Upper body motion tracking with inertial sensors (original) (raw)
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Kinematic model aided inertial motion tracking of human upper limb
2005 IEEE International Conference on Information Acquisition, 2005
A new motion tracking framework has been developed to estimate the position and orientation of human upper limb. This method fuses data from on-board accelerometers and gyroscopes, which are accommodated in a commercially available inertial sensor MT9. Human upper limb motion can be represented by a kinematic chain in which six joint variables are to be considered: three for the shoulder and three for the elbow. Based on measurements of the inertial sensor placed on the wrist, we then obtain the positions of the wrist and elbow. An extended Kalman filter then fuses the data from these sensors in order to reduce errors and noise in measurements. Preliminary results demonstrate the favorable performance of the proposed strategy.
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
Sensors (Basel, Switzerland), 2017
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).
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
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.
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 • .
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).
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. #
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.
This paper shows the results of a set of experiments aimed t at calibrating and validating an inertial sensor-based motion capture system that is used to capture and analyze the elbow joint flexion/extension motion. An experimental platform was constructed that provides accurate angular position information for reference purposes. Results obtained have an average error of 2.14 degrees when the arm is guided by a servomotor that rotates at 1 radian per second. The system also has an RMSE of 3.3, 4.9, 6.4 and 7.7 degrees for speeds of 2, 3, 4 and 5 radians per second respectively. Results show that the errors are acceptable to use a kinematic information capture platform en with inertial sensors that is focused on monitoring the recovery of the motor function in the upper limbs through physical therapy.
Measurement Science and Technology, 2013
For all segments and tests, a modified Kalman filter and a quasi-static sensor fusion algorithm were equally accurate (precision and accuracy ∼2-3 • ) compared to normalized least mean squares filtering, recursive least-squares filtering and standard Kalman filtering. The aims were to: (1) compare adaptive filtering techniques used for sensor fusion and (2) evaluate the precision and accuracy for a chosen adaptive filter. Motion sensors (based on inertial measurement units) are limited by accumulative integration errors arising from sensor bias. This drift can partly be handled with adaptive filtering techniques. To advance the measurement technique in this area, a new modified Kalman filter is developed. Differences in accuracy were observed during different tests especially drift in the internal/external rotation angle. This drift can be minimized if the sensors include magnetometers.