Abstract: A system that uses information from multiple players involved in an impact to improve the measurement of impact g-force by collaborative analysis and hence improve the prediction of the risk of concussion for an athlete, which includes a wearable device with an impact sensor and an application processor, and a smartphone mobile application. The wearable device is mounted to the player's helmet or other locations where the impact force to the head is measured. The wearable device wirelessly communicates with a smartphone, where the measured g-force, including magnitude and direction, from the wearable device, is obtained and an iterative optimization algorithm is run to make the correction and/or optimization of the measurement based on the law of physics. The said iterative optimization algorithm takes into the consideration of the data from all players involved in the impact, and minimize the error based on the law of physics.

Abstract: A system to monitor hit impact force and to predict the likelihood of a concussion which includes a wearable device with an impact sensor and an application processor for prediction, a smartphone mobile application, a cloud-based data storage system, and an algorithm improvement system. The wearable device is mounted to the player's helmet or other locations where the impact force to the head is measured and a prediction algorithm is used to predict the potential risk of concussion. The wearable device wirelessly communicates with a smartphone, where data is viewed and associated information is managed. The impact force data and all related information are also automatically uploaded from the smartphone to a cloud-based database for storage. An algorithm improvement system will periodically analyze the data in order to build a better prediction model between impact force measured and the concussion diagnosed.