IMU-based Methods Research Articles

Comparative assessment of heel rise detection for consistent gait phase separation

BackgroundAccurate identification of gait events is crucial to reliable gait analysis. Heel rise, a key event marking the transition from mid-stance to terminal stance, poses challenges in precise detection due to its gradual nature. This leads to variability in accuracy across studies utilizing diverse measuring techniques. Research questionHow do different HR detection methods compare when assessed against the underlying heel motion pattern and visual detection across varying speed, footwear conditions, and individuals? MethodsLeveraging data from over 10,000 strides in diverse scenarios with 15 healthy subjects, we evaluated methods based on measurements from optical motion capture (OMC), force plates, and shank-mounted inertial measurement units (IMUs). The evaluation of these methods included an assessment of their precision and consistency with the heel marker's motion pattern and agreement with visually detected heel rise. ResultsOMC-based heel rise detection methods, utilizing the heel marker's vertical acceleration and jerk, consistently identified the same point in the heel motion pattern, outperforming velocity-based methods and our new position-based method resembling traditional footswitch-based heel rise detection. Variability in velocity and position-based methods derives from subtle heel rise variations after mid-stance, exhibiting individual differences. Our proposed IMU-based methods show promise by closely matching OMC-based accuracy. SignificanceThe results have significant implications for gait analysis, providing insights into heel rise event detection's complexities. Accurate HR identification is crucial for gait phase separation, and our findings, especially with the robust heel marker's jerk-based method, enhance precision and consistency across walking conditions. Moreover, our successful development and validation of IMU-based algorithm offer cost-effective and mobile alternative for HR detection, expanding their potential use in comprehensive gait analysis.

3D human pose estimation with single image and inertial measurement unit (IMU) sequence

Three-dimensional human pose estimation plays an important role in the field of computer vision, such as in healthcare, sports, activity recognition, motion capture, and augmented reality. However, monocular image or video based methods are sensitive to occlusions, while multi-view methods usually require enormous computation resources. Currently, inertial measurement unit (IMU)-based methods have begun to overcome the occlusion problem and can potentially achieve real-time inference. Yet, they still suffer from insufficient precision and scale drift error over time. In this paper, we propose a novel, efficient framework to fuse a single image with temporal sequence from IMU sensors to estimate human poses and reconstruct human shapes. Our method achieves 46 mm Mean Per Joint Positional Error (MPJPE) on the Total Capture dataset with 30 frames time segment, and surpasses state-of-the-art pure IMU-based methods. Moreover, in comparison with other vision-based methods, the proposed method shows great advantage in reducing computing floating point operations per second (FLOPS) quota while still achieving competitive estimation precision. Our method achieves 74 FPS on an IPhone 12 for offline processing. In addition, our method can easily be generalized for outdoor cases.

Inertial Sensor Technologies-Their Role in Equine Gait Analysis, a Review.

Objective gait analysis provides valuable information about the locomotion characteristics of sound and lame horses. Due to their high accuracy and sensitivity, inertial measurement units (IMUs) have gained popularity over objective measurement techniques such as force plates and optical motion capture (OMC) systems. IMUs are wearable sensors that measure acceleration forces and angular velocities, providing the possibility of a non-invasive and continuous monitoring of horse gait during walk, trot, or canter during field conditions. The present narrative review aimed to describe the inertial sensor technologies and summarize their role in equine gait analysis. The literature was searched using general terms related to inertial sensors and their applicability, gait analysis methods, and lameness evaluation. The efficacy and performance of IMU-based methods for the assessment of normal gait, detection of lameness, analysis of horse-rider interaction, as well as the influence of sedative drugs, are discussed and compared with force plate and OMC techniques. The collected evidence indicated that IMU-based sensor systems can monitor and quantify horse locomotion with high accuracy and precision, having comparable or superior performance to objective measurement techniques. IMUs are reliable tools for the evaluation of horse-rider interactions. The observed efficacy and performance of IMU systems in equine gait analysis warrant further research in this population, with special focus on the potential implementation of novel techniques described and validated in humans.

3D Deformation Capture via a Configurable Self-Sensing IMU Sensor Network

Motion capture technologies reconstruct human movements and have wide-ranging applications. Mainstream research on motion capture can be divided into vision-based methods and inertial measurement unit (IMU)-based methods. The vision-based methods capture complex 3D geometrical deformations with high accuracy, but they rely on expensive optical equipment and suffer from the line-of-sight occlusion problem. IMU-based methods are lightweight but hard to capture fine-grained 3D deformations. In this work, we present a configurable self-sensing IMU sensor network to bridge the gap between the vision-based and IMU-based methods. To achieve this, we propose a novel kinematic chain model based on the four-bar linkage to describe the minimum deformation process of 3D deformations. We also introduce three geometric priors, obtained from the initial shape, material properties and motion features, to assist the kinematic chain model in reconstructing deformations and overcome the data sparsity problem. Additionally, to further enhance the accuracy of deformation capture, we propose a fabrication method to customize 3D sensor networks for different objects. We introduce origami-inspired thinking to achieve the customization process, which constructs 3D sensor networks through a 3D-2D-3D digital-physical transition. The experimental results demonstrate that our method achieves comparable performance with state-of-the-art methods.

The contribution of multibody optimization when using inertial measurement units to compute lower-body kinematics.

Kinematics obtained using Inertial Measurement Units (IMUs) still present significant differences when compared to those obtained using optoelectronic systems. Multibody Optimization (MBO) might diminish these differences by reducing soft-tissue artefacts - probably emphasized when using IMUs - as established for optoelectronic-based kinematics. To test this hypothesis, 15 subjects were equipped with 7 IMUs and 38 reflective markers tracked by 18 optoelectronic cameras. The subjects walked, ran, cycled on an ergocycle, and performed a task which induced joint movements in the transverse and frontal planes. In addition to lower-body kinematics computed using the optoelectronical system data, three IMU-based kinematics were computed: from IMU orientations without MBO; from MBO performed using the OpenSense add-on of the OpenSim software (OpenSim 4.2, Stanford, USA); as outputs from the commercialised MVN MBO (Xsens, Netherlands). Root Mean Square Errors (RMSE), coefficients of correlations, and differences in range of motion were calculated between the three IMU-based methods and the reference kinematics. MVN MBO seems to present a slight advantage over Direct kinematics or OpenSense MBO, since it presents 34 times out of 48 (12 degrees of freedom * 4 sports activities) a mean RMSE inferior to the Direct and OpenSense kinematics. However, it was not always significant and the differences rarely exceeded 2°. This study does not therefore conclude on a significant contribution of MBO in improving lower-body kinematics obtained using IMUs. This lack of results can partly be explained by the weakness of both the kinematic constraints applied to the kinematic chain and segment stiffening. Personalization of the kinematic chain, the use of more than one IMU by segment in order to provide information redundancy, or the use of other approaches based on the Kalman Filter might increase this MBO impact.

Body-Worn IMU-Based Human Hip and Knee Kinematics Estimation during Treadmill Walking.

Traditionally, inertial measurement unit (IMU)-based human joint angle estimation techniques are evaluated for general human motion where human joints explore all of their degrees of freedom. Pure human walking, in contrast, limits the motion of human joints and may lead to unobservability conditions that confound magnetometer-free IMU-based methods. This work explores the unobservability conditions emergent during human walking and expands upon a previous IMU-based method for the human knee to also estimate human hip angles relative to an assumed vertical datum. The proposed method is evaluated () in a human subject study and compared against an optical motion capture system. Accuracy of human knee flexion/extension angle (7.87 absolute root mean square error (RMSE)), hip flexion/extension angle (3.70 relative RMSE), and hip abduction/adduction angle (4.56 relative RMSE) during walking are similar to current state-of-the-art self-calibrating IMU methods that use magnetometers. Larger errors of hip internal/external rotation angle (6.27 relative RMSE) are driven by IMU heading drift characteristic of magnetometer-free approaches and non-hinge kinematics of the hip during gait, amongst other error sources. One of these sources of error, soft tissue perturbations during gait, is explored further in the context of knee angle estimation and it was observed that the IMU method may overestimate the angle during stance and underestimate the angle during swing. The presented method and results provide a novel combination of observability considerations, heuristic correction methods, and validation techniques to magnetic-blind, kinematic-only IMU-based skeletal pose estimation during human tasks with degenerate kinematics (e.g., straight line walking).

Constrained ESKF for UAV Positioning in Indoor Corridor Environment Based on IMU and WiFi.

The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, which make it impossible to achieve positioning and navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are fused together and realize higher positioning accuracy. The probability-based optimization method is used for further accuracy improvement. After data fusion, the positioning accuracy increased by 51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm. After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost sensors) are very capable of obtaining high indoor positioning accuracy.

Image-based gramian angular field processing for pedestrian stride-length estimation using convolutional neural network

<p>In an age when people spend most of their time indoors and smartphones become a necessity, there is an increasing demand to navigate user absolute position in indoor environments. While global positioning system (GPSs) perform well outdoors, their inaccuracy can not be tolerated in places where GPS signal is weak or barely detected. This leads to a number of solutions which utilize smartphone inertial measurement unit (IMU) to track user location. Most IMU-based methods track the trajectory of a person by using stride-length and heading estimation. Thus, the accuracy of stride-length estimation plays a very important role in these methods. Inspired by recent success in the field of computer vision and machine learning, we proposed an image-based stride-length estimation method that employs gramian angular field (GAF) in converting accelerometer data into images, and then feed them into a convolutional neural network (CNN) to predict the stride-length. We evaluate the performance of our proposed method by using a public dataset from Qu Wang in his GitHub repository (available at https://github.com/Archeries/StrideLengthEstimation). The result shows that our proposed method is superior in terms of accuracy in one stride and in large walking distance than others using only data collected from the accelerometer.</p>

A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography.

We present a novel method for estimating respiratory motion using inertial measurement units (IMUs) based on microelectromechanical systems (MEMS) technology. As an application of the method we consider the amplitude gating of positron emission tomography (PET) imaging, and compare the method against a clinically used respiration motion estimation technique. The presented method can be used to detect respiratory cycles and estimate their lengths with state-of-the-art accuracy when compared to other IMU-based methods, and is the first based on commercial MEMS devices, which can estimate quantitatively both the magnitude and the phase of respiratory motion from the abdomen and chest regions. For the considered test group consisting of eight subjects with acute myocardial infarction, our method achieved the absolute breathing rate error per minute of 0.44 ± 0.23 1/min, and the absolute amplitude error of 0.24 ± 0.09 cm, when compared to the clinically used respiratory motion estimation technique. The presented method could be used to simplify the logistics related to respiratory motion estimation in PET imaging studies, and also to enable multi-position motion measurements for advanced organ motion estimation.

역동적 스포츠 분야 응용을 위한 IMU 기반 관절각 추정에서의 표류오차 감소

In the case of dynamic sports activities such as skiing and sprints, it is difficult to apply optical motion capture systems because of measurement volume limitation. Alternatively, the use of inertial measurement unit (IMU) as a motion sensor has gained attention. This paper proposes a drift reduction method in the IMU-based joint angle estimation for dynamic motion-involved sports applications. To resolve the problem of conventional IMU-based methods significantly reducing performance under highly dynamic conditions, the proposed method applies a correction method using joint constraint. The proposed method is the complementary filter based on the previous drift reduction technique using the joint constraint, but performs in real time. The proposed method was validated by comparing the estimation accuracy with conventional methods under various dynamic conditions. The results showed that the proposed method was superior to the methods that did not use the constraint. While the proposed method was 0.19° less accurate than the non-realtime method of the reference, it is more practical due to its realtime correction capability.

IMU-based Assessment of Ankle Inversion Kinematics and Orthosis Migration.

Ankle orthoses are used to prevent injuries during very fast inversion motions of the ankle. In order to provide proper protection, they must fit well and migrate as little as possible. Inertial measurement units (IMUs) have become a useful tool for accurate motion analysis and are frequently used for gait analysis. In the present paper, we examine orthosis migration and inversion kinematics of human ankles protected and unprotected by an orthosis. We present two test benches that were developed for these purposes, as well as a set of inertial sensor fusion methods that are used to determine kinematic parameters from the sensor readings. To avoid the common but restrictive assumption of a homogeneous magnetic field, we determine all motion parameters without the use of magnetometer readings. We conduct a measurement series to compare the proposed IMU-based method to alternative camera-based and goniometer-based methods. Using two different ankle orthosis prototypes, we demonstrate that the proposed IMU-based methods facilitate accurate assessment of orthosis migration and ankle inversion kinematics.

Characterizing the Impact of Sampling Rate and Filter Design on the Morphology of Lower Limb Angular Velocities

The angular velocity of lower limb segments during walking is useful for calculating temporal–spatial parameters and joint kinematics in the inertial measurement unit (IMU)-based gait analysis. While many IMU-based methods for gait analysis have been proposed, configurations of data acquisition and signal conditioning parameter have not been standardized across studies. This study examined the effect of sampling rate and filter design on the characteristic angular velocity waveform obtained from IMUs donned on the lower limbs. Specifically, the frequency analysis of this waveform was performed, and the difference in timing and magnitude of the signal at gait events was evaluated relative to the initial signal. A minimum sampling rate of 35 Hz and a lowpass filter (LPF) cutoff of greater than 10 Hz were found to be appropriate parameters for acquisition and conditioning of the lower limb angular velocity signals. The required low minimum sampling rate suggests that gyroscope data are appropriate for use in bandwidth and battery life limited mobile health applications. Our results also showed that while the commonly employed LPF cutoffs of 5 Hz or less may be appropriate for mid-stance detection, they were not optimal for calculation of heel-strike, toe-off, mid-swing, or angular displacement, obtained with heel-strike and toe-off as delimiters.

A wearable solution for accurate step detection based on the direct measurement of the inter-foot distance

Accurate step detection is crucial for the estimation of gait spatio-temporal parameters. Although several step detection methods based on the use of inertial measurement units (IMUs) have been successfully proposed, they may not perform adequately when the foot is dragged while walking, when walking aids are used, or when walking at low speed. The aim of this study was to test an original step-detection method, the inter-foot distance step counter (IFOD), based on the direct measurement of the distance between feet. Gait data were recorded using a wearable prototype system (SWING2DS), which integrates an IMU and two time-of-flight distance sensors (DSs). The system was attached to the medial side of the right foot with one DS positioned close to the forefoot (FOREDS) and the other close to the rearfoot (REARDS). Sixteen healthy adults were asked to walk over ground for two minutes along a loop, including both rectilinear and curvilinear portions, during two experimental sessions. The accuracy of the IFOD step counter was assessed using a stereo-photogrammetric system as gold standard. The best performance was obtained for REARDS with an accuracy higher than 99.8% for the instrumented foot step and 88.8% for the non-instrumented foot step during both rectilinear and curvilinear walks. Key features of the IFOD step counter are that it is possible to detect both right and left steps by instrumenting one foot only and that it does not rely on foot impact dynamics. The IFOD step counter can be combined with existing IMU-based methods for increasing step-detection accuracy.

A graph optimization approach for motion estimation using inertial measurement unit data

This study presents a novel approach for processing motion data from a six-degree-of-freedom inertial measurement unit (IMU). Trajectory estimation through double integration of acceleration measurements results in the generation and accumulation of multiple errors. Existing IMU-based measurement methods often use constrained initial and final states to resolve these errors. The constraints on the initial and final states lead to a uniform distribution of the accumulated errors throughout the calculated trajectory so that they cancel each other. We develop a generalized method that can incorporate the constraints from the measurements of intermediate states. The proposed approach is inspired by graph-based simultaneous localization and mapping processes from robotics research. We tested the proposed method with simulated and actual IMU data and found that our method estimates trajectories more accurately than conventional methods with acceptably higher computational costs.

Accuracy of three methods in gait event detection during overground running

Inertial measurement units (IMUs) have been extensively used to detect gait events. Various methods have been proposed for detecting initial contact (IC) and toe-off (TO) using IMUs affixed at various anatomical locations. However, the accuracy of such methods has yet to be compared. This study evaluated the accuracy of three common methods used for detecting gait events during jogging and running: (1) S-method, in which IC is identified as the instant of peak foot-resultant acceleration and TO is identified when the acceleration exceeds a threshold of 2g in the region of interest; (2) M-method, in which IC and TO are defined as the minimum before the positive peak shank vertical acceleration and the minimum in the region of interest, respectively; and (3) L-method, in which IC is indicated by the instant of peak pelvis anteroposterior acceleration and TO is identified by the maximum in the region of interest. The performance of the IMU-based methods in detecting IC and TO and estimating stance time (ST) were tested on 11 participants at jogging and running speeds against a reference provided by a force-platform method. The S-method was the most accurate for IC detection (overall mean absolute difference (MAD): 4.7±4.1ms). The M-method was the most accurate for TO detection (overall MAD: 7.0±3.5ms). A combination of M- and S-methods, called the MS-method, was the most accurate for ST estimation (overall MAD: 9.0±3.9ms). Thus, the MS-method is recommended for ST estimation; however, this method requires four IMUs for bilateral estimation.

Preliminary results: A comparison of specific imu-based calibrations for cycling vs. conventional methods

Introduction: Recent technological advances in MEMS allow considering the biomechanical evaluation of cyclists outside the laboratory (road, track cycling, etc). Indeed, optoelectronical motion capture in cycling is commonely used in laboratory to provide relevant biomechanical parameters associated to performance optimization and/or injury prevention. However, it does not allow evaluation in ecological and outdoor conditions, which can be counteracted using inertial measurement units (IMU). These sensors give the opportunity to estimate 3D segmental rotations, from which body-to-sensor rotations can be obtained. However, IMUs usually suffer from signal drift and require calibration procedure when accurate 3D joint angles are calculated. This latter calibration procedure is of major interest and is generally based on static or dynamic tasks. However, specific calibration procedures applied to cycling are missing and should be developed for outdoor applications. The aim of this study is to develop specific IMU-based methods for analyzing cycling motion, using calibration tasks based on pedaling motion. This method is compared to conventional methods applied in IMUbased gait analysis. Â Methods: Six participants were equipped with IMU sensors on thigh and shank at a sampling rate of 75 Hz. Each sensor was placed on lower limb segments as depicted in Figure 1. Each subject achieved calibration tasks prior to cycling exercise: two conventional method following methodology adapted from (Palermo et al., 2014, Favre et al., 2009). Then, a third static calibration was realized on the bike and was based on leg extension associated with crank at bottom dead center. Finally, a dynamic calibration based on cycling motion consisting of a 2 minutes pedaling motion at 90rpm at 60% MAP allowed to estimate flexion/extension axis using an optimization procedure (Seel et al. 2012). We evaluated the influence of the four methods on the estimation of knee flexion/extension axis in IMU frame. Differences are presented along each axis with a significant level set at p<0.05. Results Differences between methods are presented in table 1 for thigh and table 2 for shank. Significant difference (p<0.05) was found between conventional methods for X and Z-axis for thigh. Significant difference (p<0.05) was also found for thigh Y-axis between static calibration on bike and dynamic method versus static conventional methods. Concerning thigh X- axis, the only difference was quantified between statics methods (p<0.05). The largest difference was obtained between conventional vs. on bike, dynamic method for thigh Z axis. Lastly, concerning shank, significant difference was observed only between conventional dynamic method and on bike static method. Â Discussion: An objective of this study was to show the interest of a dynamic calibration directly on the bike instead of conventional procedures before cycling. This study showed significant differences between tasks and seems to be relevant for cyclist in order to avoid preliminary calibrations using IMU when cycling. Such a method may therefore be used during the warming session. The next step will be to evaluate error between IMU and optoelectronical motion capture when calculating 3D anatomical joint axis (ISB norm). Further step in this work will require more subjects and will evaluate the effects of various conditions such as power output, cadence, etc.

Gait event detection in laboratory and real life settings: Accuracy of ankle and waist sensor based methods

Wearable sensors technology based on inertial measurement units (IMUs) is leading the transition from laboratory-based gait analysis, to daily life gait monitoring. However, the validity of IMU-based methods for the detection of gait events has only been tested in laboratory settings, which may not reproduce real life walking patterns. The aim of this study was to evaluate the accuracy of two algorithms for the detection of gait events and temporal parameters during free-living walking, one based on two shank-worn inertial sensors, and the other based on one waist-worn sensor. The algorithms were applied to gait data of ten healthy subjects walking both indoor and outdoor, and completing protocols that entailed both straight supervised and free walking in an urban environment. The values obtained from the inertial sensors were compared to pressure insoles data. The shank-based method showed very accurate initial contact, stride time and step time estimation (<14ms error). Accuracy of final contact timings and stance time was lower (28–51ms error range). The error of temporal parameter variability estimates was in the range 0.09–0.89%. The waist method failed to detect about 1% of the total steps and performed worse than the shank method, but the temporal parameter estimation was still satisfactory. Both methods showed negligible differences in their accuracy when the different experimental conditions were compared, which suggests their applicability in the analysis of free-living gait.

Quantifying the effects of load carriage and fatigue under load on sacral kinematics during countermovement vertical jump with IMU-based method

We present a method for quantifying sacral kinematics during countermovement jumping (CMJ) using an inertial measurement unit (IMU). The IMU-derived sacral kinematic trajectories reproduced motion capture acceleration, velocity, and displacement to within mean (standard deviation) differences of 0.024 (0.088) m/s2, 0.023 (0.026) m/s, and 0.003 (0.032) m, respectively, across 252 jumps performed by 14 subjects. The method also quantified differences in maximum sacral displacement to within 1 % and differences in maximum propulsive velocity to within 0.7 % of motion capture estimates. This builds upon existing IMU-based methods for quantifying jump performance, which do not provide sacral kinematic trajectories. The utility of this method is demonstrated by its ability to discriminate jump performance metrics across a diverse subject population. In particular, we found that 21 participants adopted multiple strategies to maximize jump height in unloaded and loaded fresh conditions, but converged to a common strategy when jumping fatigued and under load. Changes in kinematic parameters were evident across conditions, and several changes were significantly associated with changes in jump performance (i.e., height). These parameters include changes in the depth of the countermovement, duration of the propulsive phase and maximum propulsive velocity. Collectively, these results point toward the future use of this method in naturalistic environments and for multiple objectives including biomechanical performance assessment and tracking, fatigue assessment, and jump training.

Inertial sensor based method for identifying spherical joint center of rotation

The miniaturized wireless inertial measurement unit (IMU) technology and algorithms presented herein promote rapid and accurate predictions of the center-of-rotation (CoR) for ball/spherical joints. The algorithm improves upon existing IMU-based methods by directly utilizing the measured acceleration and angular velocity provided by the IMU to deduce the CoR in lieu of relying on error-prone velocity and position estimates. Results demonstrate that this new method resolves the position of the CoR to within a 3mm sphere of the true CoR determined by a precision coordinate measuring machine. Such accuracy may render this method attractive for broad use in field, laboratory and clinical settings requiring non-invasive and rapid estimates of joint CoR.