Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation

Over the last years, inertial sensing has proven to be a suitable ambulatory alternative to traditional human motion tracking based on optical position measurement systems, which are generally restricted to a laboratory environment. Besides many advantages, a major drawback is the inherent drift caused by integration of acceleration and angular velocity to obtain position and orientation. In addition, inertial sensing cannot be used to estimate relative positions and orientations of sensors with respect to each other. In order to overcome these drawbacks, this study presents an Extended Kalman Filter for fusion of inertial and magnetic sensing that is used to estimate relative positions and orientations. In between magnetic updates, change of position and orientation are estimated using inertial sensors. The system decides to perform a magnetic update only if the estimated uncertainty associated with the relative position and orientation exceeds a predefined threshold. The filter is able to provide a stable and accurate estimation of relative position and orientation for several types of movements, as indicated by the average rms error being 0.033 m for the position and 3.6 degrees for the orientation

The Fusion of Smartphone Sensors for Indoor 3D Position and Orientation Estimation

The improvement in smartphone technology has encouraged the exploration in field of user experience. The internal inertial navigation system sensors of a smartphone enables it to infer the its three dimensional indoor orientation and position when it is being pointed at certain objects by hand. However, the sensors’ flawed measurements complicate estimation of position and orientation precisely. Previous studies shows that sensor fusion of both internal and external measurements can enhanced the performance. However, those estimations didn’t cover the pointer-like usage. To achieve the possibility of smartphone as pointer, the estimation using sensor fusion has been performed. Unfortunately, these experiments resulted in bad position estimation for small precision, while the orientation estimation was passabl

Gait analysis using ultrasound and inertial sensors

Introduction and past research:\ud Inertial sensors are great for orientation estimation, but they cannot measure relative positions of human body segments directly. In previous work we used ultrasound to estimate distances between body segments [1]. In [2] we presented an easy to use system for gait analysis in clinical practice but also in-home situations. Ultrasound range estimates were fused with data from foot-mounted inertial sensors, using an extended Kalman filter, for 3D (relative) position and orientation estimation of the feet.\ud \ud Validation:\ud From estimated 3D positions we calculated step lengths and stride widths and compared this to an optical reference system for validation. Mean (±standard deviation) of absolute differences was 1.7 cm (±1.8 cm) for step lengths and 1.2 cm (±1.2 cm) for stride widths when comparing 54 walking trials of three healthy subjects.\ud \ud Clinical application:\ud Next, the system presented in [2] was used in the INTERACTION project, for measuring eight stroke subjects during a 10 m walk test [3]. Step lengths, stride widths and stance and swing times were compared with the Berg balance scale score. The first results showed a correlation between step lengths and Berg balance scale scores. To draw real conclusions, more patients and also different activities will be investigated next.\ud \ud Future work:\ud In future work we will extend the system with inertial sensors on the upperand lower legs and the pelvis, to be able to calculate a closed loop and improve the estimation of joint angles compared with systems containing only inertial sensors

Ambulatory estimation of foot movement during gait using inertial sensors

Human body movement analysis is commonly done in so-called 'gait laboratories’. In these laboratories, body movement is masured using optically based systems like Vicon, Optrotrak. The major drawback of these systems is the restriction to a laboratory environment. Therefore research is required to find ways for performing these measurements outside the gait laboratory. The estimation of foot movement is important, since balance is controlled by foot placement during gait. This study investigates whether it is possible to estimate foot movement, specifically foot placement, during gait under ambulatory conditions. The measurement system consisted of an orthopaedic sandal with two six degrees-of-freedom force/moment sensors beneath the heel and the forefoot. It should be noted that the force sensors were merely used for gait phase detection. The position and orientation of heel and forefoot were estimated using the accelerometers and gyroscopes of two miniature inertial sensors, rigidly attached to the force sensors [1,3]. In addition, errors in the walking direction were compensated for by using knowledge about the average walking direction. The proposed ambulatory measurement system was similar to the one used in a previous study [3]. In that study the position and orientation determination was restarted each step, while this study allows estimation of position and orientation during several steps including a change of direction. However, the accuracy should be investigated in more detail by an evaluation study. Moreover, the measurement system can be simplified by using a different gait phase detection system, for example by a gyroscope based detection system [2]. The financial support from the Dutch Ministry of Economic Affairs for the FreeMotion project is gratefully acknowledged. REFERENCES [1] H.J. Luinge and P.H. Veltink, “Measuring orientation of human body segments using miniature gyroscopes and accelerometers”, Med. Bio. Eng. Comp., Vol. 43, pp. 273-282, (2005). [2] I.P.I. Pappas, M.R. Popovic, M.R. Keller, V. Dietz and M. Morari, “A reliable gait phase detection system”, IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 9, pp. 113-125, (2001). [3] H.M. Schepers, P.H. Veltink and H.F.J.M. Koopman, “Ambulatory assessment of ankle and foot dynamics”, IEEE Trans. Biomed. Eng., Submitted, (2006)

3D Orientation Estimation Using Inertial Sensors

Recently, inertial sensors have been widely used in the measurement of 3D orientations because of their small size and relative low cost. One of the useful applications in the area of Neurorehabilitation is to assess the upper limb motion for patients who are under neurorehabilitation. In this paper, the computation of the 3D orientation is discussed utilising the outputs from accelerometers, gyroscopes and magnetometers. Different 3D orientation representations are discussed to give recommendations for different use scenarios. Based on the results form the 3D orientation, 2D and 3D position tracking techniques are also calculated by considering the joint links and kinematics constraints from the upper limb segments. The results showed that the performance using complementary filter can make good estimation of the orientation.

Parametric and State Estimation of Stationary MEMS-IMUs: A Tutorial

Inertial navigation systems (INS) are widely used in almost any operational environment, including aviation, marine, and land vehicles. Inertial measurements from accelerometers and gyroscopes allow the INS to estimate position, velocity, and orientation of its host vehicle. However, as inherent sensor measurement errors propagate into the state estimates, accuracy degrades over time. To mitigate the resulting drift in state estimates, different approaches of parametric and state estimation are proposed to compensate for undesirable errors, using frequency-domain filtering or external information fusion. Another approach uses multiple inertial sensors, a field with rapid growth potential and applications. The increased sampling of the observed phenomenon results in the improvement of several key factors such as signal accuracy, frequency resolution, noise rejection, and higher redundancy. This study offers an analysis tutorial of basic multiple inertial operation, with a new perspective on the error relationship to time, and number of sensors. To that end, a stationary and levelled sensors array is taken, and its robustness against the instrumental errors is analyzed. Subsequently, the hypothesized analytical model is compared with the experimental results, and the level of agreement between them is thoroughly discussed. Ultimately, our results showcase the vast potential of employing multiple sensors, as we observe improvements spanning from the signal level to the navigation states. This tutorial is suitable for both newcomers and people experienced with multiple inertial sensors

Nonlinear Observer for Visual-Inertial Navigation Using Intermittent Landmark Measurements

The development of reliable orientation, position and linear velocity estimation algorithms for the 3D visual-inertial navigation system (VINS) is instrumental in many applications, such as autonomous underwater vehicles (AUVs), and unmanned aerial vehicles (UAVs). It is extremely important when the global position system (GPS) is not available in GPS-denied environments. Recently, observers design for VINS using landmark position measurements from Kinect sensors or stereo cameras has been increasingly investigated in the literature. The aim of this work is to design a nonlinear observer for VINS under the assumption that landmark position measurements are intermittent. In practice, the landmark measurements are not continuous due to computation cost from image processing, which is different from most of the existing results relying on continuous landmark measurements. The proposed nonlinear observer, motivated from the classical linear Kalman filter, has two parts: continuous prediction using inertial measurement unit and previous landmark measurements, and instantaneous state updating upon the arrival of new landmark measurements. Almost global asymptotic stability (AGAS) has been achieved by applying the framework of the hybrid dynamical system, which means that the estimated state will asymptotically converge to the real state of the visual-inertial navigation system for almost all initial conditions. We strongly believe that our developed estimation tool will not only benefit the area of aerial vehicles engineering but also the robotics and biomedical engineering community

Ambulatory Assessment of Ankle and Foot Dynamics

Ground reaction force (GRF) measurement is important in the analysis of human body movements. The main drawback of the existing measurement systems is the restriction to a laboratory environment. This paper proposes an ambulatory system for assessing the dynamics of ankle and foot, which integrates the measurement of the GRF with the measurement of human body movement. The GRF and the center of pressure (CoP) are measured using two six-degrees-of-freedom force sensors mounted beneath the shoe. The movement of foot and lower leg is measured using three miniature inertial sensors, two rigidly attached to the shoe and one on the lower leg. The proposed system is validated using a force plate and an optical position measurement system as a reference. The results show good correspondence between both measurement systems, except for the ankle power estimation. The root mean square (RMS) difference of the magnitude of the GRF over 10 evaluated trials was (0.012 plusmn 0.001) N/N (mean plusmn standard deviation), being (1.1 plusmn 0.1)% of the maximal GRF magnitude. It should be noted that the forces, moments, and powers are normalized with respect to body weight. The CoP estimation using both methods shows good correspondence, as indicated by the RMS difference of (5.1 plusmn 0.7) mm, corresponding to (1.7 plusmn 0.3)% of the length of the shoe. The RMS difference between the magnitudes of the heel position estimates was calculated as (18 plusmn 6) mm, being (1.4 plusmn 0.5)% of the maximal magnitude. The ankle moment RMS difference was (0.004 plusmn 0.001) Nm/N, being (2.3 plusmn 0.5)% of the maximal magnitude. Finally, the RMS difference of the estimated power at the ankle was (0.02 plusmn 0.005) W/N, being (14 plusmn 5)% of the maximal power. This power difference is caused by an inaccurate estimation of the angular velocities using the optical reference measurement system, which is due to considering the foot as a single segment. The ambulatory system considers separat- - e heel and forefoot segments, thus allowing an additional foot moment and power to be estimated. Based on the results of this research, it is concluded that the combination of the instrumented shoe and inertial sensing is a promising tool for the assessment of the dynamics of foot and ankle in an ambulatory setting