Article
Kinematics of gait: new method for angle estimation based on accelerometers.
School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, Belgrade, Serbia.
Sensors (Impact Factor: 1.95). 01/2011; 11(11):1057185. DOI:10.3390/s111110571 Source: PubMed

Article: Nonlinear optimization for drift removal in estimation of gait kinematics based on accelerometers.
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ABSTRACT: A new data processing method is described for estimation of angles of leg segments, joint angles, and trajectories in the sagittal plane from data recorded by sensors units mounted at the lateral side of leg segments. Each sensor unit comprises a pair of threedimensional accelerometers which send data wirelessly to a PC. The accelerometer signals comprise timevarying and temperaturedependent offset, which leads to drift and diverged signals after integration. The key features of the proposed method are to model the offset by a slowly varying function of time (a cubic spline polynomial) and evaluate the polynomial coefficients by nonlinear numerical simplex optimization with the goal to reduce the drift in processed signals (angles and movement displacements). The angles and trajectories estimated by our method were compared with angles measured by an optical motion capture system. The comparison shows that the errors for angles (rms) were below 4° and the errors in stride length were below 2%. The algorithm developed is applicable for realtime and offline analysis of gait. The method does not need any adaptation with respect to gait velocity or individuality of gait.Journal of biomechanics 09/2012; · 2.66 Impact Factor  [show abstract] [hide abstract]
ABSTRACT: Measuring the kinematic parameters in unconstrained human motion is becoming crucial for providing feedback information in wearable robotics and sports monitoring. This paper presents a novel sensory fusion algorithm for assessing the orientations of human body segments in longterm human walking based on signals from wearable sensors. The basic idea of the proposed algorithm is to constantly fuse the measured segment's angular velocity and linear acceleration via known kinematic relations between segments. The wearable sensory system incorporates seven inertial measurement units attached to the human body segments and two instrumented shoe insoles. The proposed system was experimentally validated in a longterm walking on a treadmill and on a polygon with stairs simulating different activities in everyday life. The outputs were compared to the reference parameters measured by a stationary optical system. Results show accurate joint angle measurements (error median below 5°) in all evaluated walking conditions with no expressed drift over time.Computer methods and programs in biomedicine 12/2013; · 1.14 Impact Factor  [show abstract] [hide abstract]
ABSTRACT: The paper presents a multifunctional joint sensor with measurement adaptability for biological engineering applications, such as gait analysis, gesture recognition, etc. The adaptability is embodied in both static and dynamic environment measurements, both of body pose and in motion capture. Its multifunctional capabilities lay in its ability of simultaneous measurement of multiple degrees of freedom (MDOF) with a single sensor to reduce system complexity. The basic working mode enables 2DOF spatial angle measurement over big ranges and stands out for its applications on different joints of different individuals without recalibration. The optional advanced working mode enables an additional DOF measurement for various applications. By employing corrugated tube as the main body, the sensor is also characterized as flexible and wearable with less restraints. MDOF variations are converted to linear displacements of the sensing elements. The simple reconstruction algorithm and small outputs volume are capable of providing realtime angles and longterm monitoring. The performance assessment of the built prototype is promising enough to indicate the feasibility of the sensor.Sensors 01/2013; 13(11):1527489. · 1.95 Impact Factor
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