Head impact detection with sensor fusion and machine learning

Sammanfattning: Head injury is common in many different sports and elsewhere, and is often associated with differentdifficulties. One major problem is to identify and value the injury or the severity. Sometimes there is no sign of head injury, but a serious neck distortion has occurred, causing similar symptoms as head injuries e.g. concussion or mild TBI (traumatic brain injury). This study investigated whether direct and indirect measurements of head kinematics, combined with machine learning and 3D visualization can be used to identify head injury and value the injury. Injury statistics have found that many severe head injuries are caused by oblique impacts. An oblique impact will give rise to both linear and rotational kinematics. Since the human brain is very sensitive to rotational kinematics, many violent rotations of the head can results in large shear strains in the brain. This is when white matter and white matter connections are disrupted in the brain from acceleration and deceleration, or rotational acceleration kinematics which in turn will cause traumatic brain injuries as e.g. diffuse axonal injury (DAI). Lately there has been many studies in this field using different types of new technologies, but the most prevalent is the rise of wearable sensors that have become smaller, faster and more energy efficient where they have been integrated into mouthguards and inertial measurement units (IMUs) the size of a sim-card that measures and reports a body's specific force. It has been shown that a 6-axis IMU (3-axis rotational- and 3-axis acceleration measurements) may improve head injury prediction but more data is needed to confirm with existing head injury criterions and new criterions needs to be developed, that considers directional sensitivity. Today, IMUs are typically used in self-driving cars, aircrafts, spacecrafts, satellites etc. As of today, more and more studies have evaluated and utilized IMUs in new uncharted fields have shown promises, especially in sports, and in the neuroscience and medical field. This study proposed a method to 3D visualize head kinematics during the event of a possible head injury to indirectly identify and value the injury, by medical professionals, as well as, a direct method to identify and also value the severity of head injury with machine learning. An erroneous data collection process of reconstructed head impacts and non-head impacts have been recorded using an open-source 9-axis IMU sensor and a proprietary 6-axis IMU sensor. To value the head injury or the severity, existing head injury criterions as the Abbreviated Injury Scale (AIS), Head Injury Criterion (HIC), Head Impact Power (HIP), Severity Index (SI) and Generalized Acceleration Model for Brain Injury Threshold (GAMBIT) have been introduced. To detect head impact including the severity and non-head impact, a Random Forests (RF) classifier and Support Vector Machine (SVM) classifiers with linear- and radial basis function have been proposed, the prediction results have been promising.