Validation of an inertial sensor-based platform to acquire kinematic information for human joint angle estimation (original) (raw)
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Human Joint Angle Estimation with Inertial Sensors and Validation with A Robot Arm
IEEE Transactions on Biomedical Engineering, 2015
Traditionally, human movement has been captured primarily by motion capture systems. These systems are costly, require fixed cameras in a controlled environment, and suffer from occlusion. Recently, the availability of low-cost wearable inertial sensors containing accelerometers, gyroscopes, and magnetometers have provided an alternative means to overcome the limitations of motion capture systems. Wearable inertial sensors can be used anywhere, cannot be occluded, and are low cost. Several groups have described algorithms for tracking human joint angles. We previously described a novel approach based on a kinematic arm model and the Unscented Kalman Filter (UKF). Our proposed method used a minimal sensor configuration with one sensor on each segment. This article reports significant improvements in both the algorithm and the assessment. The new model incorporates gyroscope and accelerometer random drift models, imposes physical constraints on the range of motion for each joint, and uses zero-velocity updates to mitigate the effect of sensor drift. A high-precision industrial robot arm precisely quantifies the performance of the tracker during slow, normal, and fast movements over continuous 15 minute recording durations. The agreement between the estimated angles from our algorithm and the high-precision robot arm reference was excellent. On average, the tracker attained an RMS angle error of about 3 • for all six angles. The UKF performed slightly better than the more common Extended Kalman Filter (EKF).
Upper limb joint angle tracking with inertial sensors
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
Wearable inertial systems have recently been used to track human movement in and outside of the laboratory. Continuous monitoring of human movement can provide valuable information relevant to individual's level of physical activity and functional ability. Traditionally, orientation has been calculated by integrating the angular velocity from gyroscopes. However, a small drift in the measured velocity leads to large integration errors that grow with time. To compensate for that drift, complementary data from accelerometers are normally fused into the tracking systems using the Kalman or extended Kalman filter (EKF). In this study, we combine kinematic models designed for control of robotic arms with the unscented Kalman filter (UKF) to continuously estimate the angles of human shoulder and elbow using two wearable sensors. This methodology can easily be generalized to track other human joints. We validate the method with an optical motion tracking system and demonstrate correlation consistently greater than 0.9 between the two systems. 978-1-4244-4122-8/11/$26.00 ©2011 IEEE 5629 33rd Annual International Conference of the IEEE EMBS
Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review
Sensors
Inertial measurement units (IMUs) have become the mainstay in human motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; and secondly, to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of 4° across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope dat...
Reducing Drifts in the Inertial Measurements of Wrist and Elbow Positions
IEEE Transactions on Instrumentation and Measurement, 2000
In this paper, we present an inertial-sensor-based monitoring system for measuring the movement of human upper limbs. Two wearable inertial sensors are placed near the wrist and elbow joints, respectively. The measurement drift in segment orientation is dramatically reduced after a Kalman filter is applied to estimate inclinations using accelerations and turning rates from gyroscopes. Using premeasured lengths of the upper and lower arms, we compute the position of the wrist and elbow joints via a proposed kinematic model. Experimental results demonstrate that this new motion capture system, in comparison to an optical motion tracker, possesses an RMS position error of less than 0.009 m, with a drift of less than 0.005 ms −1 in five daily activities. In addition, the RMS angle error is less than 3 • . This indicates that the proposed approach has performed well in terms of accuracy and reliability.
Inertial sensors for motion detection of human upper limbs
2007
Purpose -This paper seeks to present an inertial motion tracking system for monitoring movements of human upper limbs in order to support a homebased rehabilitation scheme in which the recovery of stroke patients' motor function through repetitive exercises needs to be continuously monitored and appropriately evaluated. Design/methodology/approach -Two inertial sensors are placed on the upper and lower arms in order to obtain acceleration and turning rates. Then the position of the upper limbs can be deduced by using the kinematical model of the upper limbs that was designed in the previous paper. The tracking system starts from inertial data acquisition and pre-filtering, followed by a number of processes such as transformation of coordinate systems of sensor data, and kinematical modelling and optimization of position estimation. Findings -The motion detector using the proposed kinematic model only has drifts in the measurements. Fusion of acceleration and orientation data can effectively solve the drift problem without the involvement of a Kalman filter.
Inertial measurements of upper limb motion
Medical & Biological Engineering & Computing, 2006
We present an inertial sensor based monitoring system for measuring upper limb movements in real time. The purpose of this study is to develop a motion tracking device that can be integrated within a home-based rehabilitation system for stroke patients. Human upper limbs are represented by a kinematic chain in which there are four joint variables to be considered: three for the shoulder joint and one for the elbow joint. Kinematic models are built to estimate upper limb motion in 3-D, based on the inertial measurements of the wrist motion. An efficient simulated annealing optimisation method is proposed to reduce errors in estimates. Experimental results demonstrate the proposed system has less than 5% errors in most motion manners, compared to a standard motion tracker.
Applications of wearable inertial sensors in estimation of upper limb movements
Biomedical Signal Processing and Control, 2006
A new data fusion-based tracking algorithm is proposed, based on two wearable inertial sensors that are placed around the wrist and elbow joints, respectively. Assuming that the lengths of two segments of an upper limb are known, measurements of gyro turning rates can be used to locate the wrist and elbow joints via an established kinematic model. To determine the position of a translated and rotated shoulder joint, an equality-constrained optimisation technique is then proposed to seek an optimal solution, incorporating measurements from the tri-axial accelerometers. Experimental results demonstrate that the algorithm is capable of providing consistent motion tracking of human arms without drifts in 45 s, where each standard deviation is less than half of the corresponding mean value of the Euclidean distance between the estimated joint position and the origin of the world coordinate system. #
Health and technology, 2015
This paper presents the development of a system that uses inertial sensors, wireless transceivers and virtual models to monitor the exercises of motor rehabilitation of the upper limbs based on Kabat's method. This method involves performing rehabilitation complex exercises that cannot be easily reproduced by the patient, requiring permanent assistance of a qualified professional. However, it is very expensive to have a professional expert assisting the patient throughout the treatment. Therefore, the development of technologies to monitor this type of exercise is necessary. The Kabat's method has several applications, e.g. in motor rehabilitation of stroke patients. Stroke is considered the second most common cardiovascular disorder and affects about 9.6 million people in Europe alone, and an estimated 6 million people worldwide die from this disorder. Also, the natural aging process increases the number of strokes, and the demand for healthcare and motor rehabilitation services. To minimize this problem, we propose an experimental system consisting of inertial sensors, wireless transceivers and virtual models according to the models of Denavit & Hartenberg and Euler Angles & Tait Bryan. Through inertial sensors, this system can characterize the movement performed by the patient, compare it with a predefined motion and then indicate if the motor system performed the correct movement. The patients monitor their own movements and the movement pattern (correct movement). All movements are stored in a database allowing continuous checking by a qualified professional. Several experimental tests have shown that the average system error was 0.97°, which is suitable to the proposed system.
Use of multiple wearable inertial sensors in upper limb motion tracking
Medical Engineering & Physics, 2008
This paper presents a new human motion tracking system using two wearable inertial sensors that are placed near the wrist and elbow joints of the upper limb. Each inertial sensor consists of a tri-axial accelerometer, a tri-axial gyroscope and a tri-axial magnetometer. The turning rates of the gyroscope were utilised for localising the wrist and elbow joints on the assumption that the two upper limb segment lengths are known a priori. To determine the translation and rotation of the shoulder joint, an equality-constrained optimisation technique is adopted to find an optimal solution, incorporating measurements from the tri-axial accelerometer and gyroscope. Experimental results demonstrate that this new system, compared to an optical motion tracker, has RMS position errors that are normally less than 0.01 m, and RMS angle errors that are 2.5-4.8 • .
Current Directions in Biomedical Engineering
Many people are familiar with the feeling of instability, pain, or subsidence in the knee joint after a knee injury. There are many different methods for examining the knee, such as the drawer test or the Lachman test [1], before and after surgery. While these tests can be used in short term and provide useful results, motion capture systems can be used as an alternative measurement method, almost as a substitute in longer term. These include marker-based or mechanica l systems, which achieve good measurement results but are expensive and inflexible. For this reason, this paper presents a mobile, easy-to-use motion and easy expandable capture system using a low-cost IMU-based development system. The modular design of the system allows it to be adapted to each body region with simple adjustments. However, the present work focuses on applications for capturing human motion sequences and deriving three joint angles of the lower extremities to detect malposition.