Estimation and visualization of sagittal kinematics of lower limbs orientation using body-fixed sensors

A new method of estimating lower limbs orientations using a combination of accelerometers and gyroscopes is presented. The model is based on estimating the accelerations of ankle and knee joints by placing virtual sensors at the centers of rotation. The proposed technique considers human locomotion and biomechanical constraints, and provides a solution to fusing the data of gyroscopes and accelerometers that yields stable and drift-free estimates of segment orientation. The method was validated by measuring lower limb motions of eight subjects, walking at three different speeds, and comparing the results with a reference motion measurement system. The results are very close to those of the reference system presenting very small errors (Shank: rms = 1.0, Thigh: rms = 1.6 degrees) and excellent correlation coefficients (Shank: r = 0.999, Thigh: r = 0.998). Technically, the proposed ambulatory system is portable, easily mountable, and can be used for long-term monitoring without hindrance to natural activities. Finally, a gait analysis tool was designed to visualize the motion data as synthetic skeletons performing the same actions as the subjects.

Measuring orientation of human body segments using miniature gyroscopes and accelerometers

In the medical field, there is a need for small ambulatory sensor systems for measuring the kinematics of body segments. Current methods for ambulatory measurement of body orientation have limited accuracy when the body moves. The aim of the paper was to develop and validate a method for accurate measurement of the orientation of human body segments using an inertial measurement unit (IMU). An IMU containing three single-axis accelerometers and three single-axis micromachined gyroscopes was assembled in a rectangular box, sized 20 x 20 x 30 mm. The presented orientation estimation algorithm continuously corrected orientation estimates obtained by mathematical integration of the 3D angular velocity measured using the gyroscopes. The correction was performed using an inclination estimate continuously obtained using the signal of the 3D accelerometer. This reduces the integration drift that originates from errors in the angular velocity signal. In addition, the gyroscope offset was continuously recalibrated. The method was realised using a Kalman filter that took into account the spectra of the signals involved as well as a fluctuating gyroscope offset. The method was tested for movements of the pelvis, trunk and forearm. Although the problem of integration drift around the global vertical continuously increased in the order of 0.50 degrees s(-1), the inclination estimate was accurate within 3 degrees RMS. It was shown that the gyroscope offset could be estimated continuously during a trial. Using an initial offset error of 1 rad s(-1), after 2 min the off-set error was roughly 5% of the original offset error. Using the Kalman filter described, an accurate and robust system for ambulatory motion recording can be realised.

Oculo-motor stabilization reflexes: integration of inertial and visual information

STABILIZATION OF GAZE IS A FUNDAMENTAL REQUIREMENT OF AN ACTIVE VISUAL SYSTEM FOR AT LEAST TWO REASONS: (i) to increase the robustness of dynamic visual measures during observer's motion; (ii) to provide a reference with respect to the environment ([Ballard and Brown, 1992]). The aim of this paper is to address the former issue by investigating the role of integration of visuo-inertial information in gaze stabilization. The rationale comes from observations of how the stabilization problem is solved in biological systems and experimental results based on an artificial visual system equipped with space-variant visual sensors and an inertial sensor are presented. In particular the following issues are discussed: (i) the relations between eye-head geometry, fixation distance and stabilization performance; (ii) the computational requirements of the visuo-inertial stabilization approach compared to a visual stabilization approach; (iii) the evaluation of performance of the visuo-inertial strategy in a real-time monocular stabilization task. Experiments are performed to quantitatively describe the performance of the system with respect to different choices of the principal parameters. The results show that the integrated approach is indeed valuable: it makes use of visual computational resources more efficiently, extends the range of motions or external disturbances the system can effectively deal with, and reduces system complexity.