Although ultra wideband (UWB) positioning is considered as one of the most promising solutions for indoor positioning due to its high positioning accuracy, the accuracy in situations with a large number of users will be reduced because the time between two UWB position updates can be very high. To obtain a position estimate in between these updates, we can combine the UWB positioning with a different technology, e.g., an inertial measurement unit (IMU) that captures data from an accelerometer, gyroscope, and magnetometer. Previous research using the IMU outputs for location-based services employs the periodic behaviour of the accelerometer signal to count steps. However, most of these algorithms require extensive manual tuning of multiple parameters to obtain satisfactory accuracy. To overcome these practical issues, step counting algorithms using machine learning (ML) principles can be developed. In this paper, we consider accelerometer-based step counters using ML. As the performance and complexity of such algorithms depend on the features used in the training and inference phase, proper selection of the employed features is important. Therefore, in this paper, we propose a novel feature selection algorithm, where we first rank the features based on their Bhattacharyya coefficients and then systematically construct a subset of these ranked features. In this paper, we compare three ranking approaches and apply our algorithm to different ML algorithms employing an experimental set. Although the performance of the evaluated combinations slightly varies for different ML algorithms, their performance is comparable to state-of-the-art step counters, without the need to tune parameters manually.
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