DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition

Marius Bock, Michael Moeller, Kristof Van Laerhoven

Abstract

Despite recognized limitations in modeling long-range temporal dependencies, Human Activity Recognition (HAR) has traditionally relied on a sliding window approach to segment labeled datasets. Deep learning models like the DeepConvLSTM typically classify each window independently, thereby restricting learnable temporal context to within-window information. To address this constraint, we propose DeepConvContext, a multi-scale time series classification framework for HAR. Drawing inspiration from the vision-based Temporal Action Localization community, DeepConvContext models both intra- and inter-window temporal patterns by processing sequences of time-ordered windows. Unlike recent HAR models that incorporate attention mechanisms, DeepConvContext relies solely on LSTMs – with ablation studies demonstrating the superior performance of LSTMs over attention-based variants for modeling inertial sensor data. Across six widely-used HAR benchmarks, DeepConvContext achieves an average 10% improvement in F1-score over the classic DeepConvLSTM, with gains of up to 21%. Code to reproduce our experiments is publicly available via http://github.com/mariusbock/context_har.

Resources

[arxiv] [github]

Bibtex

@article{bock2025deepcovncontext,
author={Bock, Marius and Moeller, Michael and Van Laerhoven, Kristof},
title = {DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition},
year = {2025},
volume = {abs/2505.20894},
journal = {CoRR},
doi = {10.48550/arXiv.2505.20894},
url={https://doi.org/10.48550/arXiv.2505.20894} }