Modeling and Calibration for Dithering of MDRLG and Time-Delay of Accelerometer in SINS

At present, the design and manufacturing technology of mechanically dithered ring laser gyroscope (MDRLG) have matured, the strapdown inertial navigation systems (SINS) with MDRLG have been widely used in military and business scope. When the MDRLG is working, high-frequency dithering is introduced, which will cause the size effect error of the accelerometer. The accelerometer signal has a time delay relative to the system, which will cause the accelerometer time delay error. In this article, in order to solve the above-mentioned problem: (1) we model the size effect error of the mechanically dithering of the MDRLG and perform an error analysis for the size effect error of the mechanically dithering of the MDRLG; (2) we model the time delay error of accelerometer and perform an error analysis for the time delay error of accelerometer; (3) we derive a continuous linear 43-D SINS error model considering the above-mentioned two error parameters and expand the temperature coefficients of accelerometers, inner lever arm error, outer lever arm error parameters to achieve high-precision calibration of SINS. We use the piecewise linear constant system (PWCS ) method during the calibration process to prove that all calibration parameters are observable. Finally, the SINS with MDRLG is used in laboratory conditions to test the validity of the calibration method.

Demonstrating the first postulate of relativity in a lift

The first of the two postulates of relativity states that the laws of physics are the same in all inertial reference frames. Often it is assumed that the postulates are mainly concerned with objects moving at a significant fraction of the speed of light. However, the postulates are applicable at all speeds from a snail to a photon. To practically demonstrate the first postulate, the time for a ball to drop a known distance was measured in a stationary and moving lift. An accelerometer app on an iPhone 7 was used to measure the vertical acceleration while the lift travelled between floors and verified that the lift ascended and descended at a constant speed when the ball was dropped. The slow-motion feature of the iPhone 7 (240 fps) was used to capture videos of the falling ball. The number of frames for the ball to fall in a stationary, descending, and ascending lift was respectively 102.4 ± 0.55 , 102.3 ± 1.64 , 99.8 ± 4.21. A t-test revealed no significant difference between these values, confirming the validity of the first postulate. The accelerometer signal was integrated to estimate the average speed of the lift between the bottom and top floor, which was then used to estimate the height difference. An electronic balance placed on the floor of the lift was used to demonstrate the first postulate and the equivalence principle of General Relativity that states that gravitational and inertial mass are equivalent.

Technical note: Turbulence measurements from a Light Autonomous Underwater Vehicle

A self-contained turbulence instrument from Rockland Scientific was installed on a Light Autonomous Underwater Vehicle (AUV) from OceanScan Marine Systems and Technology Lda. We report on the data quality and discuss limitations of dissipation estimated from two shear probes during a deployment in the Barents Sea in February 2021. The AUV mission lasted for 5 hours, operating at a typical horizontal speed of 1.2 m s−1. The AUV was programmed to find and cross the maximum along-path thermal gradient at 10, 20 and 30 m depths along 4 km transects. Although the AUV vibrations contaminate the shear probe records, the noise is mitigated by removing vibration-induced components from shear spectra using accelerometer signal measured in multiple directions. Dissipation rate estimates in the observed transects varied in the range 1 × 10−8 and 6 × 10−6 W kg−1, with the values from the two orthogonal probes typically in agreement to within a factor of 2. Dissipation estimates from the AUV show good agreement with nearby vertical microstructure profiles obtained from the ship during the transects, indicating that the turbulence measurements from the AUV are reliable for this relatively turbulent environment. However, the lowest reliable dissipation rates are limited to 5 × 10−8 W kg−1, making this setup unfit for use in quiescent environments.

Evaluating Pedometer Algorithms on Semi-Regular and Unstructured Gaits

Pedometers are popular for counting steps as a daily measure of physical activity, however, errors as high as 96% have been reported in previous work. Many reasons for pedometer error have been studied, including walking speed, sensor position on the body and pedometer algorithm, demonstrating some differences in error. However, we hypothesize that the largest source of error may be due to differences in the regularity of gait during different activities. During some activities, gait tends to be regular and the repetitiveness of individual steps makes them easy to identify in an accelerometer signal. During other activities of everyday life, gait is frequently semi-regular or unstructured, which we hypothesize makes it difficult to identify and count individual steps. In this work, we test this hypothesis by evaluating the three most common types of pedometer algorithm on a new data set that varies the regularity of gait. A total of 30 participants were video recorded performing three different activities: walking a path (regular gait), conducting a within-building activity (semi-regular gait), and conducting a within-room activity (unstructured gait). Participants were instrumented with accelerometers on the wrist, hip and ankle. Collectively, 60,805 steps were manually annotated for ground truth using synchronized video. The main contribution of this paper is to evaluate pedometer algorithms when the consistency of gait changes to simulate everyday life activities other than exercise. In our study, we found that semi-regular and unstructured gaits resulted in 5–466% error. This demonstrates the need to evaluate pedometer algorithms on activities that vary the regularity of gait. Our dataset is publicly available with links provided in the introduction and Data Availability Statement.

Characterization Of Human Stability Using Vector Acceleration Signals

Biomedical signals carry information about a physiological event. The part of the signal pertaining to a specific event is called an epoch. Once the event has been determined, the corresponding waveform may be segmented and analyzed based on many parameters[1]. As falls have increased in recent years due to an aging population, it is important to gain insight to the reaction of an individual to perturbations. One common method of studying human reaction is by using a balance aperture. This thesis describes the physical actions that produce acceleration on a balance apparatus and captures the acceleration on an accelerometer. Algorithms were developed to segment the unstable periods of the accelerometer signal. Wavelets were used as well as non-linear filters. The non-linear filters increased the amplitudes of periods of instability, simple signal models of the output of the non-linear filters where formulated and analyzed. Vector processing techniques were also developed. The experimental results demonstrate that the acceleration during unstable periods can be differentiated by its frequency content, by its discontinuous nature and by using vector relationships. The algorithms were tested with five individuals and had over 80% accuracy.

Characterization Of Human Stability Using Vector Acceleration Signals

Biomedical signals carry information about a physiological event. The part of the signal pertaining to a specific event is called an epoch. Once the event has been determined, the corresponding waveform may be segmented and analyzed based on many parameters[1]. As falls have increased in recent years due to an aging population, it is important to gain insight to the reaction of an individual to perturbations. One common method of studying human reaction is by using a balance aperture. This thesis describes the physical actions that produce acceleration on a balance apparatus and captures the acceleration on an accelerometer. Algorithms were developed to segment the unstable periods of the accelerometer signal. Wavelets were used as well as non-linear filters. The non-linear filters increased the amplitudes of periods of instability, simple signal models of the output of the non-linear filters where formulated and analyzed. Vector processing techniques were also developed. The experimental results demonstrate that the acceleration during unstable periods can be differentiated by its frequency content, by its discontinuous nature and by using vector relationships. The algorithms were tested with five individuals and had over 80% accuracy.

Use of a Collar-Mounted Triaxial Accelerometer to Predict Speed and Gait in Dogs

Accelerometry has been used to measure treatment efficacy in dogs with osteoarthritis, although interpretation is difficult. Simplification of the output into speed or gait categories could simplify interpretation. We aimed to determine whether collar-mounted accelerometry could estimate the speed and categorise dogs’ gait on a treadmill. Eight Huntaway dogs were fitted with a triaxial accelerometer and then recorded using high-speed video on a treadmill at a slow and fast walk, trot, and canter. The accelerometer data (delta-G) was aligned with the video data and records of the treadmill speed and gait. Mixed linear and logistic regression models that included delta-G and a term accounting for the dogs’ skeletal sizes were used to predict speed and gait, respectively, from the accelerometer signal. Gait could be categorised (pseudo-R2 = 0.87) into binary categories of walking and faster (trot or canter), but not into the separate faster gaits. The estimation of speed above 3 m/s was inaccurate, though it is not clear whether that inaccuracy was due to the sampling frequency of the particular device, or whether that is an inherent limitation of collar-mounted accelerometers in dogs. Thus, collar-mounted accelerometry can reliably categorise dogs’ gaits into two categories, but finer gait descriptions or speed estimates require individual dog modelling and validation. Nonetheless, this accelerometry method could improve the use of accelerometry to detect treatment effects in osteoarthritis by allowing the selection of periods of activity that are most affected by treatment.