Weak-Annotation of HAR Datasets using Vision Foundation Models

TL;DR

This paper introduces a clustering-based weak annotation method using vision foundation models to efficiently label wearable-based human activity recognition datasets, achieving near-supervised accuracy with minimal human effort.

Contribution

The paper presents a novel annotation pipeline leveraging vision foundation models and clustering to reduce manual labeling effort in HAR datasets, achieving high accuracy.

Findings

Achieves ~90% labeling accuracy with minimal human annotation

Matches fully-supervised classifier performance using weakly annotated data

Reduces annotation time and effort significantly

Abstract

As wearable-based data annotation remains, to date, a tedious, time-consuming task requiring researchers to dedicate substantial time, benchmark datasets within the field of Human Activity Recognition in lack richness and size compared to datasets available within related fields. Recently, vision foundation models such as CLIP have gained significant attention, helping the vision community advance in finding robust, generalizable feature representations. With the majority of researchers within the wearable community relying on vision modalities to overcome the limited expressiveness of wearable data and accurately label their to-be-released benchmark datasets offline, we propose a novel, clustering-based annotation pipeline to significantly reduce the amount of data that needs to be annotated by a human annotator. We show that using our approach, the annotation of centroid clips suffices to achieve average labelling accuracies close to 90% across three publicly available HAR benchmark datasets. Using the weakly annotated datasets, we further demonstrate that we can match the accuracy scores of fully-supervised deep learning classifiers across all three benchmark datasets. Code as well as supplementary figures and results are publicly downloadable via github.com/mariusbock/weak_har.