Developing a Method to Quantify Cyclic Motion Patterns Using Smart Phone Technology
Cyclic motion patterns are those that repeat in a periodic sequence. Researchers have traditionally quantified cyclic patterns using high-quality optical or video motion capture systems that are often expensive and cumbersome. However, the modern emergence of accelerometers and gyroscopes embedded within common smart phones has inspired new research efforts to characterize motion patterns from these less expensive and more broadly-available tools. While many recent studies have focused on acceleration data, the present study seeks to derive the positional translation and orientation patterns from the smart phone data. A primary challenge with deriving positional data from accelerometer sensors is that the data must be integrated twice with respect to time, and data noise accumulates into substantial drift. For this study, the motion pattern of a mechanical horse was simultaneously recorded with a high-quality video motion capture system and with iPhone sensors. Positional data was derived from the iPhone data using an algorithm that capitalized on the known fact that the motion pattern was cyclic. Comparison of the motion-capture and iPhone-derived data sets revealed that the algorithm was very successful at reproducing the patterns of angular orientation, but not successful at completely eliminating drift from the positional translation pattern.