Zobrazeno 1 - 10
of 167
pro vyhledávání: '"A. Hölzemann"'
Autor:
M. Mähs, J. S. Pithan, I. Bergmann, L. Gabrys, J. Graf, A. Hölzemann, K. Van Laerhoven, S. Otto-Hagemann, M. L. Popescu, L. Schwermann, B. Wenz, I. Pahmeier, A. Teti
Publikováno v:
Trials, Vol 23, Iss 1, Pp 1-13 (2022)
Abstract Background One relevant strategy to prevent the onset and progression of type 2 diabetes mellitus (T2DM) focuses on increasing physical activity. The use of activity trackers by patients could enable objective measurement of their regular ph
Externí odkaz:
https://doaj.org/article/f01476b548cc4db495221d97722ed3cf
Publikováno v:
MDPI Sensors, 25 June 2023, Special Issue Inertial Measurement Units in Sport
We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist
Externí odkaz:
http://arxiv.org/abs/2305.13124
Publikováno v:
Front. Comput. Sci. - Mobile and Ubiquitous Computing 2024
Research into the detection of human activities from wearable sensors is a highly active field, benefiting numerous applications, from ambulatory monitoring of healthcare patients via fitness coaching to streamlining manual work processes. We present
Externí odkaz:
http://arxiv.org/abs/2305.08752
Autor:
de Souza Tadano, Yara, Potgieter-Vermaak, Sanja, Siqueira, Hugo Valadares, Hoelzemann, Judith J., Duarte, Ediclê S.F., Alves, Thiago Antonini, Valebona, Fabio, Lenzi, Iuri, Godoi, Ana Flavia L., Barbosa, Cybelli, Ribeiro, Igor O., de Souza, Rodrigo A.F., Yamamoto, Carlos I., Santos, Erickson, Fernandesi, Karenn S., Machado, Cristine, Martin, Scot T., Godoi, Ricardo H.M.
Publikováno v:
In Chemosphere November 2024 367
Activity recognition systems that are capable of estimating human activities from wearable inertial sensors have come a long way in the past decades. Not only have state-of-the-art methods moved away from feature engineering and have fully adopted en
Externí odkaz:
http://arxiv.org/abs/2110.06663
In this work we propose a solution to the UbiComp 2021 Challenge by Stabilo in which handwritten mathematical terms are supposed to be automatically classified based on time series sensor data captured on the DigiPen. The input data set contains data
Externí odkaz:
http://arxiv.org/abs/2109.05594
Data augmentation is a widely used technique in classification to increase data used in training. It improves generalization and reduces amount of annotated human activity data needed for training which reduces labour and time needed with the dataset
Externí odkaz:
http://arxiv.org/abs/2109.01081
Recent studies in Human Activity Recognition (HAR) have shown that Deep Learning methods are able to outperform classical Machine Learning algorithms. One popular Deep Learning architecture in HAR is the DeepConvLSTM. In this paper we propose to alte
Externí odkaz:
http://arxiv.org/abs/2108.00702
Publikováno v:
In Games and Economic Behavior
Autor:
de Souza Fernandes Duarte, Ediclê, Lucio, Paulo Sérgio, Costa, Maria João, Salgueiro, Vanda, Salgado, Rui, Potes, Miguel, Hoelzemann, Judith J., Bortoli, Daniele
Publikováno v:
In Environmental Research 1 January 2024 240 Part 2