A fast quaternion-based orientation optimizer via virtual rotation for human motion tracking

INERTIAL SENSOR-BASED MOTION ANALYSIS SYSTEM OF BRIDGE-STYLE MOVEMENT FOR REHABILITATION TREATMENTS

In the clinical course of the treatment, impartial representation of the patients’ rehabilitation state is a necessary condition for taking the best treatment to match the state of the current recovery. Bridge-style movement is one of the earliest training programs of the bed position change and is also the basis of successful standing and walking training because the bridge-style movement can inhibit the spasticity pattern of lower limb extensors and improve the control and coordination ability from the pelvis to lower limb. However, patients’ bridge-style movement planning for the current rehabilitation state largely depends on therapists’ clinical experience and subjective that may deteriorate the rehabilitation effect. Thus, it is necessary for hemiplegic patients to develop quantitative motor function assessment to judge its current rehabilitation state. This paper proposes a quantitative evaluating method to detect patients’ bridge-style movement posture and analyze their motion abilities. The real-time postural change of the bridge-style movement can be acquired by the inertial sensors attached to the waist, thigh, and crus. The bridge-style movement process of patients is recorded and analyzed by the software processing program. Finally, the experiment can be carried out to verify the feasibility and correctness of the evaluation method. The experimental results show that the evaluation method can judge patients’ current motion ability and rehabilitation state. And it is helpful for therapists to carry out targeted training for patients’ state.

Robust Inertial Measurement Unit-Based Attitude Determination Kalman Filter for Kinematically Constrained Links

The external acceleration of a fast-moving body induces uncertainty in attitude determination based on inertial measurement unit (IMU) signals and thus, frequently degrades the determination accuracy. Although previous works adopt acceleration-compensating mechanisms to deal with this problem, they cannot completely eliminate the uncertainty as they are, inherently, approaches to an underdetermined problem. This paper presents a novel constraint-augmented Kalman filter (KF) that eliminates the acceleration-induced uncertainty for a robust IMU-based attitude determination when IMU is attached to a constrained link. Particularly, this research deals with an acceleration-level kinematic constraint derived on the basis of a ball joint. Experimental results demonstrate the superiority of the proposed constrained KF over the conventional unconstrained KF: The average accuracy improved by 1.88° with a maximum of 4.18°. More importantly, whereas the accuracy of conventional KF is dependent to some extent on test acceleration conditions, that of the proposed KF is independent of these conditions. Due to the robustness of the proposed KF, it may be applied when accurate attitude estimation is needed regardless of dynamic conditions.

Effect of Strapdown Integration Order and Sampling Rate on IMU-Based Attitude Estimation Accuracy

This paper deals with the strapdown integration of attitude estimation Kalman filter (KF) based on inertial measurement unit (IMU) signals. In many low-cost wearable IMU applications, a first-order is selected for strapdown integration, which may degrade attitude estimation performance in high-speed angular motions. The purpose of this research is to provide insights into the effect of the strapdown integration order and sampling rate on the attitude estimation accuracy for low-cost IMU applications. Experimental results showed that the effect of integration order was small when the angular velocity was low and the sampling rate was large. However, as the angular velocity increased and the sampling rate decreased, the effect of integration order increased, i.e., obviously, the third-order KF resulted in better estimations than the first-order KF. When comparing the case where both transient matrix and process noise covariance matrix are applied to the corresponding order and the case where only the transient matrix is applied to the corresponding order but the process noise covariance matrix for the first-order is still used, both cases had almost equivalent estimation accuracy. However, in terms of the calculation cost, the latter case was more economical than the former, particularly for the third-order KF (i.e., the ratio of the former to the latter is 1.22 to 1).

Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor

Walking speed is widely used to study human health status. Wearable inertial measurement units (IMU) are promising tools for the ambulatory measurement of walking speed. Among wearable inertial sensors, the ones worn on the wrist, such as a watch or band, have relatively higher potential to be easily incorporated into daily lifestyle. Using the arm swing motion in walking, this paper proposes a regression model-based method for longitudinal walking speed estimation using a wrist-worn IMU. A novel kinematic variable is proposed, which finds the wrist acceleration in the principal axis (i.e. the direction of the arm swing). This variable (called pca-acc) is obtained by applying sensor fusion on IMU data to find the orientation followed by the use of principal component analysis. An experimental evaluation was performed on 15 healthy young subjects during free walking trials. The experimental results show that the use of the proposed pca-acc variable can significantly improve the walking speed estimation accuracy when compared to the use of raw acceleration information (p<0.01). When Gaussian process regression is used, the resulting walking speed estimation accuracy and precision is about 5.9% and 4.7%, respectively.

A fast Adaptive-Gain Orientation Filter of inertial/magnetic data for human motion tracking in free-living environments

High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as Adaptive-Gain Orientation Filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for Earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.

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

High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.

Variable-State-Dimension Kalman-based Filter for orientation determination using inertial and magnetic sensors

In this paper a quaternion-based Variable-State-Dimension Extended Kalman Filter (VSD-EKF) is developed for estimating the three-dimensional orientation of a rigid body using the measurements from an Inertial Measurement Unit (IMU) integrated with a triaxial magnetic sensor. Gyro bias and magnetic disturbances are modeled and compensated by including them in the filter state vector. The VSD-EKF switches between a quiescent EKF, where the magnetic disturbance is modeled as a first-order Gauss-Markov stochastic process (GM-1), and a higher-order EKF where extra state components are introduced to model the time-rate of change of the magnetic field as a GM-1 stochastic process, namely the magnetic disturbance is modeled as a second-order Gauss-Markov stochastic process (GM-2). Experimental validation tests show the effectiveness of the VSD-EKF, as compared to either the quiescent EKF or the higher-order EKF when they run separately.

Kalman-filter-based orientation determination using inertial/magnetic sensors: observability analysis and performance evaluation

In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical.

Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing

User-worn sensing units composed of inertial and magnetic sensors are becoming increasingly popular in various domains, including biomedical engineering, robotics, virtual reality, where they can also be applied for real-time tracking of the orientation of human body parts in the three-dimensional (3D) space. Although they are a promising choice as wearable sensors under many respects, the inertial and magnetic sensors currently in use offer measuring performance that are critical in order to achieve and maintain accurate 3D-orientation estimates, anytime and anywhere. This paper reviews the main sensor fusion and filtering techniques proposed for accurate inertial/magnetic orientation tracking of human body parts; it also gives useful recipes for their actual implementation.

A portable device for the clinical assessment of upper limb motion and muscle synergies

We present a device for recording and analyzing upper limb movements and muscle activities in a single unit. The device's outputs are related to aspects of clinical assessment such as joint coordination, fatigue and muscle synergies. A comparison with an optoelectronic motion capture system was also carried out during a hand to mouth and a hand to contralateral shoulder task. High correlation was found for the elbow angles, while analysis of the root mean square errors indicated that the angular outputs of the device were overestimated compared to the angles calculated using the optoelectronic system. Biceps and triceps co-contraction patterns were also observed during the hand to mouth task. Applications to the clinical assessment and monitoring of neurological disorders are discussed.