Accurate Sample Time Reconstruction of Inertial FIFO Data

Abstract

In the context of modern cyber-physical systems, the accuracy of underlying sensor data plays an increasingly important role in sensor data fusion and feature extraction. The raw events of multiple sensors have to be aligned in time to enable high quality sensor fusion results. However, the growing number of simultaneously connected sensor devices make the energy saving data acquisition and processing more and more difficult. Hence, most of the modern sensors offer a first-in-first-out (FIFO) interface to store multiple data samples and to relax timing constraints, when handling multiple sensor devices. However, using the FIFO interface increases the negative influence of individual clock drifts—introduced by fabrication inaccuracies, temperature changes and wear-out effects—onto the sampling data reconstruction. Furthermore, additional timing offset errors due to communication and software latencies increases with a growing number of sensor devices. In this article, we present an approach for an accurate sample time reconstruction independent of the actual clock drift with the help of an internal sensor timer. Such timers are already available in modern sensors, manufactured in micro-electromechanical systems (MEMS) technology. The presented approach focuses on calculating accurate time stamps using the sensor FIFO interface in a forward-only processing manner as a robust and energy saving solution. The proposed algorithm is able to lower the overall standard deviation of reconstructed sampling periods below 40 μs, while run-time savings of up to 42% are achieved, compared to single sample acquisition.

Explaining the Ergonomic Assessment of Human Movement in Industrial Contexts

“…Unsuccessful temporal synchronisation of different sensors can lead to shifted or stretched signals which would blur events [86]. Consequently, it would compromise the results of sensor fusion and distort the subsequent signal analysis.…”

“…Consequently, it would compromise the results of sensor fusion and distort the subsequent signal analysis. Often, this problem arises not only from fabrication discrepancy, wear-out results and temperature variations but also from devices' clocks, which can drift and affect the sample timing and, variations in communication latencies [86,87]. Therefore, for allowing a high quality of multiple sensor fusion, synchronisation must be ensured.…”