Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

JointTracker: Real-time inertial kinematic chain tracking with joint position estimation

In-field human motion capture (HMC) is drawing increasing attention due to the multitude of application areas. Plenty of research is currently invested in camera-based (markerless) HMC, with the advantage of no infrastructure being required on the body, and additional context information being available from the surroundings. However, the inherent drawbacks of camera-based approaches are the limited field of view and occlusions. In contrast, inertial HMC (IHMC) does not suffer from occlusions, thus being a promising approach for capturing human motion outside the laboratory. However, one major challenge of such methods is the necessity of spatial registration. Typically, during a predefined calibration sequence, the orientation and location of each inertial sensor are registered with respect to the underlying skeleton model. This work contributes to calibration-free IHMC, as it proposes a recursive estimator for the simultaneous online estimation of all sensor poses and joint positions of a kinematic chain model like the human skeleton. The full derivation from an optimization objective is provided. The approach can directly be applied to a synchronized data stream from a body-mounted inertial sensor network. Successful evaluations are demonstrated on noisy simulated data from a three-link chain, real lower-body walking data from 25 young, healthy persons, and walking data captured from a humanoid robot. The estimated and derived quantities, global and relative sensor orientations, joint positions, and segment lengths can be exploited for human motion analysis and anthropometric measurements, as well as in the context of hybrid markerless visual-inertial HMC.

Inertial Measurement Unit Sensor-to-Segment Calibration Comparison for Sport-Specific Motion Analysis

Wearable inertial measurement units (IMUs) can be utilized as an alternative to optical motion capture as a method of measuring joint angles. These sensors require functional calibration prior to data collection, known as sensor-to-segment calibration. This study aims to evaluate previously described sensor-to-segment calibration methods to measure joint angle range of motion (ROM) during highly dynamic sports-related movements. Seven calibration methods were selected to compare lower extremity ROM measured using IMUs to an optical motion capture system. The accuracy of ROM measurements for each calibration method varied across joints and sport-specific tasks, with absolute mean differences between IMU measurement and motion capture measurement ranging from <0.1° to 24.1°. Fewer significant differences were observed at the pelvis than at the hip, knee, or ankle across all tasks. For each task, one or more calibration movements demonstrated non-significant differences in ROM for at least nine out of the twelve ROM variables. These results suggest that IMUs may be a viable alternative to optical motion capture for sport-specific lower-extremity ROM measurement, although the sensor-to-segment calibration methods used should be selected based on the specific tasks and variables of interest for a given application.

Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints

Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, optical motion capture, but at lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientations in which the sensors are attached to the body segments. This knowledge is commonly obtained via time-consuming and error-prone anatomical calibration based on precisely defined poses or motions. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints in the angular rates and the relative orientations to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.

Effects of Dynamic IMU-to-Segment Misalignment Error on 3-DOF Knee Angle Estimation in Walking and Running

The inertial measurement unit (IMU)-to-segment (I2S) alignment is an important part of IMU-based joint angle estimation, and the accurate estimation of the three degree of freedom (3-DOF) knee angle can provide practical support for the evaluation of motions. In this paper, we introduce a dynamic weight particle swarm optimization (DPSO) algorithm with crossover factor based on the joint constraint to obtain the dynamic alignment vectors of I2S, and use them to perform the quaternion-based 3-DOF knee angle estimation algorithm. The optimization algorithm and the joint angle estimation algorithm were evaluated by comparing with the optical motion capture system. The range of 3-DOF knee angle root mean square errors (RMSEs) is 1.6°-5.9° during different motions. Furthermore, we also set up experiments of human walking (3 km/h), jogging (6 km/h) and ordinary running (9 km/h) to investigate the effects of dynamic I2S misalignment errors on 3-DOF knee angle estimation during different motions by artificially adding errors to I2S alignment parameters. The results showed differences in the effects of I2S misalignment errors on the estimation of knee abduction, internal rotation and flexion, which indicate the differences in knee joint kinematics among different motions. The IMU to thigh misalignment error has the greatest effect on the estimation of knee internal rotation. The effect of IMU to thigh misalignment error on the estimation of knee abduction angle becomes smaller and then larger during the two processes of switching from walking to jogging and then speeding up to ordinary running. The effect of IMU to shank misalignment error on the estimation of knee flexion angle is numerically the largest, while the standard deviation (SD) is the smallest. This study can provide support for future research on the accuracy of 3-DOF knee angle estimation during different motions.

Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits

Inertial measurement units (IMUs) offer an attractive way to study human lower-limb kinematics without traditional laboratory constraints. We present an error-state Kalman filter method to estimate 3D joint angles, joint angle ranges of motion, stride length, and step width using data from an array of seven body-worn IMUs. Importantly, this paper contributes a novel joint axis measurement correction that reduces joint angle drift errors without assumptions of strict hinge-like joint behaviors of the hip and knee. We evaluate the method compared to two optical motion capture methods on twenty human subjects performing six different types of walking gait consisting of forward walking (at three speeds), backward walking, and lateral walking (left and right). For all gaits, RMS differences in joint angle estimates generally remain below 5 degrees for all three ankle joint angles and for flexion/extension and abduction/adduction of the hips and knees when compared to estimates from reflective markers on the IMUs. Additionally, mean RMS differences in estimated stride length and step width remain below 0.13 m for all gait types, except stride length during slow walking. This study confirms the method's potential for non-laboratory based gait analysis, motivating further evaluation with IMU-only measurements and pathological gaits.

Validation of Non-Restrictive Inertial Gait Analysis of Individuals with Incomplete Spinal Cord Injury in Clinical Settings

Inertial Measurement Units (IMUs) have gained popularity in gait analysis and human motion tracking, and they provide certain advantages over stationary line-of-sight-dependent Optical Motion Capture (OMC) systems. IMUs appear as an appropriate alternative solution to reduce dependency on bulky, room-based hardware and facilitate the analysis of walking patterns in clinical settings and daily life activities. However, most inertial gait analysis methods are unpractical in clinical settings due to the necessity of precise sensor placement, the need for well-performed calibration movements and poses, and due to distorted magnetometer data in indoor environments as well as nearby ferromagnetic material and electronic devices. To address these limitations, recent literature has proposed methods for self-calibrating magnetometer-free inertial motion tracking, and acceptable performance has been achieved in mechanical joints and in individuals without neurological disorders. However, the performance of such methods has not been validated in clinical settings for individuals with neurological disorders, specifically individuals with incomplete Spinal Cord Injury (iSCI). In the present study, we used recently proposed inertial motion-tracking methods, which avoid magnetometer data and leverage kinematic constraints for anatomical calibration. We used these methods to determine the range of motion of the Flexion/Extension (F/E) hip and Abduction/Adduction (A/A) angles, the F/E knee angles, and the Dorsi/Plantar (D/P) flexion ankle joint angles during walking. Data (IMU and OMC) of five individuals with no neurological disorders (control group) and five participants with iSCI walking for two minutes on a treadmill in a self-paced mode were analyzed. For validation purposes, the OMC system was considered as a reference. The mean absolute difference (MAD) between calculated range of motion of joint angles was 5.00°, 5.02°, 5.26°, and 3.72° for hip F/E, hip A/A, knee F/E, and ankle D/P flexion angles, respectively. In addition, relative stance, swing, double support phases, and cadence were calculated and validated. The MAD for the relative gait phases (stance, swing, and double support) was 1.7%, and the average cadence error was 0.09 steps/min. The MAD values for RoM and relative gait phases can be considered as clinically acceptable. Therefore, we conclude that the proposed methodology is promising, enabling non-restrictive inertial gait analysis in clinical settings.

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 (N=12) 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).

Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

Skin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

Inertial Sensors-Applications and Challenges in a Nutshell

This editorial provides a concise introduction to the methods and applications of inertial sensors. We briefly describe the main characteristics of inertial sensors and highlight the broad range of applications as well as the methodological challenges. Finally, for the reader’s guidance, we give a succinct overview of the papers included in this special issue.

Human and Animal Motion Tracking Using Inertial Sensors

Motion is key to health and wellbeing, something we are particularly aware of in times of lockdowns and restrictions on movement. Considering the motion of humans and animals as a biomarker of the performance of the neuro-musculoskeletal system, its analysis covers a large array of research fields, such as sports, equine science and clinical applications, but also innovative methods and workplace analysis. In this Special Issue of Sensors, we focused on human and animal motion-tracking using inertial sensors. Ten research and two review papers, mainly on human movement, but also on the locomotion of the horse, were selected. The selection of articles in this Special Issue aims to display current innovative approaches exploring hardware and software solutions deriving from inertial sensors related to motion capture and analysis. The selected sample shows that the versatility and pervasiveness of inertial sensors has great potential for the years to come, as, for now, limitations and room for improvement still remain.

Logical-Linguistic Model of Diagnostics of Electric Drives with Sensors Support

The presented paper scientifically discusses the progressive diagnostics of electrical drives in robots with sensor support. The AI (artificial intelligence) model proposed by the authors contains the technical conditions of fuzzy inference rule descriptions for the identification of a robot drive’s technical condition and a source for the description of linguistic variables. The parameter of drive diagnostics for a robotized workplace that is proposed here is original and composed of the sum of vibration acceleration amplitudes ranging from a frequency of 6.3 Hz to 1250 Hz of a one-third-octave filter. Models of systems for the diagnostics of mechatronic objects in the robotized workplace are developed based on examples of CNC (Computer Numerical Control) machine diagnostics and mechatronic modules based on the fuzzy inference system, concluding with a solved example of the multi-criteria optimization of diagnostic systems. Algorithms for CNC machine diagnostics are implemented and intended only for research into precisely determined procedures for monitoring the lifetime of the mentioned mechatronic systems. Sensors for measuring the diagnostic parameters of CNC machines according to precisely determined measuring chains, together with schemes of hardware diagnostics for mechatronic systems are proposed.