Papers › Efficient Deep Clustering of Human Activities and How to Improve Evaluation

Efficient Deep Clustering of Human Activities and How to Improve Evaluation

17 Sep 2022Asian Conference on Machine Learning 2023 1arXiv:2209.08335archive 2025-07-28

Louis Mahon, Thomas Lukasiewicz

There has been much recent research on human activity re\-cog\-ni\-tion (HAR), due to the proliferation of wearable sensors in watches and phones, and the advances of deep learning methods, which avoid the need to manually extract features from raw sensor signals. A significant disadvantage of deep learning applied to HAR is the need for manually labelled training data, which is especially difficult to obtain for HAR datasets. Progress is starting to be made in the unsupervised setting, in the form of deep HAR clustering models, which can assign labels to data without having been given any labels to train on, but there are problems with evaluating deep HAR clustering models, which makes assessing the field and devising new methods difficult. In this paper, we highlight several distinct problems with how deep HAR clustering models are evaluated, describing these problems in detail and conducting careful experiments to explicate the effect that they can have on results. We then discuss solutions to these problems, and suggest standard evaluation settings for future deep HAR clustering models. Additionally, we present a new deep clustering model for HAR. When tested under our proposed settings, our model performs better than (or on par with) existing models, while also being more efficient and better able to scale to more complex datasets by avoiding the need for an autoencoder.

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Lou1sM/HAR officialpytorch report

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Tasks

Clustering Deep Clustering Human Activity Recognition Image Clustering

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Human Activity Recognition	PAMAP2	Selective HAR Clustering	ARI	0.814	#1 of 3	Archive leaderboard	report
Human Activity Recognition	PAMAP2	Selective HAR Clustering	NMI	0.884	#1 of 3	Archive leaderboard	report
Image Clustering	HAR	Selective HAR Clustering	Accuracy	0.753	#3 of 3	Archive leaderboard	report
Image Clustering	HAR	Selective HAR Clustering	NMI	0.760	#3 of 3	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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