Accurate Sample Time Reconstruction for Sensor Data Synchronization

Stieber, Sebastian; Dorsch, Rainer G.; Haubelt, Christian

doi:10.1007/978-3-319-30695-7_14

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…Furthermore, the influence of communication jitter on the measured amount of relative clock drift between the host and the sensor devices is reduced by using low-pass filter mechanisms. To prove our simulated findings in [ 1 ], we performed experiments with real sensors and improved the metric for a better comparability of the results.…”

Section: Related Workmentioning

confidence: 97%

“…Hence, the approach includes the reconstruction of the single sensor samples from the FIFO stream as well as their drift and offset corrected sampling time stamps. In addition to our previous work in [ 1 ], the ASTR is refined by considering the bandwidth of different underlying communication systems in order to compensate timing offset errors between multiple sensor devices. Furthermore, the influence of communication jitter on the measured amount of relative clock drift between the host and the sensor devices is reduced by using low-pass filter mechanisms.…”

Section: Related Workmentioning

confidence: 99%

“…Applications for virtual and augmented reality place exacting requirements to sensor data, especially in case of movement tracking. In our previous work, we identified the negative influence of inaccurate time stamps onto subsequent processing algorithms [ 1 ]. However, 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.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Sample Time Reconstruction of Inertial FIFO Data

Stieber

Dorsch

Haubelt

2017

Sensors

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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.

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Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate Sample Time Reconstruction of Inertial FIFO Data

Stieber

Dorsch

Haubelt

2017

Sensors

Self Cite

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“…Indeed, when trying to use data collected from multiple sensors for location estimation (and activity recognition), a noticeable problem is sensor synchronization: most of the time, the clocks of the emitting sensors and devices involved are not completely synchronized. The approach proposed in [ 17 ] assumes that multiple sensors—each with its own clock and all connected to a single host—collect their timestamped observations in a FIFO (First-In-First-Out) structure; the host fetches the sensor data and performs a reconstruction of the sensor sample times, assuming a constant drift for each sensor and deterministic communication times. In our work, synchronization is not explicitly solved at the data-collection step; instead, we introduce the concept of a time “window” which abstracts the timestamp units at a coarser granularity and allows our method to ignore the imperfect synchronization of the data sources/sensors.…”

Section: Related Workmentioning

confidence: 99%

Sensor-Data Fusion for Multi-Person Indoor Location Estimation

Mohebbi

Stroulia

Nikolaidis

2017

Sensors

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We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other “wearable” device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors’ coverage of the monitored space and the quality of the location estimates.

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“…We will evaluate, which sensor synchronisation techniques are applicable in our scenario, regarding accuracy of the synchronisation and the delays caused by such synchronisation techniques. A possible approach would be the timestamp reconstruction described in [15], performed centrally on the host platform.…”

Section: Synchronizationmentioning

confidence: 99%

Fast care – real-time sensor data analysis framework for intelligent assistance systems

Hein

Grützmacher

Haubelt

et al. 2017

Current Directions in Biomedical Engineering

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Main target of fast care is the development of a real-time capable sensor data analysis framework for intelligent assistive systems in the field of Ambient Assisted Living, eHealth, Tele Rehabilitation, and Tele Care. The aim is to provide a medically valid integrated situation model based on a distributed, ad-hoc connected, energy-efficient sensor infrastructure suitable for daily use. The integrated situation model combining physiological, cognitive, and kinematic information about the patient is grounded on the intelligent fusion of heterogeneous sensor data on different levels. The model can serve as a tool for quickly identifying risk and hazards as well as enable medical assistance systems to autonomously intervene in real-time and actively give telemedical feedback.

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Accurate Sample Time Reconstruction for Sensor Data Synchronization

Cited by 3 publications

References 8 publications

Accurate Sample Time Reconstruction of Inertial FIFO Data

Accurate Sample Time Reconstruction of Inertial FIFO Data

Sensor-Data Fusion for Multi-Person Indoor Location Estimation

Fast care – real-time sensor data analysis framework for intelligent assistance systems

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