Feature Selection for Machine Learning Based Step Length Estimation Algorithms An accurate step length estimation can provide valuable information to different applications such as indoor positioning systems or it can be helpful when analyzing the gait of a user, which can then be used to detect various gait impairments that lead to a reduced step length (caused by e.g., Parkinsonxe2x80x99s disease or multiple sclerosis). In this paper, we focus on the estimation of the step length using machine learning techniques that could be used in an indoor positioning system. Previous step length algorithms tried to model the length of a step based on measurements from the accelerometer and some tuneable (userspecific) parameters. Machinelearningbased step length estimation algorithms eliminate these parameters to be tuned. Instead, to adapt these algorithms to different users, it suffices to provide examples of the length of multiple steps for different persons to the machine learning algorithm, so that in the training phase the algorithm can learn to predict the step length for different users. Until now, these machine learning algorithms were trained with features that were chosen intuitively. In this paper, we consider a systematic feature selection algorithm to be able to determine the features from a large collection of features, resulting in the best performance. This resulted in a step length estimator with a mean absolute error of     3.48     cm for a known test person and     4.19     cm for an unknown test person, while current stateoftheart machinelearningbased step length estimators resulted in a mean absolute error of     4.94     cm and     6.27     cm for respectively a known and unknown test person.

Machine learning for assistance systems: patternbased approach to online step recognition Computeraided assistance systems are entering the world of work and production. Such systems utilize augmentedxe2x80x93 and virtualxe2x80x93reality for operator training and live guidance as well as mobile maintenance and support. This is particularly important in the modern production reality of everchanging products and xe2x80x98lot size onexe2x80x99 customization of production.This paper focuses on the application of machine learning approach to extend the functionality of assistance systems. Machine learning provides tools to analyse large amounts of data and extract meaningful information. The goal here is to recognize the movement of an operator which would enable automatic display of instructions relevant to them.We present the challenges facing machine learning applications in humancentered assistance systems and a framework to assess machine learning approaches feasible for this scenario. The approach is assessed on a historical data set and then deployed in a work station for live testing. The posthoc, or historical, analysis yields promising results. The adhoc, or live, analysis is a complex task and the results are affected by multiple factors, most of which are introduced by the human influence.The contribution of this paper is an approach to adapt statexe2x80x93 ofxe2x80x93thexe2x80x93art machine learning to operator movement recognition with a special focus on approaches to spatial time series data preprocessing. Presented experiment results validate the approach and show that it performs well in a realworld scenario.