Abstract
There is a need to develop more advanced tools to improve guidance on physical exercise to reduce risk of adverse events and improve benefits of exercise. Vast amounts of data are generated continuously by Personal Monitoring Devices (PMDs) from sports events, biomedical experiments, and fitness self-monitoring that may be used to guide physical exercise. Most of these data are sampled as time- or distance-series. However, the inherent high-dimensionality of exercise data is a challenge during processing. As a result, current data analysis from PMDs seldomly extends beyond aggregates. Common challanges are:•alterations in data density comparing the time- and the distance domain;•large intra and interindividual variations in the relationship between numerical data and physiological properties;•alterations in temporal statistical properties of data derived from exercise of different exercise durations. These challenges are currently unresolved leading to suboptimal analytic models. In this paper, we present algorithms and approaches to address these problems, allowing the analysis of complete PMD datasets, rather than having to rely on cumulative statistics. Our suggested approaches permit effective application of established Symbolic Aggregate Approximation modeling and newer deep learning models, such as LSTM.