Wearable technology is a growing multi-billion dollar industry as the result of an exponential increase in the availability of cost-effective inertial measurement units (IMUs). However, while the output from current sensors provides helpful summaries of motion in general, they fall short of the detailed biomechanical analyses that have been shown to predict injury and overuse. Consequently, there are two gaps within the current sensor market: (1) these devices generate a profound amount of data that is largely ignored and (2) the information derived from sensor data is not placed within a contextual narrative. Thus, using 3-dimensional (3D) linear acceleration data from an IMU, we propose to develop a highly accurate algorithm by which “typical” and “atypical” gait biomechanical patterns can be measured and interpreted.
