Unsupervised Activity Recognition Using Trajectory Heatmaps from Inertial Measurement Unit Data

Orhan Konak, Pit Wegner, Justin Albert, Bert Arnrich

2022

Abstract

The growth of sensors with varying degrees of integration and functionality has inevitably led to their entry into various fields such as digital health. Here, sensors that can record acceleration and rotation rates, so- called Inertial Measurement Units (IMU), are primarily used to distinguish between different activities, also known as Human Activity Recognition (HAR). If the associations of the motion data to the activities are not known, clustering methods are used. There are many algorithmic approaches to identify similarity structures in the incoming sensor data. These differ mainly in their notion of similarity and grouping, as well as in their complexity. This work aimed to investigate the impact of transforming the incoming time-series data into corresponding motion trajectories and trajectory heatmap images before forwarding it to well-known clustering models. All three input variables were given to the same clustering algorithms, and the results were compared using different evaluation metrics. This work shows that transforming sensor data into trajectories and images leads to a significant increase in cluster assignment for all considered models and different metrics.

Paper Citation

in Harvard Style

Konak O., Wegner P., Albert J. and Arnrich B. (2022). Unsupervised Activity Recognition Using Trajectory Heatmaps from Inertial Measurement Unit Data. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-547-0, pages 304-312. DOI: 10.5220/0010838800003116

in Bibtex Style

@conference{icaart22,
author={Orhan Konak and Pit Wegner and Justin Albert and Bert Arnrich},
title={Unsupervised Activity Recognition Using Trajectory Heatmaps from Inertial Measurement Unit Data},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2022},
pages={304-312},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010838800003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Unsupervised Activity Recognition Using Trajectory Heatmaps from Inertial Measurement Unit Data
SN - 978-989-758-547-0
AU - Konak O.
AU - Wegner P.
AU - Albert J.
AU - Arnrich B.
PY - 2022
SP - 304
EP - 312
DO - 10.5220/0010838800003116