Front End Evaluation of 'Tester' Exhibition to be Developed into a Travelling Sports Exhibition

The purpose of this evaluation is to develop a framework that will help in planning and implementing the mobile sport exhibition, increase visitor satisfaction and aid At Bristol in building successful exhibits. The evaluation mainly focuses on visitor interaction with exhibits. It is believed that learning does occur in science centres and museums. The evaluation will therefore find out if learning occurs in the Sports exhibition and if so, the nature of the learning outcomes. The evaluation also discusses advantages and disadvantages of travelling exhibitions and identifies the characteristics of good exhibits that form the basis of the framework.From the results, an indication is that children make the larger proportion of visitors to Sportastic. Their age ranges, under 10 and 10 to 15 years constituted 21% and 30% respectively. The three most enjoyed exhibits are the Sprint Challenge (running), BATAK (test your reaction and Hot Shots (football). Visitors say these exhibits are enjoyed because they are fun, competitive, entertaining, interactive and hands-on. Skateboard Challenge and Skeleton Bob are among the exhibits least enjoyed since they are reported to be boring and uncomfortable to use. The learning outcomes from the exhibits are; increased knowledge about balancing, reaction, pulse and strength.

Motion cue analysis for parkinsonian gait recognition

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson's disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject's body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.