Horizontal smooth pursuit eye movements were recorded in normal subjects in response to different patterns of target motion that was either periodic or not. Periodic patterns were triangular and sinusoidal waves. Non-periodic patterns were ramps with either constant or sinusoidally varying velocity. In both cases, several different amplitudes and peak velocities were considered. The experimental results indicate that (a) the performance of the smooth pursuit system depends on the spatio-temporal characteristics of target motion, (b) the relationship between smooth pursuit eye velocity and target velocity during the tracking of constant velocity ramps is strongly nonlinear with a saturation depending on the amplitude of target excursion, (c) in the remaining experimental conditions, there is a linear behaviour up to target velocities of 75 deg/s with a gain of about 0.9.
During eye tracking of a self-moved target, human subjects' performance differs from eye-alone tracking of an external target. Typical latency between target and eye motion onsets is shorter, ocular smooth pursuit (SP) saturation velocity increases and the maximum target motion frequency at which the SP system functions correctly is higher. Based on a previous qualitative model, a quantitative model of the coordination control between the arm motor system and the SP system is presented and evaluated here. The model structure maintains a high level of parallelism with the physiological system. It contains three main parts: the eye motor control (containing a SP branch and a saccadic branch), the arm motor control and the coordination control. The coordination control is achieved via an exchange of information between the arm and the eye sensorimotor systems, mediated by sensory signals (vision, proprioception) and motor command copy. This cross-talk results in improved SP system performance. The model has been computer simulated and the results have been compared with human subjects' behavior observed during previous experiments. The model performance is seen to quantitatively fit data on human subjects.
The aim of the present study was the validation of an instrument for evaluating balance, applied to the Tinetti test. Trunk inclination was measured by inclinometers during the Tinetti test in 163 healthy participants scoring 28/28 in the Tinetti scale (controls: 92 women, 71 men; age 19-85 years), and 111 residents in old people's homes, able to autonomously perform the test, but scoring less than 28/28 (test group: 78 women, 33 men; age 55-96 years). Trunk inclination was quantified by 20 parameters, whose standardized values were summed and provided an overall performance index (PTOT). PTOT reliability was evaluated by Cronbach's alpha, and its validity by item scale correlation, discriminant validity and concurrent validity. Influence of age and sex was assessed by a logistic regression model. Repeatable and consistent measurements were obtained (Cronbach's alpha=0.88). Parameter distribution was significantly different in controls and patients (P<0.001). Optimal PTOT threshold for discriminating between normal and abnormal performance (153.9/200) corresponded to sensitivity of 88.3%, specificity of 84.7% and area under the receiver operating characteristics curve of 0.93. PTOT correlated with the Tinetti scale score, its partial, balance-related score and Barthel's Index, but not with the Mini Mental State score. PTOT correlated with age and level of performance but not with sex; correlation with age did not prevent the possibility of discriminating between different levels of performance and between normal and abnormal performance. The instrument provided objective discrimination between different performance levels, in particular, between normal and altered performance.
The problem of a correct fall risk assessment is becoming more and more critical with the ageing of the population. In spite of the available approaches allowing a quantitative analysis of the human movement control system's performance, the clinical assessment and diagnostic approach to fall risk assessment still relies mostly on non-quantitative exams, such as clinical scales. This work documents our current effort to develop a novel method to assess balance control abilities through a system implementing an automatic evaluation of exercises drawn from balance assessment scales. Our aim is to overcome the classical limits characterizing these scales i.e. limited granularity and inter-/intra-examiner reliability, to obtain objective scores and more detailed information allowing to predict fall risk. We used Microsoft Kinect to record subjects' movements while performing challenging exercises drawn from clinical balance scales. We then computed a set of parameters quantifying the execution of the exercises and fed them to a supervised classifier to perform a classification based on the clinical score. We obtained a good accuracy (~82%) and especially a high sensitivity (~83%).
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