Note on Coarse Alignment of Gyro-Free Inertial Navigation System.

In this note, the feasibility of initial alignment of a gyro-free inertial navigation system (GF-INS) is investigated. Initial roll and initial pitch are obtained using leveling of conventional INS since centripetal acceleration is very small. The equation for the initial heading cannot be used since the GF inertial measurement unit (IMU) cannot directly measure the Earth rate. A new equation is derived to obtain the initial heading from GF-IMU accelerometer outputs. Initial heading is expressed in the accelerometer outputs of two configurations, which satisfies a specific condition among 15 GF-IMU configurations presented in the literature. The initial heading error to arrangement and accelerometer error is quantitatively analyzed from the initial heading calculation equation of GF-INS and the initial heading error analysis of the general INS. The initial heading error is investigated when gyroscopes are used with GF-IMU. The results show that the initial heading error depends more on the performance of the gyroscope than that of the accelerometer, and the initial heading cannot be obtained within a practical error level by using only GF-IMU, even when an extremely accurate accelerometer is used. Therefore, aiding sensors have to be used in order to have a practical initial heading.

Association of atypical femoral fracture location and lower limb mechanical axis: a computed tomography-based finite element analysis.

Atypical femoral fractures (AFFs) are categorized as low-energy fractures of the femoral shaft or subtrochanteric region. The use of computed tomography-based finite element analysis demonstrated that the femoral weakest point against tensile stress coincided with AFF location, which was determined by the lower limb axis and femoral bowing. INTRODUCTION: This study aimed to assess the relationship between the femoral weakest point against tensile stress and the lower limb axis and geometry, including femoral bowing, using a computed tomography (CT)-based finite element analysis (FEA) model. METHODS: We retrospectively reviewed 19 patients with AFFs and analyzed their CT images of the contralateral intact femur. We performed FEA to find the maximum principal stress (MPS) and maximal tensile stress loading area (femoral weakest point, FWP) of each patient and matched the FWP with the real location of AFF. We applied mechanical axes differently, as neutral, varus, and valgus, in the FEA model, when we analyzed the change in MPS and FWP based on lower limb alignment. We compared the degree of agreement between the real fracture location and FWP before and after knee mechanical axis adjustment. RESULTS: The average participant age was 75.9 (range, 61-87) years, and all participants were women. In the 19 patients included, we observed 20 and 7 shaft and subtrochanteric AFFs, respectively. The average mechanical axis at the knee joint level was 22.6 mm (range, 0-70 mm) of the varus. All the patients showed an increasing trend of MPS and a distal movement of FWP when the mechanical axis of the knee was applied from the valgus to varus alignment. The root mean square errors between the FWP and real fracture location were 14.58% and 10.87% before and after adjustment, respectively, implying that the degree of agreement was better in patients who underwent mechanical adjustment. CONCLUSION: The use of CT/FEA demonstrated that the FWP against tensile stress coincided with AFF location, which was determined by the lower limb axis and femoral bowing.

Kinematic Assessment to Measure Change in Impairment during Active and Active-Assisted Type of Robotic Rehabilitation for Patients with Stroke.

Analysis of kinematic features related to clinical assessment scales may qualitatively improve the evaluation of upper extremity movements of stroke patients. We aimed to investigate kinematic features that could correlate the change in the Fugl-Meyer Assessment (FMA) score of stroke survivors through upper extremity robotic rehabilitation. We also analyzed whether changes in kinematic features by active and active-assisted robotic rehabilitation correlated differently with changes in FMA scores. Fifteen stroke patients participated in the upper extremity robotic rehabilitation program, and nine kinematic features were calculated from reach tasks for assessment. Simple and multiple linear regression analyses were used to characterize correlations. Features representing movement speed were associated with changes in FMA scores for the group that used an active rehabilitation robot. In contrast, in the group that used an active-assisted rehabilitation robot, features representing movement smoothness were associated with changes in the FMA score. These estimates can be an important basis for kinematic analysis to complement clinical scales.

A comparison of the effects and usability of two exoskeletal robots with and without robotic actuation for upper extremity rehabilitation among patients with stroke: a single-blinded randomised controlled pilot study.

BACKGROUND: Robotic rehabilitation of stroke survivors with upper extremity dysfunction may yield different outcomes depending on the robot type. Considering that excessive dependence on assistive force by robotic actuators may interfere with the patient's active learning and participation, we hypothesised that the use of an active-assistive robot with robotic actuators does not lead to a more meaningful difference with respect to upper extremity rehabilitation than the use of a passive robot without robotic actuators. Accordingly, we aimed to evaluate the differences in the clinical and kinematic outcomes between active-assistive and passive robotic rehabilitation among stroke survivors. METHODS: In this single-blinded randomised controlled pilot trial, we assigned 20 stroke survivors with upper extremity dysfunction (Medical Research Council scale score, 3 or 4) to the active-assistive robotic intervention (ACT) and passive robotic intervention (PSV) groups in a 1:1 ratio and administered 20 sessions of 30-min robotic intervention (5 days/week, 4 weeks). The primary (Wolf Motor Function Test [WMFT]-score and -time: measures activity), and secondary (Fugl-Meyer Assessment [FMA] and Stroke Impact Scale [SIS] scores: measure impairment and participation, respectively; kinematic outcomes) outcome measures were determined at baseline, after 2 and 4 weeks of the intervention, and 4 weeks after the end of the intervention. Furthermore, we evaluated the usability of the robots through interviews with patients, therapists, and physiatrists. RESULTS: In both the groups, the WMFT-score and -time improved over the course of the intervention. Time had a significant effect on the WMFT-score and -time, FMA-UE, FMA-prox, and SIS-strength; group × time interaction had a significant effect on SIS-function and SIS-social participation (all, p < 0.05). The PSV group showed better improvement in participation and smoothness than the ACT group. In contrast, the ACT group exhibited better improvement in mean speed. CONCLUSIONS: There were no differences between the two groups regarding the impairment and activity domains. However, the PSV robots were more beneficial than ACT robots regarding participation and smoothness. Considering the high cost and complexity of ACT robots, PSV robots might be more suitable for rehabilitation in stroke survivors capable of voluntary movement. Trial registration The trial was registered retrospectively on 14 March 2018 at ClinicalTrials.gov (NCT03465267).

Vision/INS Integrated Navigation System for Poor Vision Navigation Environments.

In order to improve the performance of an inertial navigation system, many aiding sensors can be used. Among these aiding sensors, a vision sensor is of particular note due to its benefits in terms of weight, cost, and power consumption. This paper proposes an inertial and vision integrated navigation method for poor vision navigation environments. The proposed method uses focal plane measurements of landmarks in order to provide position, velocity and attitude outputs even when the number of landmarks on the focal plane is not enough for navigation. In order to verify the proposed method, computer simulations and van tests are carried out. The results show that the proposed method gives accurate and reliable position, velocity and attitude outputs when the number of landmarks is insufficient.

Design and Prediction of Aptamers Assisted by In Silico Methods.

An aptamer is a single-stranded DNA or RNA that binds to a specific target with high binding affinity. Aptamers are developed through the process of systematic evolution of ligands by exponential enrichment (SELEX), which is repeated to increase the binding power and specificity. However, the SELEX process is time-consuming, and the characterization of aptamer candidates selected through it requires additional effort. Here, we describe in silico methods in order to suggest the most efficient way to develop aptamers and minimize the laborious effort required to screen and optimise aptamers. We investigated several methods for the estimation of aptamer-target molecule binding through conformational structure prediction, molecular docking, and molecular dynamic simulation. In addition, examples of machine learning and deep learning technologies used to predict the binding of targets and ligands in the development of new drugs are introduced. This review will be helpful in the development and application of in silico aptamer screening and characterization.