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This paper aims to describe a novel hybrid inertial measurement unit (IMU) for motion capturing via a new configuration of strategically distributed inertial sensors, and a calibration approach for the accelerometer and gyroscope sensors mounted in a flight vehicle motion tracker built on the inertial navigation system.

The hybrid-IMU is designed with five accelerometers and one auxiliary gyroscope instead of the accelerometer and gyroscope triads in the conventional IMU.

Simulation studies for tracking with both attitude angles and translational movement of a flight vehicle are conducted to illustrate the effectiveness of the proposed method.

The cross-quadratic terms of angular velocity are selected to process the direct measurements of angular velocities of body frame and to avoid the integration of angular acceleration vector compared with gyro-free configuration based on only accelerometers. The inertial sensors are selected from the commercial microelectromechanical system devices to realize its low-cost applications.

This paper aims to present a pipeline to progressively deal with the online external parameter calibration and estimator initialization of the Stereo-inertial measurement unit (IMU) system, which does not require any prior information and is suitable for system initialization in a variety of environments.

Before calibration and initialization, a modified stereo tracking method is adopted to obtain a motion pose, which provides prerequisites for the next three steps. Firstly, the authors align the pose obtained with the IMU measurements and linearly calculate the rough external parameters and gravity vector to provide initial values for the next optimization. Secondly, the authors fix the pose obtained by the vision and restore the external and inertial parameters of the system by optimizing the pre-integration of the IMU. Thirdly, the result of the previous step is used to perform visual-inertial joint optimization to further refine the external and inertial parameters.

The results of public data set experiments and actual experiments show that this method has better accuracy and robustness compared with the state of-the-art.

This method improves the accuracy of external parameters calibration and initialization and prevents the system from falling into a local minimum. Different from the traditional method of solving inertial navigation parameters separately, in this paper, all inertial navigation parameters are solved at one time, and the results of the previous step are used as the seed for the next optimization, and gradually solve the external inertial navigation parameters from coarse to fine, which avoids falling into a local minimum, reduces the number of iterations during optimization and improves the efficiency of the system.

The purpose of this paper is to present a novel data glove which can capture the motion of the arm and hand by inertial and magnetic sensors. The proposed data glove is used to provide the information of the gestures and teleoperate the robotic arm-hand.

The data glove comprises 18 low-cost inertial and magnetic measurement units (IMMUs) which not only make up the drawbacks of traditional data glove that only captures the incomplete gesture information but also provide a novel scheme of the robotic arm-hand teleoperation. The IMMUs are compact and small enough to wear on the upper arm, forearm, palm and fingers. The calibration method is proposed to improve the accuracy of measurements of units, and the orientations of each IMMU are estimated by a two-step optimal filter. The kinematic models of the arm, hand and fingers are integrated into the entire system to capture the motion gesture. A positon algorithm is also deduced to compute the positions of fingertips. With the proposed data glove, the robotic arm-hand can be teleoperated by the human arm, palm and fingers, thus establishing a novel robotic arm-hand teleoperation scheme.

Experimental results show that the proposed data glove can accurately and fully capture the fine gesture. Using the proposed data glove as the multiple input device has also proved to be a suitable teleoperating robotic arm-hand system.

Integrated with 18 low-cost and miniature IMMUs, the proposed data glove can give more information of the gesture than existing devices. Meanwhile, the proposed algorithms for motion capture determine the superior results. Furthermore, the accurately captured gestures can efficiently facilitate a novel teleoperation scheme to teleoperate the robotic arm-hand.

The selection of a suitable inertial measurement unit (IMU) is a critical step in an inertial navigation system (INS) design. Nevertheless, inertial sensors manufacturers are unwilling to publish their products’ accurate performance parameters along with a price. This paper aims to summarise the current IMU market review and point out parameters important for short-term inertial navigation.

The market review is based on the information published by manufacturers in brochures, datasheets and websites. Some information, including price, was also collected from sensors distributors. The entire collection of data includes data of over 150 sensors from 32 manufacturers and is valid for the first half of the year 2020.

This paper answers the following questions: •Why and where use inertial navigation? •Which parameters should one emphasise during IMU selection?•What is currently available on the IMU market? •Which parameters have a significant influence on price? •What are the advantages of specific sensor technology?

This paper gathers data published by IMU manufacturers, allowing for a quick overview of the current market. Based on real data, different sensor technologies are compared. The performed analysis presents the statistical basis for the IMU selection. By theoretical considerations a significance of sensor parameters is drawn and an approach to an IMU selection based on limited number of parameters is proposed. Although the considerations have been carried out regarding inertial navigation, the results from an extensive analysis of commercially available sensors may also be useful for other applications.

The purpose of this paper is to develop a highly miniaturized wireless inertial sensor system based on a novel 3D packaging technique using a flexible printed circuit (FPC). The device is very suitable for wearable applications in which small size and lightweight are required such as body area network, medical, sports and entertainment applications.

Modern wireless inertial measurement units are typically implemented on a rigid 2D printed circuit board (PCB). The design concept presented here is based around the use of a novel planar, six‐faceted, crucifix or cross‐shaped FPC instead of a rigid PCB. A number of specific functional blocks (such as microelectromechanical systems gyroscope and accelerometer sensors, microcontroller (MCU), radio transceiver, antenna, etc.) are first assigned to each of the six faces which are each 1 cm² in area. The FPC cross is then developed into a 1 cm³, 3D configuration by folding the cross at each of five bend planes. The result is a low‐volume and lightweight, 1 cm³ wireless inertial sensor that can sense and send motion sensed data wirelessly to a base station. The wireless sensor device has been designed for low power operation both at the hardware and software levels. At the base station side, a radio receiver is connected to another MCU unit, which sends received data to a personal computer (PC) and graphical user interface. The industrial, scientific and medical band (2.45 GHz) is used to achieve half duplex communication between the two sides.

A complete wireless sensor system has been realized in a 3D cube form factor using an FPC. The packaging technique employed during the work is shown to be efficient in fabricating the final cubic system and resulted in a significant saving in the final size and weight of the system. A number of design issues are identified regarding the use of FPC for implementing the 3D structure and the chosen solutions are shown to be successful in dealing with these issues.

Currently, a limitation of the system is the need for an external battery to power the sensor system. A second phase of development would be required to investigate the possibility of the integration of a battery and charging system within the cube structure. In addition, the use of flexible substrate imposes a number of restrictions in terms of the ease of manufacturability of the final system due to the requirement of the required folding step.

The small size and weight of the developed system is found to be extremely useful in different deployments. It would be useful to further explore the system performance in different application scenarios such as wearable motion tracking applications. In terms of manufacturability, component placement needs to be carefully considered, ensuring that there is sufficient distance between the components, bend planes and board edges and this leads to a slightly reduced usable area on the printed circuit.

This paper provides a novel and useful method for realizing a wireless inertial sensor system in a 3D package. The value of the chosen approach is that a significant reduction in the required system volume is achieved. In particular, a 78.5 per cent saving in volume is obtained in decreasing the module size from a 25 to a 15 mm³ size.

This paper aims to present a method for improving the performance of the visual-inertial navigation system (VINS) by using a bio-inspired polarized light compass.

The measurement model of each sensor module is derived, and a robust stochastic cloning extended Kalman filter (RSC-EKF) is implemented for data fusion. This fusion framework can not only handle multiple relative and absolute measurements, but can also deal with outliers, sensor outages of each measurement module.

The paper tests the approach on data sets acquired by a land vehicle moving in different environments and compares its performance against other methods. The results demonstrate the effectiveness of the proposed method for reducing the error growth of the VINS in the long run.

The main contribution of this paper lies in the design/implementation of the RSC-EKF for incorporating the homemade polarized light compass into visual-inertial navigation pipeline. The real-world tests in different environments demonstrate the effectiveness and feasibility of the proposed approach.

Before designing a navigation system, it is necessary to analyse possible approaches in terms of expected accuracy, existing limitations and economic justification to select the most advantageous solution. This paper aims to collect possible navigation methods that can provide correction for inertial navigation and to evaluate their suitability for use on a manoeuvring tactical missile.

The review of existing munitions was based on data collected from the literature and online databases. The data collected included dimensions, performance, applied navigation and guidance methods and their achievable accuracy. The requirements and limitations identified were confronted with the range of sensor parameters available on the market. Based on recent literature, navigation methods were reviewed and evaluated for applicability to inertial navigation system (INS) correction in global navigation satellite system-denied space.

The performance analysis of existing munition shows that small and relatively inexpensive micro-electro-mechanical system-type inertial sensors are required. A review of the parameters of existing devices of this type has shown that they are subject to measurement errors that do not allow them to achieve the delivery accuracy expected of precision missiles. The most promising navigation correction methods for manoeuvring flying objects have been identified.

The information presented in this paper is the result of the first phase of a project and presents the results of the requirements selection, initial sizing and preliminary design of the navigation system. This paper combines a review of the current state of the art in missile systems and an analysis of INS accuracy including the selection of sensor parameters.

This study aims to leverage inertial sensors via a walk test to associate kinematic variables with functional assessment results among walkable subjects with chronic stroke.

Adults with first-ever stroke survivors were recruited for this study. First, functional assessments were obtained by using Fugl–Meyer Assessment for lower extremity and Berg balance scales. A self-assembled inertial measurement system obtained walking variables from a walk test after being deployed on subjects’ affected limbs and lower back. The average walking speeds, average range of motion in the affected limbs and a new gait symmetry index were computed and correlated with the two functional assessment scales using Spearman’s rank correlation test.

The average walking speeds were moderately correlated with both Fugl–Meyer assessment scales (γ = 0.62, p < 0.01, n = 23) and Berg balance scales (γ = 0.68, p < 0.01, n = 23). After being modified by the subjects’ height, the new gait symmetry index revealed moderate negative correlations with the Fugl–Meyer assessment scales (γ = −0.51, p < 0.05) and Berg balance scales (γ = −0.52, p < 0.05). The other kinematics failed to correlate well with the functional scales.

Neuromotor and functional assessment results from inertial sensors can facilitate their application in telemonitoring and telerehabilitation.

The average walking speeds and modified gait symmetry index are valuable parameters for inertial sensors in clinical research to deduce neuromotor and functional assessment results. In addition, the lower back is the optimal location for the inertial sensors.

The purpose of this paper is to develop an online smoothing zero-velocity-update (ZUPT) method that helps achieve smooth estimation of human foot motion for the ZUPT-aided inertial pedestrian navigation system.

The smoothing ZUPT is based on a Rauch–Tung–Striebel (RTS) smoother, using a six-state Kalman filter (KF) as the forward filter. The KF acts as an indirect filter, which allows the sensor measurement error and position error to be excluded from the error state vector, so as to reduce the modeling error and computational cost. A threshold-based strategy is exploited to verify the detected ZUPT periods, with the threshold parameter determined by a clustering algorithm. A quantitative index is proposed to give a smoothness estimate of the position data.

Experimental results show that the proposed method can improve the smoothness, robustness, efficiency and accuracy of pedestrian navigation.

Because of the chosen smoothing algorithm, a delay no longer than one gait cycle is introduced. Therefore, the proposed method is suitable for applications with soft real-time constraints.

The paper includes implications for the smooth estimation of most types of pedal locomotion that are achieved by legged motion, by using a sole foot-mounted commercial-grade inertial sensor.

This paper helps realize smooth transitions between swing and stance phases, helps enable continuous correction of navigation errors during the whole gait cycle, helps achieve robust detection of gait phases and, more importantly, requires lower computational cost.

The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing.

In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope.

Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor.

The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed.

Today, most of human–robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors.

Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human–robot interaction scenario show the performance of the system.