Developing computer vision technology to automate pitch analysis in baseball.

Lokator is a baseball training system designed to document pitch location while teaching pitch command, selection and sequencing. It is composed of a pitching target and a smartphone app. The target is divided into a set of zones to identify the pitch location. The main limitation of the current system is its reliance on the user\u27s feedback. After each throw, the pitcher or the coach needs to identify and report the target\u27s zone that was hit by the ball by just relying on the naked eye. The purpose of this thesis is to investigate the possibility of using computer vision technology to automate the pitch analysis in baseball and improve the usability and accuracy of the Lokator system. Towards this goal, we have developed, implemented and tested a computer vision-based software system that adds the following contributions to the Lokator system: Automated and accurate reading of the pitch location on the target. Automated and accurate estimation of the velocity of the ball. Provide contextual information about the pitch, such as vertical movement of the ball which can indicate late breaking. Replace the target by a catcher and estimate the pitch location using a virtual target. We have tested the software on a large set of recording. Those recordings are from indoor and outdoor environments with various illumination conditions and different backgrounds. The software was also tested on videos with softball pitches. To estimate the accuracy of the software, the sponsor gave us a set of 15 videos that include a total of 144 pitches along with the hit location of each pitch. Another set of 8 videos were provided to measure the accuracy of our software in terms of speed calculation

Smart Wearables for Tennis Game Performance Analysis

For monitoring the progress of athletes in various sports and disciplines, several different approaches are nowadays available. Recently, miniature wearables have gained popularity for this task due to being lightweight and typically cheaper than other approaches. They can be positioned on the athlete’s body, or in some cases, the devices are incorporated into sports requisites, like tennis racquet handles, balls, baseball bats, gloves, etc. Their purpose is to monitor the performance of an athlete by gathering essential information during match or training. In this chapter, the focus will be on the different possibilities of tennis game monitoring analysis. A miniature wearable device, which is worn on a player’s wrist during the activity, is going to be presented and described. The smart wearable device monitors athletes’ arm movements with sampling the output of the 6 DOF IMU. Parallel to that, it also gathers biometric information like pulse rate and skin temperature. All the collected information is stored locally on the device during the sports activity. Later, it can be downloaded to a PC and transferred to a cloud-based service, where visualization of the recorded data and more detailed game/training statistics can be performed

Computer Vision Solutions for Range of Motion Assessment

Joint range of motion (ROM) is an important indicator of physical functionality and musculoskeletal health. In sports, athletes require adequate levels of joint mobility to minimize the risk of injuries and maximize performance, while in rehabilitation, restoring joint ROM is essential for faster recovery and improved physical function. Traditional methods for measuring ROM include goniometry, inclinometry and visual estimation; all of which are limited in accuracy due to the subjective nature of the assessment. With the rapid development of technology, new systems based on computer vision are continuously introduced as a possible solution for more objective and accurate measurements of the range of motion. Therefore, this article aimed to evaluate novel computer vision-based systems based on their accuracy and practical applicability for a range of motion assessment. The review covers a variety of systems, including motion-capture systems (2D and 3D cameras), RGB-Depth cameras, commercial software systems and smartphone apps. Furthermore, this article also highlights the potential limitations of these systems and explores their potential future applications in sports and rehabilitation

The Development of a Novel Pitching Assessment Tool

Posture based ergonomic assessment tools are widely used to evaluate posture and injury risk for many workplace/occupational tasks. To date, there is no validated equivalent that can be used to assess the posture of a pitcher during baseball pitching. Therefore, the purpose of this study was to develop an inexpensive tool which can allow for the rapid assessment of a pitcher’s posture at lead foot strike, and establish the inter- and intra- rater reliability of the tool. For this study, 11 participants threw 30 pitches (15 fastballs, 15 curveballs) off an indoor pitching. Full body 3D kinematics were measured using reflective markers attached to anatomical landmarks and rigid bodies attached to body segments using a 10-camera Vicon Motion Capture system along with two high-speed video cameras (rear and side view) to record each pitch during the experimental trials. The kinematic data was analyzed, after which the highest velocity fastball of each of the 11 pitchers was selected for further analysis. A Pitching Mechanics Tool was designed to evaluate 16 different parameters at lead foot strike. Each of the 16 parameters had posture ranges or categories established based on scientific literature. Six evaluators with at least five years of experience working with adult pitchers completed the Pitching Mechanics Tool. Findings showed moderate to good levels of repeatability across multiple sessions as well as across multiple evaluators. Additionally, PMT results suggested that 2D qualitative analysis is a viable alternative to 3D motion capture

NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action

The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view Mo- Cap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motiondriven animation accessible to the general public. However, performance of existing HMR methods degrade when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action,and show that NeMo achieves better 3D reconstruction compared to various baselines

Internal Funding Newsletter, Academic Year 2020-2021

The University of Nebraska at Omaha is committed to fostering the academic and scholarly pursuits of faculty, staff, and students. While the 2020- 2021 Academic Year brought many changes and challenges, UNO has continued to invest in a multitude of funding programs to promote research and creative activity. This year’s programs provided over $530,000 for student and faculty projects that reflect the broad range of scholarly interests of the UNO community, including gender differences in remote meeting behaviors and networking, an inquiry into the scope of missing and murdered Indigenous women on the Pine Ridge Indian Reservation and its border towns, the enduring emotional and philosophical effects of the American civil war, and the importance of social Interactions in zoo-managed male African elephants

Columbia Chronicle (01/24/2011)

Student newspaper from January 24, 2011 entitled The Columbia Chronicle. This issue is 48 pages and is listed as Volume 46, Number 16. Cover story: Past, present bound together Editor-in-Chief: Spencer Roushhttps://digitalcommons.colum.edu/cadc_chronicle/1805/thumbnail.jp

2021 Program and Abstracts for the Celebration of Student Scholarship

Abstracts from the Celebration of Student Scholarship held in the Spring of 2021

UB Knightlines Spring 2012

The UB Knightlines newsletter for Spring 2012. This issue contains articles discussing innovations at UB, UB's agreement with Dr. Peter J. D'Adamo to launch the Center of Excellence in Generative Medicine, psychology majors learning about animal-assisted therapy, UB student musicians playing in downtown Bridgeport, the inspiration of UB student Lailali Almazaydeh on young women to prioritize education, new dining choices on campus, UB's involvement in Connecticut's NASA Space Day, SASD students winning the Architecture for Humanity ParkFEST Competition, UB Business students helping Sony Home Entertainment run a holiday marketing study on shopping apps, UB Gymnastics team winning the USAG Collegiate National Championships for a fourth year in a row, and other campus and sports news