PERFORM Publications

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Title		Authors	PubMed ID
1	Neural topic modeling on hyperspheres: Spherical representation learning with von Mises-Fisher mixtures	Guo D; Luo Z; Bouguila N; Fan W;	41791177 ENCS
2	Disentangled representation learning for multi-view clustering via von Mises-Fisher hyperspherical embedding	Li Z; Luo Z; Bouguila N; Su W; Fan W;	40664160 ENCS
3	Clustering and Interpretability of Residential Electricity Demand Profiles	Kallel S; Amayri M; Bouguila N;	40218540 ENCS
4	SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting	Zgaren A; Bouachir W; Bouguila N;	39997554 ENCS
5	Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings	Guo J; Fan W; Amayri M; Bouguila N;	39662201 ENCS
6	FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images	Paul S; Patterson Z; Bouguila N;	38535151 ENCS
7	Perceptions of self-monitoring dietary intake according to a plate-based approach: A qualitative study	Kheirmandparizi M; Gouin JP; Bouchaud CC; Kebbe M; Bergeron C; Madani Civi R; Rhodes RE; Farnesi BC; Bouguila N; Conklin AI; Lear SA; Cohen TR;	38015899 PERFORM
8	Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency	Al-Bazzaz H; Azam M; Amayri M; Bouguila N;	37837127 ENCS
9	Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration	Ge B; Najar F; Bouguila N;	37754943 ENCS
10	Human Activity Recognition with an HMM-Based Generative Model	Manouchehri N; Bouguila N;	36772428 ENCS
11	Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications	Luo Z; Amayri M; Fan W; Bouguila N;	36685642 ENCS
12	Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning	Bouhamed O; Amayri M; Bouguila N;	35590880 ENCS
13	Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures	Bourouis S; Pawar Y; Bouguila N;	35009726 ENCS
14	Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images	Bourouis S; Alharbi A; Bouguila N;	34460578 ENCS

Title:	Human Activity Recognition with an HMM-Based Generative Model
Authors:	Manouchehri N, Bouguila N
Link:	https://pubmed.ncbi.nlm.nih.gov/36772428/
DOI:	10.3390/s23031390
Publication:	Sensors (Basel, Switzerland)
Keywords:	hidden Markov models; human activity recognition; medical applications; proportional data; scaled Dirichlet distribution;
PMID:	36772428	Category:	Date Added:	2023-02-11
Dept Affiliation:	ENCS 1 Algorithmic Dynamics Lab, Unit of Computational Medicine, Karolinska Institute, 171 77 Stockholm, Sweden. 2 Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G1T7, Canada.

Description:

Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded-scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.

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