Head injuries are a concern for athletes and motorcyclists. A blow to the head resulting from a fall or collision with another object can result in a fatal or permanently disabling injury. Even relatively low-energy impacts can cumulatively have several impairing effects. Helmets seek to prevent these effects and protect users from injury, generally by lessening accelerations experienced by the brain. A new type of helmet has been created which differs significantly from conventional designs, and hopes to address their limitations. The helmet consists of an array of elastomeric rubber dampers in addition to the traditional crushable foam cushion. In this thesis, the design of the Omni Directional Suspension (ODS) helmet and its potential to mitigate brain injury are discussed. Due to the nature of the ODS helmet design, it is expected that the technology will perform comparatively better than conventional designs in low-energy impacts, in which concussion or Mild Traumatic Brain Injury (MTBI) is the most common injury. A series of tests simulating realistic crash conditions were conducted in a controlled laboratory environment for this purpose. A total of eight high velocity tests (roughly six meters per second) and six low velocity tests (roughly three meters per second) were conducted using ODS technology prototypes. Control tests were conducted using commercially available helmets, fourteen at high and fourteen at low velocities. Instrumentation involved a "Hybrid 3" headform inside the test helmets, using a nine-accelerometer array sampling at 10kHz. Data were filtered and processed, then analyzed in Microsoft Excel and Minitab software. The tests were analyzed according to commonly used injury prediction models. These included peak linear and angular accelerations, the Head Injury Criterion (HIC), Head Impact Power (HIP), and others. The effects of the ODS technology was in this way compared to traditional helmets, and the benefits of the technology directly assessed. The results of this research generally show that the ODS technology performs better than the control helmets in multiple injury prediction models.