Two-mode navigation method for low-cost inertial measurement unit-based indoor pedestrian navigation

With the continuous development of positioning technology in today’s world, the accuracy requirements for navigation and positioning are also getting higher and higher. Global Positioning and Navigation System (GPS) can provide high-precision long-term navigation and positioning information. However, it has a strong dependence on the external environment, which means that it is easily disturbed by environmental changes and affects the accuracy of navigation and positioning and even leads to positioning failure. The inertial navigation system (INS) is an autonomous navigation system. It uses sensors to measure the specific force and angular velocity of the carrier for positioning and navigation, which means that it is less affected by the environment. However, the inertial navigation device will produce a certain initial error due to the restriction of the manufacturing level, and the error will increase with time, so the inertial navigation method is not suitable for long-term navigation. Therefore, it is of great practical significance to realize satellite/inertial navigation integrated navigation by combining the respective advantages of satellite navigation and inertial navigation methods and avoiding their respective disadvantages. This paper is aimed at studying the satellite/inertial navigation integrated navigation method based on the improved Kalman filter algorithm. The satellite inertial navigation integrated navigation experiment is carried out based on the improved Kalman filter algorithm. In the experiment, the noise reduction experiment of the designed satellite inertial navigation system was carried out by using the filtering noise reduction function of the improved Kalman filter algorithm, and the conclusion was drawn after the experiment. The navigation accuracy of the satellite inertial navigation system is improved by a total of 2 m after the improved Kalman filter algorithm is used to filter the noise reduction.

This chapter focuses on the self‐contained aiding techniques for pedestrian inertial navigation, which can limit the navigation error propagation of the strapdown inertial navigation while keeping the whole system independent of the environment. One of the most commonly used aiding techniques in the pedestrian inertial navigation is the Zero‐Velocity Update (ZUPT) aiding. One of the main advantages of ZUPT is its ability to obtain pseudo‐measurement of the velocity, which is otherwise unobservable by inertial measurement units (IMUs). There are two key parts involved in the ZUPT‐aided navigation algorithm: the stance phase detector and the pseudo‐measurement of the motion of the foot. In the pedestrian inertial navigation, the Extended Kalman Filter is commonly used to fuse the IMU readouts with other aiding techniques to obtain a more accurate navigation result. The chapter introduces the concept, algorithmic implementation, and parameter selection of the ZUPT‐aided pedestrian inertial navigation.

In this article, we develop a pedestrian inertial navigation (PIN) that uses an inertial measurement unit (IMU) mounted on a shoe to locate a pedestrian in an infrastructure-free environment. In the PIN, stand-alone navigation is possible by calculating the navigation solution using the inertial navigation system (INS) mechanism. For accurate navigation, however, it is necessary to compensate for accumulated errors over time. In a foot-mounted PIN, the error can be compensated through zero-velocity (ZV) correction method by detecting the moment the foot touches the ground. One important point here is to detect ZVs accurately. In most of the literature related to this, ZV detection techniques for normal gait have been proposed. In this article, we first propose a powerful ZV detection algorithm for various gait types. This algorithm consists of a new buffering method of the sensor output power, an exact ZV detection method through glitch removal, and a reinforcement method for ZV detection based on auxiliary step detection. Second, we design a PIN/ZV integration filter, and describe the real-time operation plan of all these functions. The performance of the PIN using the proposed algorithm is verified through experiments.

Human walking is an important and intuitive operation in daily life, but it is different for people missing this ability. Recognizing human gait cycle portions during walking is very useful for understanding the biomechanics of the muscles, pre-disease’s diagnosis, and designing lower limb prosthetics. The normal gait cycle is divided into stance and swing phases. In this work, benchmarking of several classification techniques is performed, based on electromyography (EMG) data collected from seven lower limb muscles, with the gait cycle phase used as a target vector to label the EMG data. The dataset is split into a training set used to build a statistical model using a specified classification technique, and a test set for purpose of testing a pre-generated statistical model. EMG signals are unfortunately normally corrupted with noise, which drastically reduces their classification performance. A median filter and root mean square (RMS) filter were thus applied to the raw EMG signals, and the performance of the classification techniques calculated in all cases for the raw EMG, EMG with median filter, and EMG with RMS filters for comparison purposes. The use of filters offered good enhancement in the classification process. The median filtered EMG signal performed best and gave higher accuracy than the raw EMG and RMS filtered EMG signals due to the technique’s efficacy in removing outliers. This work also offers a simple explanation of each classifier algorithm used, and a graphical feature selection was also applied to all seven muscles of interest to identify the muscle with the most influence on the gait cycle. The Rectus Femoris muscle shows the best activity separation between swing and stance phases, working mainly during the swing phase; this higher activity during the swing phase thus caused it to be identified as the muscle with the most influence over the normal gait cycle.

PurposeBefore 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueThe 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.

Segment-interaction and its relevance to the control of movement during sprinting

The complex construction of the joint apparatus of the cat distal forelimb, which allows the paw three degrees of freedom, poses special requirements on the neural signals controlling the paw position. To understand the electromyography (emg) signals of the distal forelimb muscles during locomotion, it is necessary to know the kinematics of the forelimb joints in detail. As no such information is available, we used the pulsed X-ray technique in trained cats during treadmill locomotion to analyse the angular excursions of the wrist, the metacarpophalangeal (MCP) and the proximal interphalangeal (PIP) joints. X-ray illuminations were done in either the parasagittal or the frontal plane. At the beginning of the stance phase the wrist (WR) and the MCP joints extended slowly, and the PIP joints flexed. Whereas the WR and the PIP joints maintained a constant angular position of approximately 200 degrees and 60 degrees, respectively, throughout the stance phase, extension continued in the MCP joints from 240 degrees at touch-down to 300 degrees at the end of the stance phase. Slightly before lift-off (100 ms) the WR and the MCP joints flexed rapidly. This flexion changed approximately 150 ms after lift-off into a slow extension. The PIP joints extended rapidly at the beginning and at the end of the swing phase, during the interposed period of the swing phase they displayed a slow flexion. Rotatory movements of the forelimb in the radioulnar joints were present during the swing and stance phases. During the swing phase the limb first supinated (starting 100 ms after lift-off); pronation occurred immediately before ground contact. During the stance phase the supination angle was kept constant until 100 ms before lift-off, when a short pronation was found. The paw was kept in an ulnar deviated position throughout the complete step cycle. Ulnar deviation decreased at the end of the swing and stance phases. The results of this study increase our understanding of how the body weight is transmitted on to the ground. They suggest four main functions for the skeletomotor apparatus and the underlying neural commands to secure the forward movement of the animal during the stance phase: (i) preparation and stabilization of a force-transmitting platform; (ii) stabilization of the wrist and the carpal/metacarpal joints; (iii) stabilization of the supination angle; (iv) antigravity control of the extension in the MCP.

Pedestrian Inertial Navigation System is of great significance in the fields of smart city services, emergency assistance, home health supervision, etc. However, the wearable Micro Inertial Measurement Unit (MIMU) contains relatively high noise and the error of MIMU accumulates with time. This paper proposes a method of pedestrian inertial navigation assisted by geomagnetic matching positioning. According to the measurement of pedestrian inertial navigation based on zero velocity update (PINS-ZUPT) and the geomagnetic matching positioning, the system model of the PINS-ZUPT/Geomagnetic matching navigation with the Kalman Filtering (KF) working in the zero-velocity interval is established in this paper. The positioning errors and velocity errors are gained from the Iterative Closet Contour Point (ICCP) algorithm of the geomagnetic matching positioning and the zero-velocity information respectively, which can be used as the observation values of KF to obtain the optimal pedestrian trajectory and correct accumulated errors. The experiment shows that the average positioning error of integrated navigation is within 1m under the mileage of 140m, reduced by more than 50% relative to PINS-ZUPT, which verifies the effectiveness of the integrated navigation.

Kinematic Features of Rear-Foot Motion Using Anterior and Posterior Ankle-Foot Orthoses in Stroke Patients With Hemiplegic Gait

The main objective of this work is to make an algorithm to detect onset and offset of stance and swing phase of human gait cycle. In order to proceed, the signal from the force sensors (placed underneath each foot) were first divided according to the fundamental period by auto-correlation. This ensures that each period contains a full stance phase and two fragmented swing phases. After this step, Short Time Fourier transform has been performed on each period of the signal to detect the initial search point (highest DC value) on the stance phase. Then by judging the width and slope of the straight line from changing search points on the signal to a changing destination point (starting from the minimum or maximum time value) of the graph the onset and offset of the stance and swing phase has been detected. The changing search point has small distance from the previous one, if the slope of the signal is small, but the distance increases with the increasing change of slope of the signal. The destination point changes with the change of slope of the straight line. This algorithm has been applied to the signals in the database of Parkinsons disease, which shows a success rate of more than 90%.

Peak medial (but not lateral) hamstring activity is significantly lower during stance phase of running. An EMG investigation using a reduced gravity treadmill

In pedestrian inertial navigation, one possible placement of Inertial Measurement Units (IMUs) is on a footwear. This placement allows to limit the accumulation of navigation errors due to the bias drift of inertial sensors and is generally a preferable placement of sensors to achieve the highest precision of pedestrian inertial navigation. However, inertial sensors mounted on footwear experience significantly higher accelerations and angular velocities (10s of g and 1000s of deg/sec) during regular pedestrian activities than during more conventional navigation tasks, which could exceed Full Scale Range (FSR) of many commercial-off-the-shelf IMUs, therefore degrading accuracy of pedestrian navigation systems. This paper proposes a reconstruction filter to mitigate localization error in pedestrian navigation due to insufficient FSR of inertial sensors. The proposed reconstruction filter approximates immeasurable accelerometer’s signals with a triangular function and estimates the size of the triangles using a Gaussian Process (GP) regression. To evaluate performance of pedestrian navigation systems enhanced by the proposed reconstruction filter, we conducted two series of indoor pedestrian navigation experiments with a VectorNav VN-200 IMU and an Analog Device ADIS16497-3 IMU, which have accelerometer’s FSR of ±16 g and ±40 g, respectively. In the first series of experiments, forces experienced by the foot-mounted IMUs did not exceed the FSRs of the sensors, while in the second series, the forces surpassed the FSR of the VN-200 IMU and saturated the accelerometer’s readings. The saturated accelerometer’s readings reduced the accuracy of estimated positions using the VN-200 by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1.34}\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${3.37}\times $ </tex-math></inline-formula> along horizontal and vertical directions. When applying our proposed reconstruction filter to the saturated accelerometer’s measurements, the navigation accuracy along horizontal and vertical directions was increased by 5% and 50%, respectively, as compared to using unreconstructed signals.

BackgroundThe metabolic cost of locomotion is a key factor in walking and running performance. It has been studied by analysing the activation and co-activation of the muscles of the lower limbs. However, these measures do not comprehensively address muscle mechanics, in contrast to approaches using muscle moments and co-contraction. Research questionWhat is the effect of speed and type of locomotion on muscle moments and co-contraction, and their relationship with metabolic cost during walking and running? MethodsEleven recreational athletes (60.5 ± 7.1 kg; 169.0 ± 6.6 cm; 23.6 ± 3.3 years) walked and ran on a treadmill at different speeds, including a similar speed of 1.75 m.s−1. Metabolic cost was estimated from gas exchange measurements. Muscle moments and co-contraction of ankle and knee flexors and extensors during the stance and swing phases were estimated using an electromyographic-driven model. ResultsBoth the slowest and fastest walking speeds had significantly higher metabolic costs than intermediate ones (p < 0.05). The metabolic cost of walking was correlated with plantarflexors moment during swing phase (r = 0.62 at 0.5 m.s−1, r = 0.67 at 1,25 m.s−1), dorsiflexors moment during stance phase (r = 0.65 at 1.25 m.s−1, r = 0.67 at 1.5 and 1.75 m.s−1), and ankle co-contraction during the stance phase (r = 0.63 at 1.25 and 1.75 m.s−1). The metabolic cost of running at 3.25 m.s−1 during the swing phase was correlated with the dorsiflexors moment (r = 0.63), plantarflexors moment (r = 0.61) and ankle co-contraction (r = 0.60). Discussion and conclusionFluctuations in metabolic cost of walking and running could be explained, at least in part, by increased ankle antagonist moments and co-contraction.

The forward motion of terrestrial vertebrates can be divided into stride lengths (distance traveled by the body from touchdown to the next touchdown of a limb) which is the sum of the lengths of stance and swing phases for each limb. Those lengths are influenced by the anatomy of the limbs; longer limbs, for example, produce longer steps and strides. During typical quadrupedal walking, at least one forelimb and hindlimb are in contact with the ground at any given time. This means the fore‐ and hindlimbs (regardless of limb proportions) should have the same stride length, though the length of the phases (stance, swing) may differ, as long as they sum to equal stride lengths. In addition to the anatomical limb length (ALL), joint excursion patterns, which often differs between the forelimb and hindlimb can influence stance and step. Animals can behaviorally adjust their limb postures to change their effective limb lengths (ELL, the minimum distance between the shoulder/hip and wrist/ankle) in order to achieve the necessary stride length. Thus, we hypothesize that forelimb and hindlimb stride lengths should be equal, but this may be achieved in different ways and therefore all other parameters may vary possibly in relation to limb proportions.To study this, we examined 69 quadrupedal walking bouts of four New World monkeys with differing ALL. Preliminary analyses indicate a close correlation of forelimb and hindlimb stride length (0.93, p&lt;0.01), regardless of differences in ALL proportions. Factors that contribute to an equal stride length are more variable. Forelimb and hindlimb stance phase length were highly correlated (0.700, p&lt;0.01), and forelimb and hindlimb swing phase length were highly correlated (0.834, p&lt;0.01). Although significant, a low correlation of the length of swing and stance phase was found for both the forelimb (0.412, p&lt;0.01) and the hindlimb (0.430, p&lt;0.01). When modeling step length in stance by angular excursion and ELL, the model accounts for an average of 85% of the distance traveled. Taken as a whole, these data, especially the intralimb and interlimb patterns, suggest that the step and swing lengths are not easily predicted by limb anatomy or effective limb lengths alone. Thus, other factors must also contribute to step length and thus equal stride lengths, such as bending of the back and contributions made by hand and foot posture. Overall, these data indicate that although stride lengths do need to be equal between the forelimb and hindlimb, contributing parameters vary between strides and may vary by species anatomy and ecology.Support or Funding InformationNSF BCS‐9222526

The application of the integrated kinematic segment to the study of human locomotion, II — Joint dynamics