Standing balance is an important unbiased indicator of concussion severity. However, limited accessibility to high-end technology and unreliability of simple balance assessment tools make it difficult to assess standing balance accurately outside of research laboratory settings. The objective of this thesis was to develop and validate a simple objective balance assessment tool that can provide an accurate, reliable, and affordable alternative to the currently available sideline methods. In Experiment 1, thirty healthy subjects were filmed performing the Balance Error Scoring System (BESS) while wearing inertial measurement units (IMUs) that measured linear accelerations and angular velocities from seven landmarks:​ forehead, chest, waist, right & left wrist, right & left shin. Each video was scored by four experienced BESS raters. Mean experienced rater scores were used to develop an algorithm to compute objective BESS (oBESS) scores solely from IMU data. oBESS was able to accurately fit and predict mean experienced rater BESS scores using acceleration data from only one IMU located at the forehead. In Experiment 2, twenty healthy subjects wore the same network of IMUs and serially performed 12 BESS tests in a hypoxic altitude chamber, aimed at increasing the number of balance errors. Each video was scored by three experienced raters and two athletic trainers. Similarly to Experiment 1, experienced rater scores were used along with IMU data to develop the oBESS algorithm. However, because experienced raters displayed low inter-rater and intra-rater reliability, algorithm training and analyses were performed only using trials where the raters had marginal scoring differences. The oBESS was able to fit mean experienced rater scores with greater accuracy than the two athletic trainers, but not at a level commonly associated with high clinical reliability. In summary, this thesis shows that the oBESS can reliably predict total BESS scores in normal subjects, but only if trained using an accurate gold standard that allows the algorithm to overcome measurement error associated with the human-scored BESS. Pending further validation, the oBESS may represent a useful and valid tool to assess balance in athletes on the sideline by offering an objective alternative to the current scoring methods of the BESS.