4 papers
Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models
Heiko Oppel, Andreas Spilz, Michael Munz
Denoising Diffusion Probabilistic Models (DDPMs) can generate synthetic timeseries data to help improve the performance of a classifier, but their sampling process is computational…
GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors
Andreas Spilz, Heiko Oppel, Jochen Werner +3
Wearable inertial measurement units (IMUs) provide a cost-effective approach to assessing human movement in clinical and everyday environments. However, developing the associated c…
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs
Heiko Oppel, Andreas Spilz, Michael Munz
Denoising diffusion probabilistic models are able to generate synthetic sensor signals. The training process of such a model is controlled by a loss function which measures the dif…
IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems
Heiko Oppel, Michael Munz
Kinematic sensors are often used to analyze movement behaviors in sports and daily activities due to their ease of use and lack of spatial restrictions, unlike video-based motion c…