# A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian Mutation

arXiv: https://arxiv.org/abs/2201.05314 · HTML: https://mlsanity.com/paper/2201.05314/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 14 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1902.09130/) (2019)
- [Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/1904.01189/) (2019)
- [PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition](https://mlsanity.com/paper/1911.12409/) (2019)
- [Memory Attention Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1804.08254/) (2018)
- [JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2011.07787/) (2020)
- [Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning](https://mlsanity.com/paper/2006.11812/) (2020)
- [An adaptive hybrid algorithm for social networks to choose groups with independent members](https://mlsanity.com/paper/1910.01875/) (2019)
- [Energy-efficient topology to enhance the wireless sensor network lifetime using connectivity control](https://mlsanity.com/paper/2005.03370/) (2020)

## Built on by (most cited first, 3 of 3)

- [Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition](https://mlsanity.com/paper/2402.02431/) (2024)
- [Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition](https://mlsanity.com/paper/2307.07791/) (2023)
- [Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion](https://mlsanity.com/paper/2408.02033/) (2024)

## Abstract (arXiv)

Human activity discovery aims to cluster the activities performed by humans without any prior information on what defines each activity. Most methods presented in human activity recognition are supervised, where there are labeled inputs to train the system. In reality, it is difficult to label activities data because of its huge volume and the variety of human activities. This paper proposes an unsupervised framework to perform human activity discovery in 3D skeleton sequences. First, an approach for data pre-processing is presented. In this stage, important frames are selected based on kinetic energy. Next, the displacement of joints, statistical displacements, angles, and orientation features are extracted to represent the activities information. Since not all extracted features have useful information, the dimension of features is reduced using PCA. Most methods proposed for human activity discovery are not fully unsupervised. They use pre-segmented videos before categorizing activities. To deal with this, we have used a sliding time window to segment the time series of activities with some overlapping. Then, activities are discovered by our proposed Hybrid Particle swarm optimization (PSO) with Gaussian Mutation and K-means (HPGMK) algorithm to provide diverse solutions. PSO is used due to its straightforward idea and powerful global search capability which can identify the ideal solution in a few iterations. Finally, k-means is applied to the outcome centroids from each iteration of the PSO to overcome the slow convergence rate of PSO. The experiment results on five datasets show that the proposed framework has superior performance in discovering activities compared to the other state-of-the-art methods and has increased accuracy of at least 4% on average.