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A superconductive angular accelerometer (SAA) is an important sensor that supplements a superconductive angular gradiometer on moving platforms. The SAA measurements compensate one of the main errors, i.e., angular velocity squared in the gradiometer output. While the angular accelerations can be measured, angular velocity squared is computed by integration. However, a major difficulty arises when the angular accelerations are noisy because of translational accelerations of the platform and temperature fluctuations of the environment during the operation. Therefore, filtering of angular accelerations is essential before integration to derive angular velocities. We have implemented wavelet de-noising and de-trending techniques in order to mitigate these errors. The wavelet results are compared with the results of bandpass filtering. Since stationary observations are used, the best filtering method is selected based on its improvements in reducing the root mean square of the known noise floor. The best de-noising results showed 99% improvement in reducing the known noise level, whereas wavelet de-trending and de-noising results indicate a 99.4% improvement. Some bandpass digital filtering are also designed and applied to the angular acceleration, but wavelets perform slightly better than the best bandpass filter. The filtered angular acceleration can improve a gravity gradient error by almost 50%, which can ultimately affect the geophysical interpretation of the gradients. Despite the encouraging results in the laboratory, the expected superiority of wavelet filtering under real dynamic conditions has yet to be demonstrated. [ABSTRACT FROM PUBLISHER]

The use of inertial sensors (accelerometer and gyroscopes) for evaluation of movement disorder motion, including essential tremor (ET) and Parkinson's disease (PD), is becoming prevalent. This paper uses a novel combination of six degree-of-freedom motion analysis and coherence based processing methodologies to uncover differences in the signature of motion for the ET and PD movement disorders. This is the first analysis of such motions utilizing the novel methodology outlined, and it displays a distinct motion profile differentiating between these two groups. Such an analysis can be used to assist medical professionals in diagnosing movement disorders given a currently high error rate of diagnosis. As well, the Kalman smoothing analysis performed in this paper can be quite useful for any application when tracking of human motion is required. Another contribution of the work is the use of wavelets in zero phase lag filtering, which helped in preparing the data for analysis by removing unwanted frequencies without introducing distortions into the data. [ABSTRACT FROM AUTHOR]

There has been a lot of interest in recent years in using inertial sensors (accelerometers and gyroscopes) to monitor movement disorder motion and monitor the efficacy of treatment options. Two of the most prominent movement disorders, which are under evaluation in this research paper, are essential tremor (ET) and Parkinson's disease (PD). These movement disorders are first evaluated to show that ET and PD motion often depict more (tremor) motion content in the 3-12 Hz frequency band of interest than control data and that such tremor motion can be characterized using inertial sensors. As well, coherence analysis is used to compare between pairs of many of the six degrees-of-freedom of motions under evaluation, to determine the similarity in tremor motion for the various degrees-of-freedom at different frequency bands of interest. It was quite surprising that this coherence analysis depicts that there is a statistically significant relationship using coherence analysis when differentiating between control and effectively medicated PD motion. The statistical analysis uncovers the novel finding that PD medication induced dyskinesia is depicted within coherence data from inertial signals. Dyskinesia is involuntary motion or the absence of intended motion, and it is a common side effect among medicated PD patients. The results show that inertial sensors can be used to differentiate between effectively medicated PD motion and control motion; such a differentiation can often be difficult to perform with the human eye because effectively medicated PD patients tend to not produce much tremor. As well, the finding that PD motion, when well medicated, does still differ significantly from control motion allows for researchers to quantify potential deficiencies in the use of medication. By using inertial sensors to spot such deficiencies, as outlined in this research paper, it is hoped that medications with even a larger degree of efficacy can be created in the future. [ABSTRACT FROM AUTHOR]

Various high budget industries that utilize wheel-based vehicles rely on wheel odometry as an integral aspect of their navigation process. This research introduces a low-cost alternative for typical wheel encoders that are typically used to determine the on-track speed of vehicles. The proposed system is referred to as an Accelerometer-based Wheel Odometer for Kinematics determination (AWOK). The AWOK system comprises just a single axis accelerometer mounted radially at the center of any given wheel. The AWOK system can provide direct distances instead of just velocities, which are provided by typical wheel speedometers. Hence, the AWOK system is advantageous in comparison to typical wheel odometers. Besides, the AWOK system comprises a simple assembly with a highly efficient data processing algorithm. Additionally, the AWOK system provides a high capacity to handle high dynamics in comparison to similar approaches found in previous related work. Furthermore, the AWOK system is not affected by the inherited stochastic errors in micro-machined electro-mechanical systems (MEMS) inertial sensors, whether short-term or long-term errors. Above all, the AWOK system reported a relative accuracy of 0.15% in determining the distance covered by a car. [ABSTRACT FROM AUTHOR]