@article{DuekingHolmbergSperlich2017,
  author    = {D{\"u}king, Peter and Holmberg, Hans-Christer and Sperlich, Billy},
  title     = {Instant Biofeedback Provided by Wearable Sensor Technology Can Help to Optimize Exercise and Prevent Injury and Overuse},
  series = {Frontiers in Physiology},
  volume    = {8},
  journal   = {Frontiers in Physiology},
  number    = {167},
  doi       = {10.3389/fphys.2017.00167},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158044},
  year      = {2017},
  language  = {en}
}
@article{DuekingHothoHolmbergetal.2016,
  author    = {D{\"u}king, Peter and Hotho, Andreas and Holmberg, Hans-Christer and Fuss, Franz Konstantin and Sperlich, Billy},
  title     = {Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies},
  series = {Frontiers in Physiology},
  volume    = {7},
  journal   = {Frontiers in Physiology},
  number    = {71},
  doi       = {10.3389/fphys.2016.00071},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-165516},
  year      = {2016},
  abstract  = {Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.},
  language  = {en}
}
@techreport{FundaMarinGarciaGermanetal.2023,
  type      = {Working Paper},
  author    = {Funda, Christoph and Mar{\´i}n Garc{\´i}a, Pablo and German, Reinhard and Hielscher, Kai-Steffen},
  title     = {Online Algorithm for Arrival \& Service Curve Estimation},
  series = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  journal   = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  doi       = {10.25972/OPUS-32211},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322112},
  pages     = {5},
  year      = {2023},
  abstract  = {This paper presents a novel concept to extend state-of-the-art buffer monitoring with additional measures to estimate service-curves. The online algorithm for service-curve estimation replaces the state-of-the-art timestamp logging, as we expect it to overcome the main disadvantages of generating a huge amount of data and using a lot of CPU resources to store the data to a file during operation. We prove the accuracy of the online-algorithm offline with timestamp data and compare the derived bounds to the measured delay and backlog. We also do a proof-of- concept of the online-algorithm, implement it in LabVIEW and compare its performance to the timestamp logging by CPU load and data-size of the log-file. However, the implementation is still work-in-progress.},
  language  = {en}
}
@article{PfisterFoerster2022,
  author    = {Pfister, Roland and Foerster, Anna},
  title     = {How to measure post-error slowing: The case of pre-error speeding},
  series = {Behavior Research Methods},
  volume    = {54},
  journal   = {Behavior Research Methods},
  number    = {1},
  issn      = {1554-3528},
  doi       = {10.3758/s13428-021-01631-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-273244},
  pages     = {435-443},
  year      = {2022},
  abstract  = {Post-error slowing is one of the most widely employed measures to study cognitive and behavioral consequences of error commission. Several methods have been proposed to quantify the post-error slowing effect, and we discuss two main methods: The traditional method of comparing response times in correct post-error trials to response times of correct trials that follow another correct trial, and a more recent proposal of comparing response times in correct post-error trials to the corresponding correct pre-error trials. Based on thorough re-analyses of two datasets, we argue that the latter method provides an inflated estimate by also capturing the (partially) independent effect of pre-error speeding. We propose two solutions for improving the assessment of human error processing, both of which highlight the importance of distinguishing between initial pre-error speeding and later post-error slowing.},
  language  = {en}
}