3 research outputs found
Towards Machine Learning on data from Professional Cyclists
Professional sports are developing towards increasingly scientific training
methods with increasing amounts of data being collected from laboratory tests,
training sessions and competitions. In cycling, it is standard to equip
bicycles with small computers recording data from sensors such as power-meters,
in addition to heart-rate, speed, altitude etc. Recently, machine learning
techniques have provided huge success in a wide variety of areas where large
amounts of data (big data) is available. In this paper, we perform a pilot
experiment on machine learning to model physical response in elite cyclists. As
a first experiment, we show that it is possible to train a LSTM machine
learning algorithm to predict the heart-rate response of a cyclist during a
training session. This work is a promising first step towards developing more
elaborate models based on big data and machine learning to capture performance
aspects of athletes.Comment: Accepted for the 12th World Congress on Performance Analysis of
Sports, Opatija, Croatia, 201
SHIBR-The Swedish Historical Birth Records : a semi-annotated dataset
This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms' performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child's first name, birth date, date of baptism, father's first/last name, mother's first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.open access</p