Targeted isometric force impulses in patients with traumatic brain injury reveal delayed motor programming and change of strategy

The capability of quickly (as soon as possible) producing fast uncorrected and accurate isometric force impulses was examined to assess the motor efficiency of patients with moderate to severe traumatic brain injury (TBI) and good motor recovery at a clinical evaluation. Twenty male right-handed patients with moderate to severe TBI and 24 age-matched healthy male right-handed controls participated in the study. The experimental task required subjects to aim brief and uncorrected isometric force impulses to targets visually presented along with subjects’ force displays. Both TBI patients and controls were able to produce force impulses whose mean peak amplitudes varied proportionally to the target load with no detectable group difference. Patients with TBI, however, were slower than controls in initiating their responses (reaction times [RTs] were longer by 125 msec) and were also slower during the execution of their motor responses, reaching the peak forces requested 23 msec later than controls (time to peak force: 35% delay). Further, their mean dF/dt (35 kg/sec) was slower than that of controls (53 kg/sec), again indicating a 34% impairment with respect to controls. Overall, patients with TBI showed accurate but delayed and slower isometric force impulses. Thus, an evaluation taking into account also response time features is more effective in picking up motor impairments than the standard clinical scales focusing on accuracy of movement only

Gender Classification in Human Gait Using Support Vector Machine

Abstract. We describe an automated system that classifies gender by utilising a set of human gait data. The gender classification system consists of three stages: i) detection and extraction of the moving human body and its contour from im-age sequences; ii) extraction of human gait signature by the joint angles and body points; and iii) motion analysis and feature extraction for classifying gen-der in the gait patterns. A sequential set of 2D stick figures is used to represent the gait signature that is primitive data for the feature generation based on mo-tion parameters. Then, an SVM classifier is used to classify gender in the gait patterns. In experiments, higher gender classification performances, which are 96 % for 100 subjects, have been achieved on a considerably larger database.