Multiple Inertial Measurement Units Research Articles

Multi-IMU Based Alternate Navigation Frameworks: Performance & Comparison for UAS

On-board state estimation is a persistent challenge to fielding unmanned aerial systems (UAS), particularly when global positioning system (GPS) measurements are not available. The dominant estimation tool remains the extended Kalman filter (EKF) applied to an inertial measurement unit (IMU). The growing availability of low-cost commercial-grade IMUs raises questions about how to best improve sensor readings into an estimate when measurements are available from multiple IMUs. This paper evaluates four different approaches to attitude estimation from multiple IMU measurements and their application in high dynamic motion. The four approaches are fusion of measurements (virtual IMU), fusion of state estimates (Federated KF), feedback fusion state estimate (Feedback Federated KF), and an EKF design incorporating the additional measurements (Augmented KF); these correspond to fusion before, within, or after state estimation. The performance of the approaches are quantified for varying IMU number theoretically and experimentally. The experiments use on-board autopilot hardware implementations of the estimators during maneuvers in a motion capture volume and flights on-board an Unmanned Aerial Systems (UAS) implementation, with the peak and root-mean-square (RMS) errors used to quantify accuracy. The RMS error results indicate that the feedback federated Kalman Filter using five IMUs returns 38% compared to general federated Kalman Filter using 37% accuracy improvement over a single IMU. This improvement compares to a 19% improvement for virtual IMU and 9% improvement for the Augmented KF respectively. These results indicate that both the Federated KF approach achieves the lowest RMS error relative to the virtual IMU and augmented KF approaches and inform the design of multi-IMU UAS pose estimators.

Improving the Navigation Performance of the MEMS IMU Array by Precise Calibration

Using a microelectromechanical system (MEMS) inertial measurement unit (IMU) array composed of multiple low-cost IMUs can reduce the measurement errors of inertial sensors, and improve its navigation performance. However, there is doubt about the benefit of the IMU array in the scene of GNSS/INS <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic navigation</i> . Therefore, to evaluate the navigation performance of the arrays, we developed four groups of IMU arrays, each containing 16 MEMS IMUs. Each IMU was accurately calibrated and compensated to improve the performance. The field experiments proved that the navigation accuracy of the IMU array improved by 3.4 times statistically over a single IMU, which is close to the theoretical limit, i.e., 4 times. Comparison data processing indicated that the individual IMU calibration reduced the horizontal position error of the array by 54% on average, which confirms that the precise calibration of each IMU, especially the cross-axis effect and mounting angles, is crucial to the array’s navigation performance. This research provides firm experimental support for the application of IMU arrays in the field of navigation.

NTU VIRAL: A visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint

In recent years, autonomous robots have become ubiquitous in research and daily life. Among many factors, public datasets play an important role in the progress of this field, as they waive the tall order of initial investment in hardware and manpower. However, for research on autonomous aerial systems, there appears to be a relative lack of public datasets on par with those used for autonomous driving and ground robots. Thus, to fill in this gap, we conduct a data collection exercise on an aerial platform equipped with an extensive and unique set of sensors: two 3D lidars, two hardware-synchronized global-shutter cameras, multiple Inertial Measurement Units (IMUs), and especially, multiple Ultra-wideband (UWB) ranging units. The comprehensive sensor suite resembles that of an autonomous driving car, but features distinct and challenging characteristics of aerial operations. We record multiple datasets in several challenging indoor and outdoor conditions. Calibration results and ground truth from a high-accuracy laser tracker are also included in each package. All resources can be accessed via our webpage https://ntu-aris.github.io/ntu_viral_dataset/.

Estimation of Mechanical Power Output Employing Deep Learning on Inertial Measurement Data in Roller Ski Skating.

The ability to optimize power generation in sports is imperative, both for understanding and balancing training load correctly, and for optimizing competition performance. In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs). Thirteen athletes conducted roller ski skating trials on a treadmill with varying incline and speed. The acceleration and gyroscope data collected with the IMUs were run through statistical feature processing, before being used by the deep learning model to estimate power output. The model was thereafter used for prediction of power from test data using two approaches. First, a user-dependent case was explored, reaching a power estimation within 3.5% error. Second, a user-independent case was developed, reaching an error of 11.6% for the power estimation. Finally, the LSTM model was compared to two other machine learning models and was found to be superior. In conclusion, the user-dependent model allows for precise estimation of roller skiing power output after training the model on data from each athlete. The user-independent model provides less accurate estimation; however, the accuracy may be sufficient for providing valuable information for recreational skiers.

Placement of inertial measurement units in Racket Sports: Perceptions of coaches for IMU use during training and competition.

While inertial measurement units (IMU) have become an integral part of sports performance analysis, upper body-mounted IMUs have been found to exhibit poor reliability in measuring lower-limb loading. In racket sports, IMUs have been placed in a number of positions on the upper body, lower body and racket in a research setting. A potential limitation to the concurrent use of multiple IMUs is that coaches may be reluctant to allow their athletes to wear the units during training and competition due to concerns that the units would interfere with athlete movement. This study seeks to understand the perceptions of racket sports coaches towards the use of IMUs in training and competition. A total of 58 racket sport coaches responded to a survey on the use of IMUs during training and competition. Based on the responses, 96.6% (56 out of 58) of coaches indicated that they would allow their athletes to wear IMUs in training, while 65.5% (38 out of 58) would allow their athletes to wear IMUs during competition. For use in training, 9 of the 14 suggested IMU placements received significant positive responses. However, none of the suggested IMU placements received significant positive responses for use during competition and 11 of the 14 received significant negative responses. This suggests that while coaches understand the benefits of collecting data from IMUs during competition. Despite this, for use in training, a number of upper and lower body-mounted IMUs placements have the potential to be part of regular monitoring in racket sports.

MIMC-VINS: A Versatile and Resilient Multi-IMU Multi-Camera Visual-Inertial Navigation System

As cameras and inertial sensors are becoming ubiquitous in mobile devices and robots, it holds great potential to design visual-inertial navigation systems (VINS) for efficient versatile 3-D motion tracking, which utilize any (multiple) available cameras and inertial measurement units (IMUs) and are resilient to sensor failures or measurement depletion. To this end, rather than the standard VINS paradigm using a minimal sensing suite of a single camera and IMU, in this article, we design a real-time consistent multi-IMU multi-camera (MIMC) VINS estimator that is able to seamlessly fuse multimodal information from an arbitrary number of uncalibrated cameras and IMUs. Within an efficient multi-state constraint Kalman filter framework, the proposed MIMC-VINS algorithm optimally fuses asynchronous measurements from all sensors while providing smooth, uninterrupted, and accurate 3-D motion tracking even if some sensors fail. The key idea of the proposed MIMC-VINS is to perform high-order on-manifold state interpolation to efficiently process all available visual measurements without increasing the computational burden due to estimating additional sensors’ poses at asynchronous imaging times. In order to fuse the information from multiple IMUs, we propagate a joint system consisting of all IMU states while enforcing rigid-body constraints between the IMUs during the filter update stage. Finally, we estimate online both spatiotemporal extrinsic and visual intrinsic parameters to make our system robust to errors in prior sensor calibration. The proposed system is extensively validated in both Monte Carlo simulations and real-world experiments.

Classifying diverse manual material handling tasks using a single wearable sensor

The use of inertial measurement units (IMUs) for monitoring and classifying physical activities has received substantial attention in recent years, both in occupational and non-occupational contexts. However, a “user-friendly” approach is needed to promote this approach to quantify physical demands in actual workplaces. We explored the use of a single IMU for extracting information about different manual material handling (MMH) tasks (i.e., specific type of task performed, and associated duration and frequency), using a bidirectional long short-term memory network for classification. Classification performance using single IMUs placed on several body parts was compared with performance using multiple IMU configurations (2, 3, and 17 IMUs). Overall, the use of a single sensor led to satisfactory results (e.g., median accuracy >97%) in classifying MMH tasks and estimating task duration and frequency. Limited benefits were obtained using additional sensors, and several sensor locations yielded similar outcomes. Classification performance, though, was relatively inferior for push/pull vs. other tasks.

SMC-Based Synchronization of Multiple Inertial Measurement Units with Application to Attitude Tracking Control

In this paper, the attitude control of aircraft with multiple inertial measurement units under the influence of unknown gyro zero drift and external disturbance is studied. First of all, the observers are designed to estimate the zero drift biases based on the consensus algorithm. The angular velocity used for aircraft control is obtained by compensating the biases. Then, considering the external disturbance in the aircraft motion, this paper introduces a super-twisting sliding-mode algorithm to design the observer in order to compensate the disturbance. In addition, based on the proposed observers, a controller is designed to realize attitude control of the aircraft with the gyro zero drift and the external disturbance. Finally, the simulation results are given to verify the effectiveness of the proposed control law.

Learning Sensor Interdependencies for IMU-to-Segment Assignment

Due to the recent technological advances in inertial measurement units (IMUs), many applications for the measurement of human motion using multiple body-worn IMUs have been developed. In these applications, each IMU has to be attached to a predefined body segment. A technique to identify the body segment on which each IMU is mounted allows users to attach inertial sensors to arbitrary body segments, which avoids having to remeasure due to incorrect attachment of the sensors. We address this IMU-to-segment assignment problem and propose a novel end-to-end learning model that incorporates a global feature generation module and an attention-based mechanism. The former extracts the feature representing the motion of all attached IMUs, and the latter enables the model to learn the dependency relationships between the IMUs. The proposed model thus identifies the IMU placement based on the features from global motion and relevant IMUs. We quantitatively evaluated the proposed method using synthetic and real public datasets with three sensor configurations, including a full-body configuration mounting 15 sensors. The results demonstrated that our approach significantly outperformed the conventional and baseline methods for all datasets and sensor configurations.

American Academy of Sports Physical Therapy Platform Presentation Abstracts SPL1–SPL82

American Academy of Sports Physical Therapy Platform Presentation Abstracts SPL1–SPL82

Multiple Inertial Measurement Unit Combination and Location for Center of Pressure Prediction in Gait.

Center of pressure (COP) during a gait cycle indicates crucial information with regard to fall risk such as balance capacity. The drawbacks of conventional research instruments include inconvenient use during activities of daily living and expensive costs. The present study illustrates the promising fall-relevant information predicted by acceleration and angular velocity data from different placement sensors with machine learning techniques. This approach is inspired by the emerging machine learning technique, specifically the long short-term memory (LSTM), which is often used in time series data and aims to decrease the burden of the user while using the novel wearable technology. The Jaccard similarity coefficient, which implies the consistency of profile alignment between prediction and real situation, achieved 94% accuracy in the walking direction. Furthermore, the number of sensors used and the placement influenced the feasibility of an application. The outcome revealed that the accuracy could exceed 90% with only one sensor placed on the foot in the walking direction, and the toe would be the best location for sensor placement. To examine the performance of machine learning, the current study employed two parameters from different perspectives. One is a commonly used parameter, which represented the error, and the other investigated the similarity between the prediction and ground truth. From a similarity perspective, the parameter can be used as a metric to assess the consistency of profile alignment.

Fusing Accelerometry with Videography to Monitor the Effect of Fatigue on Punching Performance in Elite Boxers.

Wearable sensors and motion capture technology are accepted instruments to measure spatiotemporal variables during punching performance and to study the externally observable effects of fatigue. This study aimed to develop a computational framework enabling three-dimensional inverse dynamics analysis through the tracking of punching kinematics obtained from inertial measurement units and uniplanar videography. The framework was applied to six elite male boxers performing a boxing-specific punch fatigue protocol. OpenPose was used to label left side upper-limb landmarks from which sagittal plane kinematics were computed. Custom-made inertial measurement units were embedded into the boxing gloves, and three-dimensional punch accelerations were analyzed using statistical parametric mapping to evaluate the effects of both fatigue and laterality. Tracking simulations of a sub-set of left-handed punches were formulated as optimal control problems and converted to nonlinear programming problems for solution with a trapezoid collocation method. The laterality analysis revealed the dominant side fatigued more than the non-dominant, while tracking simulations revealed shoulder abduction and elevation moments increased across the fatigue protocol. In future, such advanced simulation and analysis could be performed in ecologically valid contexts, whereby multiple inertial measurement units and video cameras might be used to model a more complete set of dynamics.

다중 IMU 센서와 연합형 필터 융합 기반의 GPS-free 위치 추정

Localization techniques based on the integration of MEMSs(Micro Electro Mechanical Systems) and GPS(Global Positioning System) have been widely used in various fields, including aerospace, biomedical, automotive, and robotics, among others. However, it is well known that GPS signals are weakened or blocked in tunnels, urban canyons, or indoor environments. A solution is to use multiple IMU(Inertial Measurement Unit) sensors. In this paper, we present a federated fusion algorithm for GPS-free localization using multiple MEMS IMUs. The federated filter uses a master fusion algorithm to combine the outputs of independent sensors so that the current position can be estimated. The proposed structure exploits a quaternion-based extended Kalman filter and a linear Kalman filter for attitude estimations and velocity estimations, respectively. The effectiveness of the proposed algorithm was demonstrated with MATLAB simulation.

Multisensor-Based 3D Gesture Recognition for a Decision-Making Training System

This article demonstrates a gesture recognition method using multiple Inertial Measurement Unit (IMU) sensors, which can record acceleration and rotation information for hand joints. The proposed gesture recognition method comprises frequency ConvNet and TemporalNet to extract the representative features within a sliding window of IMU signals for recognizing various types of hand gestures. To validate the proposed gesture recognition method, basketball official referee signals (ORSs), which comprise sixty-five types of gestures including both large motion hand movement and subtle motion hand movement, are utilized as the main recognition task to evaluate the proposed method. The evaluation results reveal the proposed recognition model can achieve convinced performance, which outperforms other existing works. In addition, the satisfied performance of the proposed recognition model encourages us to develop a decision-making training (DMT) system for cultivating basketball referees. The results of subjective evaluations by the recruited 20 participants indicate the training system based on the proposed gestures recognition method can efficiently strengthen the decision-making skills of users.

Effects of Upper-Limb, Lower-Limb, and Full-Body Compression Garments on Full Body Kinematics and Free-Throw Accuracy in Basketball Players

Compression garments can enhance performance and promote recovery in athletes. Different body coverage with compression garments may impose distinct effects on kinematic movement mechanics and thus basketball free-throw accuracy. The objective of this study was to examine basketball free-throw shooting accuracy, consistency and the range of motion of body joints while wearing upper-, lower- and full-body compression garments. Twenty male basketball players performed five blocks of 20 basketball free-throw shooting trials in each of the following five compression garment conditions: control-pre, top, bottom, full (top + bottom) and control-post. All conditions were randomized except pre- and post-control (the first and last conditions). Range of motion of was acquired by multiple inertial measurement units. Free-throw accuracy and the coefficient of variation were also analyzed. Players wearing upper-body or full-body compression garments had significantly improved accuracy by 4.2% and 5.9%, respectively (p &lt; 0.05), but this difference was not observed with shooting consistency. Smaller range of motion of head flexion and trunk lateral bending (p &lt; 0.05) was found in the upper- and full-body conditions compared to the control-pre condition. These findings suggest that an improvement in shooting accuracy could be achieved by constraining the range of motion through the use of upper-body and full-body compression garments.

A Lightweight and Accurate Localization Algorithm Using Multiple Inertial Measurement Units

This paper proposes a novel inertial-aided localization approach by fusing information from multiple inertial measurement units (IMUs) and exteroceptive sensors. IMU is a low-cost motion sensor which provides measurements on angular velocity and gravity compensated linear acceleration of a moving platform, and widely used in modern localization systems. To date, most existing inertial-aided localization methods exploit only one single IMU. While the single-IMU localization yields acceptable accuracy and robustness for different use cases, the overall performance can be further improved by using multiple IMUs. To this end, we propose a lightweight and accurate algorithm for fusing measurements from multiple IMUs and exteroceptive sensors, which is able to obtain noticeable performance gain without incurring additional computational cost. To achieve this, we first probabilistically map measurements from all IMUs onto a virtual IMU. This step is performed by stochastic estimation with least-square estimators and probabilistic marginalization of inter-IMU rotational accelerations. Subsequently, the propagation model for both state and error state of the virtual IMU is also derived, which enables the use of the classical filter-based or optimization-based sensor fusion algorithms for localization. Finally, results from both simulation and real-world tests are provided, which demonstrate that the proposed algorithm outperforms competing algorithms by noticeable margins.

Gait Segmentation Method Using a Plantar Pressure Measurement System with Custom-Made Capacitive Sensors.

Gait analysis has been widely studied by researchers due to the impact in clinical fields. It provides relevant information on the condition of a patient’s pathologies. In the last decades, different gait measurement methods have been developed in order to identify parameters that can contribute to gait cycles. Analyzing those parameters, it is possible to segment and identify different phases of gait cycles, making these studies easier and more accurate. This paper proposes a simple gait segmentation method based on plantar pressure measurement. Current methods used by researchers and clinicians are based on multiple sensing devices (e.g., multiple cameras, multiple inertial measurement units (IMUs)). Our proposal uses plantar pressure information from only two sensorized insoles that were designed and implemented with eight custom-made flexible capacitive sensors. An algorithm was implemented to calculate gait parameters and segment gait cycle phases and subphases. Functional tests were performed in six healthy volunteers in a 10 m walking test. The designed in-shoe insole presented an average power consumption of 44 mA under operation. The system segmented the gait phases and sub-phases in all subjects. The calculated percentile distribution between stance phase time and swing phase time was almost 60%/40%, which is aligned with literature reports on healthy subjects. Our results show that the system achieves a successful segmentation of gait phases and subphases, is capable of reporting COP velocity, double support time, cadence, stance phase time percentage, swing phase time percentage, and double support time percentage. The proposed system allows for the simplification of the assessment method in the recovery process for both patients and clinicians.

Automatic Classification of Squat Posture Using Inertial Sensors: Deep Learning Approach.

Without expert coaching, inexperienced exercisers performing core exercises, such as squats, are subject to an increased risk of spinal or knee injuries. Although it is theoretically possible to measure the kinematics of body segments and classify exercise forms with wearable sensors and algorithms, the current implementations are not sufficiently accurate. In this study, the squat posture classification performance of deep learning was compared to that of conventional machine learning. Additionally, the location for the optimal placement of sensors was determined. Accelerometer and gyroscope data were collected from 39 healthy participants using five inertial measurement units (IMUs) attached to the left thigh, right thigh, left calf, right calf, and lumbar region. Each participant performed six repetitions of an acceptable squat and five incorrect forms of squats that are typically observed in inexperienced exercisers. The accuracies of squat posture classification obtained using conventional machine learning and deep learning were compared. Each result was obtained using one IMU or a combination of two or five IMUs. When employing five IMUs, the accuracy of squat posture classification using conventional machine learning was 75.4%, whereas the accuracy using deep learning was 91.7%. When employing two IMUs, the highest accuracy (88.7%) was obtained using deep learning for a combination of IMUs on the right thigh and right calf. The single IMU yielded the best results on the right thigh, with an accuracy of 58.7% for conventional machine learning and 80.9% for deep learning. Overall, the results obtained using deep learning were superior to those obtained using conventional machine learning for both single and multiple IMUs. With regard to the convenience of use in self-fitness, the most feasible strategy was to utilize a single IMU on the right thigh.

Multiple Inertial Measurement Units–An Empirical Study

An inertial navigation system is commonly used in various marine platforms above and below the sea surface to calculate the position, velocity and orientation of its carrier platform. Such systems contain an inertial measurement unit (IMU) to measure the specific force and angular velocity which in turn are integrated to obtain the navigation state. Due to sensor noises and other error terms, the navigation solution drifts in time. In situations of pure inertial navigation (no external aiding), multiple IMUs (MIMU) can be used to improve the performance of a single unit. In this paper, we explore the benefits of using a MIMU system for common navigation operations. To that end, a 32 MIMU architecture (192 inertial sensors) was designed and constructed for the experimental evaluations. Utilizing this system we examined the effect of the number of sensors in the architecture versus position accuracy, stationary calibration, coarse alignment and gyro free design. We derive closed form empirical expressions enabling insight to the connection between number of IMUs to the expected performance.

Development of a Data Logger for Capturing Human-Machine Interaction in Wheelchair Head-Foot Steering Sensor System in Dyskinetic Cerebral Palsy.

The use of data logging systems for capturing wheelchair and user behavior has increased rapidly over the past few years. Wheelchairs ensure more independent mobility and better quality of life for people with motor disabilities. Especially, for people with complex movement disorders, such as dyskinetic cerebral palsy (DCP) who lack the ability to walk or to handle objects, wheelchairs offer a means of integration into daily life. The mobility of DCP patients is based on a head-foot wheelchair steering system. In this work, a data logging system is proposed to capture data from human-wheelchair interaction for the head-foot steering system. Additionally, the data logger provides an interface to multiple Inertial Measurement Units (IMUs) placed on the body of the wheelchair user. The system provides accurate and real-time information from head-foot navigation system pressure sensors on the wheelchair during driving. This system was used as a tool to obtain further insights into wheelchair control and steering behavior of people diagnosed with DCP in comparison with a healthy subject.