is included in main.py. Download the datasets and run SWL-Adapt as follows. This gives the performance of each evaluation with each user in the set of new users as the new user, and their average.

python main.py --data_path [/path/to/dataset] --dataset [realWorld, OPPORTUNITY, or PAMAP2]