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 s, while run-time savings of up to 42% are achieved, compared to single sample acquisition.
Highlights
Applications for virtual and augmented reality place exacting requirements to sensor data, especially in case of movement tracking
The sensors completely fulfill the requirements of the presented approach, as they provide a sensor timer as well as a FIFO interface
With the increasing number of sensors that are built into mobile phones, fitness trackers, game controllers, clothes and many other mobile devices, the fusion of the locally available sensor data becomes more and more important
Summary
Applications for virtual and augmented reality place exacting requirements to sensor data, especially in case of movement tracking. In the areas including fitness tracking, activity recognition, gesture detection and many more, the fusion of multiple sensor data streams forms the basis for highly sophisticated feature extraction algorithms. MEMS have become the state-of-the-art technology for extremely small, robust and energy efficient sensor devices. They combine mechanical elements, measuring physical quantities, with traditional integrated circuit components on a single chip [2]. While most modern mobile devices and wearables are equipped with a variety of sensors, more and more people are using applications that are based on the gathered sensor data They are expecting highly accurate results, e.g., from activity recognition, step counting, heart rate measurements, indoor navigation and more. Due to varying communication latencies, the read out sensor data is influenced by individual jitters and offsets
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