Directional Tuning Curves, Elementary Movement Detectors, and the Estimation of the Direction of Visual Movement

Both the insect brain and the vertebrate retina detect visual movement with neurons having broad, cosine-shaped directional tuning curves oriented in either of two perpendicular directions. This article shows that this arrangement can lead to isotropic estimates of the direction of movement: for any direction the estimate is unbiased (no systematic errors) and equally accurate (constant random errors). A simple and robust computational scheme is presented that accounts for the directional tuning curves as measured in movement sensitive neurons in the blowfly. The scheme includes movement detectors of various spans, and predicts several phenomena of movement perception in man.

Center of mass movement estimation using an ambulatory measurement sytem

Center of Mass (CoM) displacement, an important variable to characterize human walking, was estimated in this study using an ambulatory measurement system. The ambulatory system was compared to an optical reference system. Root-mean-square differences between the magnitudes of the CoM appeared to be comparable to those described in literature

Center of mass movement estimation using an ambulatory measurement system

Human body movement analysis is done in so-called 'gait-laboratories' where several gait variables are estimated by measurement systems such as optical position measurement systems, EMG or force plates. The accuracy of the ambulatory system is verified by comparing it to an optical reference system based on the semental kinematics method

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)

State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (\textit{Ursus maritimus}) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results