The SII grant program strives to advance the Indianapolis sports community by creating unique interdisciplinary collaboration around industry specific challenges and opportunities. The grant program seeks proposals that further its mission in four key areas:

  1. Enhancement of the fan experience
  2. Grassroots sports advancement
  3. Sports product innovation
  4. Education/talent development

Proposals are sought from tenured, tenure-track, and non-tenure track faculty from the nine schools on the IUPUI campus that make up the Sports Innovation Institute.

See our previous stories below:

Exercise Bicycle Design Project

an exercise bike

Rafael Bahamonde, Ed Herger, Paul Yearling, Jordan Houchins, Christopher Chin, Abdulateef Abola, Nathan Magnuson, and Shaquitta Dent

Partnership: Department of Kinesiology and School of Engineering and Technology

The objective of the experiment was to improve a spin bike originally designed by Kelvin and Christian Bester. The bikes design was intended to exercise the quadriceps and hip flexors and encourage faster return stroke. Students from the School of Engineering and Technology and the Department of Kinesiology created a new design of this spin bike to help fix the impracticality and inefficiency of the original design. The students from the Department of Kinesiology used a Keiser M3i Indoor Cycle as the control bike when comparing the data received during the testing from the modified bike. Electromyography (EMG) data was collected throughout the experiment to help evaluate and record the activity from one single rider on both bikes with no resistance present and then with resistance present.

The results of the experiment showed that the objective set forth in the experiment was met. The test showed that the hip flexors (by the way the rectus femoris) are activated more with the modified bike during the upward phase of the pedaling cycle than the traditional stationary bike. Possible improvements to the new design could include a force test, assist mechanism, and a tensioner. The force test would be used to see if the amount of force needed to sustain a constant speed on the modified bike is like a normal spin bike. An assist mechanism would help the rider maintain a normal riding form. The tensioner added to the belt would allow the rider to put more force onto the pedal system. The experiment was deemed a success as the test results demonstrated greater muscle activity thus another experiment implementing the improvements is encouraged.

Event based Real-time Social Media Sentiment Analysis

New bike design. Right and left cranks independent with separate resistance.

Mathew Palakal, Salman Haider, Erica Shonkwiler, and David Pierce

Partnership: School of Informatics, School of Physical Education and Tourism Management

The purpose of this project is to outline the implementation of a real-time sentiment analyzer dashboard based on various events, trends or hashtags as described and expressed on social media. This project is classified into three broad sections, namely data engineering, analytics and visualization. The objective of this project was to have a fully automated system that allows any brand to measure its reputation by looking at their social media presence. It should not only provide customer sentiment, but also find customer reach, respond to customer posts or tweets, analyze competitors, assist in any potential crisis and capability to add context to share of voice. Machine Learning algorithms used in this project are Naive Bayes and SVM with above 80 percent accuracy.

The result of this project was an application created via Node.js that provided consumers with features such as real-time automated sentiment classification, collective visual representation of all social media activity, Geo-location tracking, customer engagement, dynamic charts for rapid business decision making, and after event reporting.

Future work of this study includes the idea that machine learning techniques perform well for classifying sentiment in tweets; however, the accuracy could be improved. Ideas that could help improve the accuracy including polarity of a tweet may depend on the perspective you are interpreting the tweet from.

Other ideas include semantics by using a semantic role labeler that may indicate which noun is mainly associated with the verb; the classification would take place accordingly. Fake posts, including more social media platforms like LinkedIN, Quora, Youtube etc., and commercial use of the system could be considered.

Design of organic-shaped sport helmets through bioinspired form-finding and optimization algorithms

4 pictures of 3D printed objects

Andres Tovar and Zebulun M. Wood

Partnership: School of Engineering and Technology and School of Informatics

The aim of this project was to address three critical aspects of helmet design. These critical elements consist of the need for high-fidelity computer simulation models, to develop methods and tools to use the computer simulations to generate organic-shaped architecture and innovative layouts, and to provide the ability to 3D print helmet prototypes to help identify flaws that can only be seen in a physical model. To achieve the objective of the project, the following tasks were completed: development of a virtual testing protocol for helmet designs, the creation of helmet designs that utilize free-form finding methods and topology optimization algorithms, and evaluation of the design using both virtual and physical, 3D-printed prototypes. 

The results of this project yielded a computational topology optimization tool to generate optimal lattice helmet liners, innovative bio-inspired helmet liner designs, a virtual testing protocol for evaluating helmet liner designs and cellular structures and full helmet designs, and a 3D printed helmet linear and helmet designs. 

In the future, this project has a potential technological, academic, and commercial impact because of the development of computer models that allow measurements not possible in physical tests.  Academic and research collaboration between the fields of Engineering and Technology, Informatics and Computing, Arts, Sports, and Medicine, and the development of conceptual helmet designs and computational design tools may be attractive to the market.