Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

This article proposes a process to identify the standing stabilizer, namely, the controller in humans to keep upright posture stable against perturbations. We model the controller as a piecewise-linear feedback system, where the state of the center of mass (COM) is regulated by coordinating the whole body so as to locate the zero-moment point (ZMP) at the desired position. This was developed for humanoid robots and is possibly able to elaborate the fundamental control scheme used by humans to stabilize themselves. Difficulties lie on how to collect motion trajectories in a wide area of the state space for reliable identification and how to identify the piecewise-affine dynamical system. For the former problem, a motion measurement protocol is devised based on the theoretical phase portrait of the system. Regarding the latter problem, some clustering techniques including K-means method and EM (Expectation-and-Maximization) algorithm were examined. We found that a modified K-means method produced the most accurate result in this study. The method was applied to the identification of a lateral standing controller of a human subject. The result of the identification quantitatively supported a hypothesis that the COM-ZMP regulator reasonably models the human’s controller when deviations of the angular momentum about the COM are limited.

Reliability of the weight-bearing ankle dorsiflexion range of motion measurement using a smartphone goniometer application

Background Weight-bearing ankle dorsiflexion range of motion measurement (weight-bearing lunge test) is gaining in popularity because it mimics lower extremity function in daily physical activities. The purpose of the study is to assess the intra-rater and the inter-rater reliability of the weight-bearing ankle dorsiflexion range of motion measurement with a flexed knee using a smartphone application Spirit Level Plus installed on an Android smartphone. Methods Thirty-two young, healthy subjects participated in the study and were measured in four sessions by two examiners. One measurement was taken on each ankle in every session. Eight measurements were taken from each participant. A total of 256 were taken from all the participants. The measurements for the individual subject were repeated no sooner than 24 hours after the first session. In order to assess the reliability, intraclass correlation coefficients (ICC), standard error measurements (SEM) and minimal detectable change (MDC) at the 95% confidence interval were calculated. Results Statistical data analysis revealed moderate intra-rater reliability for the right ankle (ICC = 0.72, 95% CI [0.49–0.85]) and good intra-rater reliability for the left ankle (ICC = 0.82, 95% CI [0.66–0.91]). Inter-rater reliability is moderate for the right (ICC = 0.73, 95% CI [0.52–0.86]) and the left ankle (ICC = 0.65, 95% CI [0.39–0.81]). Conclusion The observed method is moderately reliable and appropriate when the main objective is to assess ankle dorsiflexion mobility in weight-bearing when weight-bearing is not contraindicated. The concurrent validity of the Spirit Level Plus application is excellent.

IMUMETER—A Convolution Neural Network-Based Sensor for Measurement of Aircraft Ground Performance

The paper presents the development of the IMUMETER sensor, designed to study the dynamics of aircraft movement, in particular, to measure the ground performance of the aircraft. A motivation of this study was to develop a sensor capable of airplane motion measurement, especially for airfield performance, takeoff and landing. The IMUMETER sensor was designed on the basis of the method of artificial neural networks. The use of a neural network is justified by the fact that the automation of the measurement of the airplane’s ground distance during landing based on acceleration data is possible thanks to the recognition of the touchdown and stopping points, using artificial intelligence. The hardware is based on a single-board computer that works with the inertial navigation platform and a satellite navigation sensor. In the development of the IMUMETER device, original software solutions were developed and tested. The paper describes the development of the Convolution Neural Network, including the learning process based on the measurement results during flight tests of the PZL 104 Wilga 35A aircraft. The ground distance of the test airplane during landing on a grass runway was calculated using the developed neural network model. Additionally included are exemplary measurements of the landing distance of the test airplane during landing on a grass runway. The results obtained in this study can be useful in the development of artificial intelligence-based sensors, especially those for the measurement and analysis of aircraft flight dynamics.

Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser

Satellites have many high-, medium-, and low-frequency micro vibration sources that lead to the optical axis jitter of the optical load and subsequently degrade the remote sensing image quality. To address this problem, this paper developed an image motion detection and restoration method based on an inertial reference laser, and describe edits principle and key components. To verify the feasibility and performance of this method, this paper also built an image motion measurement and restoration system based on an inertial reference laser, which comprised a camera (including the inertial reference laser unit and a Hartmann wavefront sensor), an integrating sphere, a simulated image target, a parallel light pope, a vibration isolation platform, a vibration generator, and a 6 degrees of freedom platform. The image restoration principle was also described. The background noise in the experiment environment was measured, and an image motion measurement accuracy experiment was performed. Verification experiments of image restoration were also conducted under various working conditions. The experiment results showed that the error of image motion detection based on the inertial reference laser was less than 0.12 pixels (root mean square). By using image motion data to improve image quality, the modulation transfer function (MTF) of the restored image was increased to 1.61–1.88 times that of the original image MTF. The image motion data could be used as feedback to the fast steering mirror to compensate for the satellite jitter in real time and to directly obtain high-quality images.