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.
To achieve a good estimate of the power consumption of an embedded system, including its firmware, is a crucial step in the development of systems with a severely constrained power supply. This is especially true for cases where the device is powered by a small battery or through energy harvesting. The state-of-the-art approaches to measure or estimate the power consumption are formal methods, using power debugging tools with the real hardware or simulation based estimations. In the work at hand, a novel method to estimate the power consumption is proposed, it utilizes the sensor-in-the-loop architecture and enhancing it with a power estimation functionality. The proposed method combines the benefits of former methods, allowing for run-time analysis of the power-consumption in a reproducible way using recorded data without the need for power debugging hardware. In the experiments it is shown that, once set up, the proposed method is able to estimate the power consumption with an error of less than 1 % compared to a power debugging hardware. Thus, the proposed method provides a reliable and fast way to estimate the systems power consumption.
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