Human Motion Detection Using Sharpened Dimensionality Reduction and Clustering

TL;DR

This paper introduces a method combining sharpened dimensionality reduction with clustering algorithms to improve labeling and analysis of high-dimensional human activity data, demonstrating superior performance over traditional methods.

Contribution

The paper proposes integrating SDR with clustering techniques, especially k-means, for effective labeling of high-dimensional data projections, validated on synthetic and real-world datasets.

Findings

Clustering SDR results improves label accuracy over plain DR.

K-means outperforms other clustering methods in SDR applications.

The proposed pipeline is scalable and effective for human activity data analysis.

Abstract

Sharpened dimensionality reduction (SDR), which belongs to the class of multidimensional projection techniques, has recently been introduced to tackle the challenges in the exploratory and visual analysis of high-dimensional data. SDR has been applied to various real-world datasets, such as human activity sensory data and astronomical datasets. However, manually labeling the samples from the generated projection are expensive. To address this problem, we propose here to use clustering methods such as k-means, Hierarchical Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Spectral Clustering to easily label the 2D projections of high-dimensional data. We test our pipeline of SDR and the clustering methods on a range of synthetic and real-world datasets, including two different public human activity datasets extracted from smartphone accelerometer or gyroscope recordings of various movements. We apply clustering to assess the visual cluster separation of SDR, both qualitatively and quantitatively. We conclude that clustering SDR results yields better labeling results than clustering plain DR, and that k-means is the recommended clustering method for SDR in terms of clustering accuracy, ease-of-use, and computational scalability.