Unsupervised Diffusion Model for Sensor-based Human Activity Recognition

Si Zuo, Vitor Fortes, Sungho Suh, Stephan Sigg, Paul Lukowicz

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaAbstractScientificvertaisarvioitu

Abstrakti

Recognizing human activities from sensor data is a vital task in various domains, but obtaining diverse and labeled sensor data remains challenging and costly. In this paper, we propose an unsupervised statistical feature-guided diffusion model for sensor-based human activity recognition. The proposed method aims to generate synthetic time-series sensor data without relying on labeled data, addressing the scarcity and annotation difficulties associated with real-world sensor data. By conditioning the diffusion model on statistical information such as mean, standard deviation, Z-score, and skewness, we generate diverse and representative synthetic sensor data. We conducted experiments on public human activity recognition datasets and compared the proposed method to conventional oversampling methods and state-of-the-art generative adversarial network methods. The experimental results demonstrate that the proposed method can improve the performance of human activity recognition and outperform existing techniques.
AlkuperäiskieliEnglanti
Sivut205
Sivumäärä1
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiEi oikeutettu
TapahtumaACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers - Cancun, Meksiko
Kesto: 8 lokak. 202312 lokak. 2023

Conference

ConferenceACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers
LyhennettäUbiComp/ISWC
Maa/AlueMeksiko
KaupunkiCancun
Ajanjakso08/10/202312/10/2023

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