Advanced Debugging Architecture for Smart Inertial Sensors using Sensor-in-the-Loop

Gis, Daniel; Büscher, Nils; Haubelt, Christian

doi:10.1109/rsp51120.2020.9244851

Cited by 4 publications

(10 citation statements)

References 6 publications

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“…For the computational effort, all algorithms were executed and evaluated on a real device using the Sensor-in-the-Loop (SiL) architecture from [ 29 ] using a set of prerecorded human motion data to ensure comparable results. The software of the smart sensor has been adapted to process previously recorded data injected from a host computer via a debug interface into the

C and process it as if the data were delivered by the inertial sensors.…”

Section: Background and Methodsmentioning

confidence: 99%

On the Functional and Extra-Functional Properties of IMU Fusion Algorithms for Body-Worn Smart Sensors

Büscher

Gis

Kühn

et al. 2021

Sensors

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In this work, four sensor fusion algorithms for inertial measurement unit data to determine the orientation of a device are assessed regarding their usability in a hardware restricted environment such as body-worn sensor nodes. The assessment is done for both the functional and the extra-functional properties in the context of human operated devices. The four algorithms are implemented in three data formats: 32-bit floating-point, 32-bit fixed-point and 16-bit fixed-point and compared regarding code size, computational effort, and fusion quality. Code size and computational effort are evaluated on an ARM Cortex M0+. For the assessment of the functional properties, the sensor fusion output is compared to a camera generated reference and analyzed in an extensive statistical analysis to determine how data format, algorithm, and human interaction influence the quality of the sensor fusion. Our experiments show that using fixed-point arithmetic can significantly decrease the computational complexity while still maintaining a high fusion quality and all four algorithms are applicable for applications with human interaction.

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C and process it as if the data were delivered by the inertial sensors.…”

Section: Background and Methodsmentioning

confidence: 99%

On the Functional and Extra-Functional Properties of IMU Fusion Algorithms for Body-Worn Smart Sensors

Büscher

Gis

Kühn

et al. 2021

Sensors

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“…Smart sensors can be classified into three different integration levels. A detailed description of the integration levels System On Chip (SoC), SiP and System on Module (SoM) can be found in [ 6 ]. Our proposed SiL concept is suitable for all three kinds of smart sensor implementations and combinations of them.…”

Section: Smart Sensorsmentioning

confidence: 99%

“…In [ 6 ] we show that the memory overhead of the SiL is negligible, the example implementation uses only around 0.63% of the flash memory.…”

Section: Sensor-in-the-loopmentioning

confidence: 99%

“…Both methods of sensor system evaluation, have their advantages, but also their drawbacks. To overcome the drawbacks, we proposed a novel approach in [ 6 ] which can be compared to a Hardware-in-the-Loop (HiL) method [ 7 ]. This approach aims to combine all positive aspects of the previously described methods and uses real hardware in a prototype or on an evaluation board.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the jitter in the time series of the sensor data has a significant influence on the quality of the functional properties [ 8 ]. Since in [ 6 ] we only considered the functionality of the system and did a first performance estimation, in this article we will do a proper analysis of the timing behavior. Furthermore, we will investigate the influence of the jitter on said system.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of Timing Behavior and Jitter in a Smart Inertial Sensor Debugging Architecture

Gis

Büscher

Haubelt

2021

Sensors

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Due to upcoming higher integration levels of microprocessors, the market of inertial sensors has changed in the last few years. Smart inertial sensors are becoming more and more important. This type of sensor offers the benefit of implementing sensor-processing tasks directly on the sensor hardware. The software development on such sensors is quite challenging. In this article, we propose an approach for using prerecorded sensor data during the development process to test and evaluate the functionality and timing of the sensor firmware in a repeatable and reproducible way on the actual hardware. Our proposed Sensor-in-the-Loop architecture enables the developer to inject sensor data during the debugging process directly into the sensor hardware in real time. As the timing becomes more critical in future smart sensor applications, we investigate the timing behavior of our approach with respect to timing and jitter. The implemented approach can inject data of three 3-DOF sensors at 1.6 kHz. Furthermore, the jitter shown in our proposed sampling method is at least three times lower than using real sensor data. To prove the statistical significance of our experiments, we use a Gage R&R analysis, extended by the assessment of confidence intervals of our data.

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Data Augmentation Framework for Smart Sensor System Development Using the Sensor-in-the-Loop Prototyping Platform

Buscher,

Gis,

Wolff

et al. 2021

2021 IEEE International Workshop on Rapid System Prototyping (RSP)

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Advanced Debugging Architecture for Smart Inertial Sensors using Sensor-in-the-Loop

Cited by 4 publications

References 6 publications

On the Functional and Extra-Functional Properties of IMU Fusion Algorithms for Body-Worn Smart Sensors

On the Functional and Extra-Functional Properties of IMU Fusion Algorithms for Body-Worn Smart Sensors

Investigation of Timing Behavior and Jitter in a Smart Inertial Sensor Debugging Architecture

Data Augmentation Framework for Smart Sensor System Development Using the Sensor-in-the-Loop Prototyping Platform

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