Enhancing Accuracy in Actigraphic Measurements: A Lightweight Calibration Method for Triaxial Accelerometers

This paper presents a simple, lightweight, automatic calibration method for low-cost triaxial accelerometers, utilizing the Earth’s gravitational constant in various orientations. It can be easily implemented using only fixed-point arithmetic and can run on low-power microcontrollers for real-time measurements, making it practical for scenarios with limited data storage and computational power, such as actigraphy or IoT applications. The method offers ease of use by automatically detecting motionless intervals, eliminating the need for complex positioning techniques. The procedure detects resting states and calculates the corresponding three-dimensional mean acceleration values during the measurement. After appropriately selecting these mean values, a set of calibration points is formed and passed to a gradient-based optimization algorithm for iterative estimation of the calibration coefficients. Different metrics were used for verification and comparison with other methods, which were calculated through simulations and tests based on real measurements. The results show that, despite its lightweight nature, the method performs equally to more complex solutions. This article provides a thorough explanation of a novel method for collecting calibration points, the optimization algorithm, and the methods used for performance evaluation in a reproducible manner.

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Downhole Microseismic Monitoring Using Time-Division Multiplexed Fiber-Optic Accelerometer Array

Microseismic monitoring is of importance for several geoscience research aspects and for applications in oil and gas industry. For signals generated by the ultra-weak microseismic events, conventional moving-coil geophone systems have reached their limit in detection sensitivity especially at high frequency range. Here we for the first time present a specially tailored fiber-optic sensing system targeting at downhole microseismic monitoring. The system contains 30 individual interferometric accelerometers and 2 reference sensors, which are time-division multiplexed into a 12-level vector seismic sensor array. The multiplexed accelerometers can achieve ~50 ng/√Hz noise equivalent acceleration, which is superior to the commercial available moving-coil geophone systems at frequencies above 200 Hz. The measured sensitivity of the accelerometers can reach ~200 rad/g from 10 Hz to 1 kHz. The dynamic range is above 134 dB over the same frequency range and is higher than its electronic counterpart in the low frequency band. Moreover, the sensors can function properly under the harsh condition of 120 °C temperature and 40 MPa pressure over the 4-hour test duration. The sensor array along with the interrogator has been running uninterruptedly over 3 weeks in a multi-stage hydraulic fracturing stimulation field test. On-site results show that our system can clearly resolve the vector nature of both compressional and shear waves generated by the microseismic events.

Published in the IEEE Photonics Society Section within IEEE Access.

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Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living


Smartphones and smartwatches, which include powerful sensors, provide a readily available platform for implementing and deploying mobile motion-based behavioral biometrics. However, the few studies that utilize these commercial devices for motion-based biometrics are quite limited in terms of the sensors and physical activities that they evaluate. In many such studies, only the smartwatch accelerometer is utilized and only one physical activity, walking, is investigated. In this study we consider the accelerometer and gyroscope sensor on both the smartphone and smartwatch, and determine which combination of sensors performs best. Furthermore, eighteen diverse activities of daily living are evaluated for their biometric efficacy and, unlike most other studies, biometric identification is evaluated in addition to biometric authentication. The results presented in this article show that motion-based biometrics using smartphones and/or smartwatches yield good results, and that these results hold for the eighteen activities. This suggests that zero-effort continuous biometrics based on normal activities of daily living is feasible, and also demonstrates that certain easy-to-perform activities, such as clapping, may be a viable alternative (or supplement) to gait-based biometrics.

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