An adaptive-gain complementary filter for real-time human motion tracking with MARG sensors in free-living environments

A data-efficient and easy-to-use lip language interface based on wearable motion capture and speech movement reconstruction

Lip language recognition urgently needs wearable and easy-to-use interfaces for interference-free and high-fidelity lip-reading acquisition and to develop accompanying data-efficient decoder-modeling methods. Existing solutions suffer from unreliable lip reading, are data hungry, and exhibit poor generalization. Here, we propose a wearable lip language decoding technology that enables interference-free and high-fidelity acquisition of lip movements and data-efficient recognition of fluent lip language based on wearable motion capture and continuous lip speech movement reconstruction. The method allows us to artificially generate any wanted continuous speech datasets from a very limited corpus of word samples from users. By using these artificial datasets to train the decoder, we achieve an average accuracy of 92.0% across individuals (n = 7) for actual continuous and fluent lip speech recognition for 93 English sentences, even observing no training burn on users because all training datasets are artificially generated. Our method greatly minimizes users' training/learning load and presents a data-efficient and easy-to-use paradigm for lip language recognition.

Estimation of body segmental orientation for prosthetic gait using a nonlinear autoregressive neural network with exogenous inputs

Assessment of the prosthetic gait is an important clinical approach to evaluate the quality and functionality of the prescribed lower limb prosthesis as well as to monitor rehabilitation progresses following limb amputation. Limited access to quantitative assessment tools generally affects the repeatability and consistency of prosthetic gait assessments in clinical practice. The rapidly developing wearable technology industry provides an alternative to objectively quantify prosthetic gait in the unconstrained environment. This study employs a neural network-based model in estimating three-dimensional body segmental orientation of the lower limb amputees during gait. Using a wearable system with inertial sensors attached to the lower limb segments, thirteen individuals with lower limb amputation performed two-minute walk tests on a robotic foot and a passive foot. The proposed model replicates features of a complementary filter to estimate drift free three-dimensional orientation of the intact and prosthetic limbs. The results indicate minimal estimation biases and high correlation, validating the ability of the proposed model to reproduce the properties of a complementary filter while avoiding the drawbacks, most notably in the transverse plane due to gravitational acceleration and magnetic disturbance. Results of this study also demonstrates the capability of the well-trained model to accurately estimate segmental orientation, regardless of amputation level, in different types of locomotion task.

Robust Attitude and Heading Estimation under Dynamic Motion and Magnetic Disturbance

Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns.

Generating an Adaptive and Robust Walking Pattern for a Prosthetic Ankle-Foot by Utilizing a Nonlinear Autoregressive Network With Exogenous Inputs

One of the major challenges in developing powered lower limb prostheses is emulating the behavior of an intact lower limb with different walking speeds over diverse terrains. Numerous studies have been conducted on control algorithms in the field of rehabilitation robotics to achieve this overarching goal. Recent studies on powered prostheses have frequently used a hierarchical control scheme consisting of three control levels. Most control structures have at least one element of discrete transition properties that requires numerous sensors to improve classification accuracy, consequently increasing computational load and costs. In this study, we proposed a user-independent and free-mode method for eliminating the need to switch among different controllers. We constructed a database by using four OPAL wearable devices (Mobility Lab, APDM Inc., USA) for seven able-bodied subjects. We recorded the gait of each subject at three ambulation speeds during ground-level walking to train a nonlinear autoregressive network with an exogenous input recurrent neural network (NARX RNN) to estimate foot orientation (angular position) in the sagittal plane using shank angular velocity as external input. The trained NARX RNN estimated the foot orientation of all the subjects at different walking speeds over flat terrain with an average root-mean-square error (RMSE) of 2.1° ± 1.7°. The minimum correlation between the estimated and measured values was 86%. Moreover, a t-test showed that the error was normally distributed with a high certainty level (0.88 minimum p -value).

Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All

The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors. The aim of this work is to analyze the accuracy of ten SFAs while running under the best possible conditions (i.e., their parameter values are set using the orientation reference) in nine experimental scenarios including three rotation rates and three commercial products. The main finding is that parameter values must be specific for each SFA according to the experimental scenario to avoid errors comparable to those obtained when the default parameter values are used. Overall, when optimally tuned, no statistically significant differences are observed among the different SFAs in all tested experimental scenarios and the absolute errors are included between 3.8 deg and 7.1 deg. Increasing the rotation rate generally leads to a significant performance worsening. Errors are also influenced by the MIMU commercial model. SFA MATLAB implementations have been made available online.

Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation

Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filter's gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms.

Wireless Motion Capture System for Upper Limb Rehabilitation

This work is devoted to the presentation of a Wireless Sensor System implementation for upper limb rehabilitation to function as a complementary system for a patient’s progress supervision during rehabilitation exercises. A cost effective motion capture sensor node composed by a 9 Degrees-of-Freedom (DoF) Inertial Measurement Unit (IMU) is mounted on the patient’s upper limb segments and sends wirelessly the corresponding measured signals to a base station. The sensor orientation and the upper limb individual segments movement in 3-Dimensional (3D) space are derived by processing the sensors’ raw data. For the latter purpose, a biomechanical model which resembles that of a kinematic model of a robotic arm based on the Denavit-Hartenberg (DH) configuration is used to approximate in real time the upper limb movements. The joint angles of the upper limb model are estimated from the extracted sensor node’s orientation angles. The experimental results of a human performing common rehabilitation exercises using the proposed motion capture sensor node are compared with the ones using an off-the-shelf sensor. This comparison results to very low error rates with the root mean square error (RMSE) being about 0.02 m.

Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera

Using a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation, we propose to fuse a camera with low-cost inertial sensors based on a nonlinear complementary filter design. An implicit geometry measurement model is derived using two feature points in an image. This geometry measurement is fused with the angle rate measurement and vector measurement from inertial sensors using the proposed nonlinear complementary filter with only two parameters to be adjusted. The proposed nonlinear complementary filter is posed directly on the special orthogonal group SO(3). Based on the theory of nonlinear system stability analysis, the proposed filter ensures locally asymptotic stability. A quaternion-based discrete implementation of the filter is also given in this paper for computational efficiency. The proposed algorithm is validated using a smartphone with built-in inertial sensors and a rear camera. The experimental results indicate that the proposed algorithm outperforms all the compared counterparts in estimated accuracy and provides competitive computational complexity.

A Wearable Sensor System to Measure Step-Based Gait Parameters for Parkinson's Disease Rehabilitation

Spatiotemporal parameters of gait serve as an important biomarker to monitor gait impairments as well as to develop rehabilitation systems. In this work, we developed a computationally-efficient algorithm (SDI-Step) that uses segmented double integration to calculate step length and step time from wearable inertial measurement units (IMUs) and assessed its ability to reliably and accurately measure spatiotemporal gait parameters. Two data sets that included simultaneous measurements from wearable sensors and from a laboratory-based system were used in the assessment. The first data set utilized IMU sensors and a GAITRite mat in our laboratory to monitor gait in fifteen participants: 9 young adults (YA₁) (5 females, 4 males, age 23.6 ± 1 years), and 6 people with Parkinson's disease (PD) (3 females, 3 males, age 72.3 ± 6.6 years). The second data set, which was accessed from a publicly-available repository, utilized IMU sensors and an optoelectronic system to monitor gait in five young adults (YA₂) (2 females, 3 males, age 30.5 ± 3.5 years). In order to provide a complete representation of validity, we used multiple statistical analyses with overlapping metrics. Gait parameters such as step time and step length were calculated and the agreement between the two measurement systems for each gait parameter was assessed using Passing-Bablok (PB) regression analysis and calculation of the Intra-class Correlation Coefficient (ICC (2,1)) with 95% confidence intervals for a single measure, absolute-agreement, 2-way mixed-effects model. In addition, Bland-Altman (BA) plots were used to visually inspect the measurement agreement. The values of the PB regression slope were close to 1 and intercept close to 0 for both step time and step length measures. The results obtained using ICC (2,1) for step length showed a moderate to excellent agreement for YA (between 0.81 and 0.95) and excellent agreement for PD (between 0.93 and 0.98), while both YA and PD had an excellent agreement in step time ICCs (>0.9). Finally, examining the BA plots showed that the measurement difference was within the limits of agreement (LoA) with a 95% probability. Results from this preliminary study indicate that using the SDI-Step algorithm to process signals from wearable IMUs provides measurements that are in close agreement with widely-used laboratory-based systems and can be considered as a valid tool for measuring spatiotemporal gait parameters.

A wearable motion capture device able to detect dynamic motion of human limbs

Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs.

Current wearable motion capture technologies are unable to accurately detect dynamic motion of human limbs due to drift and instability problems. Here, the authors report a wearable motion capture device combining tri-axis velocity sensor and inertial sensors for accurate 3D limb motion capture.

Accuracy of the Orientation Estimate Obtained Using Four Sensor Fusion Filters Applied to Recordings of Magneto-Inertial Sensors Moving at Three Rotation Rates

Magneto-Inertial technology is a well-established alternative to optical motion capture for human motion analysis applications since it allows prolonged monitoring in free-living conditions. Magneto and Inertial Measurement Units (MIMUs) integrate a triaxial accelerometer, a triaxial gyroscope and a triaxial magnetometer in a single and lightweight device. The orientation of the body to which a MIMU is attached can be obtained by combining its sensor readings within a sensor fusion framework. Despite several sensor fusion implementations have been proposed, no well-established conclusion about the accuracy level achievable with MIMUs has been reached yet. The aim of this preliminary study was to perform a direct comparison among four popular sensor fusion algorithms applied to the recordings of MIMUs rotating at three different rotation rates, with the orientation provided by a stereophotogrammetric system used as a reference. A procedure for suboptimal determination of the parameter filter values was also proposed. The findings highlighted that all filters exhibited reasonable accuracy (rms errors <; 6.4°). Moreover, in accordance with previous studies, every algorithm's accuracy worsened as the rotation rate increased. At the highest rotation rate, the algorithm from Sabatini (2011) showed the best performance with errors smaller than 4.1° rms.

A Wearable Flow-MIMU Device for Monitoring Human Dynamic Motion

For personalized rehabilitation or sports training, it is necessary to monitor dynamic motions and track postures of human body and limbs. Traditional methods using micro inertial sensors usually suffer from drift problems in tracking dynamic motions. In this paper, a wearable flow-MIMU human motion capture device is proposed by incorporating micro flow sensor with micro inertial measurement unit (MIMU). Motion velocity is detected by a micro flow sensor and utilized to figure out the motion acceleration. The gravity accelerations are extracted by eliminating the motion accelerations from the accelerometer outputs. Finally, posture estimation is implemented by using a tailor-designed Kalman-based data fusion of the gyroscope outputs and the extracted gravity accelerations. The flow-MIMU device with wireless communication is designed like a watch to be wearable. Experimental results validate that the motion velocity, acceleration and posture of human limb are determined accurately and free of accumulative error in monitoring of dynamic motions using the proposed method. The wearable flow-MIMU device provides an advantageous monitoring approach for applications of personalized rehabilitation, sports training, intelligent prosthetics, etc.

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.

A First-Order Differential Data Processing Method for Accuracy Improvement of Complementary Filtering in Micro-UAV Attitude Estimation

There are many algorithms that can be used to fuse sensor data. The complementary filtering algorithm has low computational complexity and good real-time performance characteristics. It is very suitable for attitude estimation of small unmanned aerial vehicles (micro-UAVs) equipped with low-cost inertial measurement units (IMUs). However, its low attitude estimation accuracy severely limits its applications. Though, many methods have been proposed by researchers to improve attitude estimation accuracy of complementary filtering algorithms, there are few studies that aim to improve it from the data processing aspect. In this paper, a real-time first-order differential data processing algorithm is proposed for gyroscope data, and an adaptive adjustment strategy is designed for the parameters in the algorithm. Besides, the differential-nonlinear complementary filtering (D-NCF) algorithm is proposed by combine the first-order differential data processing algorithm with the basic nonlinear complementary filtering (NCF) algorithm. The experimental results show that the first-order differential data processing algorithm can effectively correct the gyroscope data, and the Root Mean Square Error (RMSE) of attitude estimation of the D-NCF algorithm is smaller than when the NCF algorithm is used. The RMSE of the roll angle decreases from 1.1653 to 0.5093, that of the pitch angle decreases from 2.9638 to 1.5542, and that of the yaw angle decreases from 0.9398 to 0.6827. In general, the attitude estimation accuracy of D-NCF algorithm is higher than that of the NCF algorithm.

A Virtual Reality Soldier Simulator with Body Area Networks for Team Training

Soldier-based simulators have been attracting increased attention recently, with the aim of making complex military tactics more effective, such that soldiers are able to respond rapidly and logically to battlespace situations and the commander’s decisions in the battlefield. Moreover, body area networks (BANs) can be applied to collect the training data in order to provide greater access to soldiers’ physical actions or postures as they occur in real routine training. Therefore, due to the limited physical space of training facilities, an efficient soldier-based training strategy is proposed that integrates a virtual reality (VR) simulation system with a BAN, which can capture body movements such as walking, running, shooting, and crouching in a virtual environment. The performance evaluation shows that the proposed VR simulation system is able to provide complete and substantial information throughout the training process, including detection, estimation, and monitoring capabilities.

A Fuzzy Tuned and Second Estimator of the Optimal Quaternion Complementary Filter for Human Motion Measurement with Inertial and Magnetic Sensors

To accurately measure human motion at high-speed, we proposed a simple structure complementary filter, named the Fuzzy Tuned and Second EStimator of the Optimal Quaternion Complementary Filter (FTECF). The FTECF is applicable to inertial and magnetic sensors, which include tri-axis gyroscopes, tri-axis accelerometers, and tri-axis magnetometers. More specifically, the proposed method incorporates three parts, the input quaternion, the reference quaternion, and the fuzzy logic algorithm. At first, the input quaternion was calculated with gyroscopes. Then, the reference quaternion was calculated by applying the Second EStimator of the Optimal Quaternion (ESOQ-2) algorithm on accelerometers and magnetometers. In addition, we added compensation for accelerometers in the ESOQ-2 algorithm so as to eliminate the effects of limb motion acceleration in high-speed human motion measurements. Finally, the fuzzy logic was utilized to calculate the fusion factor for a complementary filter, so as to adaptively fuse the input quaternion with the reference quaternion. Additionally, the overall algorithm design is more simplified than traditional methods. Confirmed by the experiments, using a commercial inertial and magnetic sensors unit and an optical motion capture system, the efficiency of the proposed method was more improved than two well-known methods. The root mean square error (RMSE) of the FTECF was less than 2.2° and the maximum error was less than 5.4°.

Dynamic Toolface Estimation for Rotary Steerable Drilling System

In drilling engineering, Toolface is an angle used to describe bit direction. It is a challenging task to accurately estimate Toolface while drilling because of the downhole harsh conditions, but it is a primary step for the dynamic point-the-bit rotary steerable system (DPRSS). A new dynamic Toolface estimator is present, which fuses measurements from two accelerometers and one gyro. A dual-accelerometer Toolface measuring method is designed to compensate the circumferential acceleration of DPRSS. A nonlinear Complementary Filter (CF) is used to suppress the effect of vibration and axial acceleration. The frequency-domain characteristics of nonlinear CF are analyzed and its natural frequency is determined adaptively based on real time drilling conditions. This new estimator is validated on a DPRSS prototype under typical drilling modes; it is demonstrated with high robustness and follows the references satisfactorily.

Adaptive Absolute Ego-Motion Estimation Using Wearable Visual-Inertial Sensors for Indoor Positioning

This paper proposes an adaptive absolute ego-motion estimation method using wearable visual-inertial sensors for indoor positioning. We introduce a wearable visual-inertial device to estimate not only the camera ego-motion, but also the 3D motion of the moving object in dynamic environments. Firstly, a novel method dynamic scene segmentation is proposed using two visual geometry constraints with the help of inertial sensors. Moreover, this paper introduces a concept of "virtual camera" to consider the motion area related to each moving object as if a static object were viewed by a "virtual camera". We therefore derive the 3D moving object's motion from the motions for the real and virtual camera because the virtual camera's motion is actually the combined motion of both the real camera and the moving object. In addition, a multi-rate linear Kalman-filter (MR-LKF) as our previous work was selected to solve both the problem of scale ambiguity in monocular camera tracking and the different sampling frequencies of visual and inertial sensors. The performance of the proposed method is evaluated by simulation studies and practical experiments performed in both static and dynamic environments. The results show the method's robustness and effectiveness compared with the results from a Pioneer robot as the ground truth.

Player Monitoring in Indoor Team Sports: Concurrent Validity of Inertial Measurement Units to Quantify Average and Peak Acceleration Values

The increasing interest in assessing physical demands in team sports has led to the development of multiple sports related monitoring systems. Due to technical limitations, these systems primarily could be applied to outdoor sports, whereas an equivalent indoor locomotion analysis is not established yet. Technological development of inertial measurement units (IMU) broadens the possibilities for player monitoring and enables the quantification of locomotor movements in indoor environments. The aim of the current study was to validate an IMU measuring by determining average and peak human acceleration under indoor conditions in team sport specific movements. Data of a single wearable tracking device including an IMU (Optimeye S5, Catapult Sports, Melbourne, Australia) were compared to the results of a 3D motion analysis (MA) system (Vicon Motion Systems, Oxford, UK) during selected standardized movement simulations in an indoor laboratory (n = 56). A low-pass filtering method for gravity correction (LF) and two sensor fusion algorithms for orientation estimation [Complementary Filter (CF), Kalman-Filter (KF)] were implemented and compared with MA system data. Significant differences (p < 0.05) were found between LF and MA data but not between sensor fusion algorithms and MA. Higher precision and lower relative errors were found for CF (RMSE = 0.05; CV = 2.6%) and KF (RMSE = 0.15; CV = 3.8%) both compared to the LF method (RMSE = 1.14; CV = 47.6%) regarding the magnitude of the resulting vector and strongly emphasize the implementation of orientation estimation to accurately describe human acceleration. Comparing both sensor fusion algorithms, CF revealed slightly lower errors than KF and additionally provided valuable information about positive and negative acceleration values in all three movement planes with moderate to good validity (CV = 3.9 – 17.8%). Compared to x- and y-axis superior results were found for the z-axis. These findings demonstrate that IMU-based wearable tracking devices can successfully be applied for athlete monitoring in indoor team sports and provide potential to accurately quantify accelerations and decelerations in all three orthogonal axes with acceptable validity. An increase in accuracy taking magnetometers in account should be specifically pursued by future research.

Online Wavelet Complementary velocity Estimator

In this paper, we have proposed a new online Wavelet Complementary velocity Estimator (WCE) over position and acceleration data gathered from an electro hydraulic servo shaking table. This is a batch estimator type that is based on the wavelet filter banks which extract the high and low resolution of data. The proposed complementary estimator combines these two resolutions of velocities which acquired from numerical differentiation and integration of the position and acceleration sensors by considering a fixed moving horizon window as input to wavelet filter. Because of using wavelet filters, it can be implemented in a parallel procedure. By this method the numerical velocity is estimated without having high noise of differentiators, integration drifting bias and with less delay which is suitable for active vibration control in high precision Mechatronics systems by Direct Velocity Feedback (DVF) methods. This method allows us to make velocity sensors with less mechanically moving parts which makes it suitable for fast miniature structures. We have compared this method with Kalman and Butterworth filters over stability, delay and benchmarked them by their long time velocity integration for getting back the initial position data.

Wearable Heading Estimation for Motion Tracking in Health Care by Adaptive Fusion of Visual-Inertial Measurements

The increasing demand for health informatics has become a far-reaching trend in the ageing society. The utilization of wearable sensors enables monitoring senior people daily activities in free-living environments, conveniently and effectively. Among the primary health-care sensing categories, the wearable visual-inertial modality for human motion tracking gradually exerts promising potentials. In this paper, we present a novel wearable heading estimation strategy to track the movements of human limbs. It adaptively fuses inertial measurements with visual features following locality constraints. Body movements are classified into two types: general motion (which consists of both rotation and translation). or degenerate motion (which consists of only rotation). A specific number of feature correspondences between camera frames are adaptively chosen to satisfy both the feature descriptor similarity constraint and the locality constraint. The selected feature correspondences and inertial quaternions are employed to calculate the initial pose, followed by the coarse-to-fine procedure to iteratively remove visual outliers. Eventually, the ultimate heading is optimized using the correct feature matches. The proposed method has been thoroughly evaluated on the straight-line, rotatory and ambulatory movement scenarios. As the system is lightweight and requires small computational resources, it enables effective and unobtrusive human motion monitoring, especially for the senior citizens in the long-term rehabilitation.

How Magnetic Disturbance Influences the Attitude and Heading in Magnetic and Inertial Sensor-Based Orientation Estimation

With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method.

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).

Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations

BACKGROUND

Model based analysis of human upper limb movements has key importance in understanding the motor control processes of our nervous system. Various simulation software packages have been developed over the years to perform model based analysis. These packages provide computationally intensive-and therefore off-line-solutions to calculate the anatomical joint angles from motion captured raw measurement data (also referred as inverse kinematics). In addition, recent developments in inertial motion sensing technology show that it may replace large, immobile and expensive optical systems with small, mobile and cheaper solutions in cases when a laboratory-free measurement setup is needed. The objective of the presented work is to extend the workflow of measurement and analysis of human arm movements with an algorithm that allows accurate and real-time estimation of anatomical joint angles for a widely used OpenSim upper limb kinematic model when inertial sensors are used for movement recording.

METHODS

The internal structure of the selected upper limb model is analyzed and used as the underlying platform for the development of the proposed algorithm. Based on this structure, a prototype marker set is constructed that facilitates the reconstruction of model-based joint angles using orientation data directly available from inertial measurement systems. The mathematical formulation of the reconstruction algorithm is presented along with the validation of the algorithm on various platforms, including embedded environments.

RESULTS

Execution performance tables of the proposed algorithm show significant improvement on all tested platforms. Compared to OpenSim's Inverse Kinematics tool 50-15,000x speedup is achieved while maintaining numerical accuracy.

CONCLUSIONS

The proposed algorithm is capable of real-time reconstruction of standardized anatomical joint angles even in embedded environments, establishing a new way for complex applications to take advantage of accurate and fast model-based inverse kinematics calculations.

On the Orientation Error of IMU: Investigating Static and Dynamic Accuracy Targeting Human Motion

The accuracy in orientation tracking attainable by using inertial measurement units (IMU) when measuring human motion is still an open issue. This study presents a systematic quantification of the accuracy under static conditions and typical human dynamics, simulated by means of a robotic arm. Two sensor fusion algorithms, selected from the classes of the stochastic and complementary methods, are considered. The proposed protocol implements controlled and repeatable experimental conditions and validates accuracy for an extensive set of dynamic movements, that differ in frequency and amplitude of the movement. We found that dynamic performance of the tracking is only slightly dependent on the sensor fusion algorithm. Instead, it is dependent on the amplitude and frequency of the movement and a major contribution to the error derives from the orientation of the rotation axis w.r.t. the gravity vector. Absolute and relative errors upper bounds are found respectively in the range [0.7° ÷ 8.2°] and [1.0° ÷ 10.3°]. Alongside dynamic, static accuracy is thoroughly investigated, also with an emphasis on convergence behavior of the different algorithms. Reported results emphasize critical issues associated with the use of this technology and provide a baseline level of performance for the human motion related application.

Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs

Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the "tilt" quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.