Impact of shoe design on basketball performance and the application of soft sensors to improve dynamic fit.
Luczak, Anthony Lee
AdvisorBurch, Reuben F.
Carruth, Daniel W.
Embargo TypeComplete embargo for 1 year
Embargo Lift Date2021-05-15
This dissertation is composed of four different studies focused on using Human Factors Engineering (HFE) assessment tools traditionally used in industrial settings to evaluate personal protective equipment (PPE) footwear of basketball athletes and assessment of compressible soft robotic sensors to evaluate pressures. The first study developed a Basketball Shoe Taxonomy (BST) designed to categorize shoes using a combination of design factors and effects on performance. The second study investigated the influence of basketball shoe design on jumping performance. Using four jumping patterns, six male and ten female basketball National Collegiate Athletic Association (NCAA) Division I student-athletes completed 16 trials wearing two different Adidas basketball shoe designs. There was no significant difference in effect of shoe type on jumping performance (p > 0.05). The third study examined each athlete’s perception of comfort and quality of fit of the shoes used in the second study using a visual analog scale (VAS) and Likert scale survey. One student-athlete out of 16 reported that one of the shoes tested was their favorite and the most comfortable basketball shoe they had ever worn. Results indicated an average overall comfort rating below 60% for both shoes and there was not a significant difference in perception of comfort or quality of fit between the shoes (p > 0.05). The final study was designed to validate the use of compressible Stretchsense™ sensors (CSSs) to ground reaction pressures. Participants performed three repetitions of squatting, shifting center of pressure between the right foot and left foot, and shifting center of pressure forward and back between the toes and heels. Performance was evaluated using CSSs, BodiTrak Vector Plater™ (BVP), and Kistler Force Plates™ (KFPs). The results indicate that CSSs are an acceptable replacement to ground reaction pressure mats. In addition, the use of an Autoregressive Integrated Moving Average (ARIMA) model resulted in average R2 values greater than 90%. High R2 values in the ARIMA modeling indicates that the software accurately models the human 3D foot-shoe interaction pressures used in the development of the ground reaction pressure socks (GRPS) for sport applications and for fall detection in elderly and balance impaired individuals.