Creating and benchmarking a new dataset for physical activity monitoring

By Attila Reiss, Didier Stricker. Petra, 2012

300 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Physiology

View Paper: https://www.semanticscholar.org/paper/a087916314b7986ac11510dc2e26a812431675f5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Kristof Van Laerhoven
2. Philip Schmidt
3. Attila Reiss
4. Ina Indlekofer

In Computer Vision and Pattern Recognition

1. Boudewijn P. F. Lelieveldt
2. Elmar Eisemann
3. Anna Vilanova
4. Nicola Pezzotti
5. Thomas Höllt
6. Kristof Van Laerhoven
7. Philip Schmidt
8. Jason H. Moore

Citations in the corpus

• Approximated and User Steerable tSNE for Progressive Visual Analytics (2015, methodology): “activity monitoring [46] that comprises readings of three Inertial Measurement Units (IMU) and a heart rate monitor applied to nine different subjects.”
• PMLB: a large benchmark suite for machine learning evaluation and comparison (2017, background): “…will always be a need to identify and generate custom benchmarks for new or specialized problem domains, e.g. physical activity monitoring data Reiss and Stricker (2012) or dynamical systems simulation La Cava et al. (2016), it is vital for the bioinformatics and ML community to have a…”
• Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks (2019, background): “Large performance difference between subject/session-dependent and -independent evaluation has been reported for example in the human activity recognition domain [41].”