The vibration and acoustic emissions produced within facet joints of the lumbar spine, known as crepitus, can be a potential biomarker to identify decreased joint functioning and the site of low back pain. Using piezoelectric accelerometers and a silicone "phantom" mechanical model we sought to identify the site of crepitus. Past analyses of these data with human observers have been too time consuming for eventual practical clinical application, and a more expedient algorithmic method of analysis is preferable. In this study the signal filtering and processing functions of MATLAB were harnessed to filter aberrant noise as well as determine the location (level and left or right side) from which crepitus originated during induced crepitus events in the phantom model (n=30). Development of this automated method refined the definition of facet joint crepitus. The automated method was found to be as reliable and valid as assessment by human observers and took significantly less time (p=0.009). Future studies will assess the reliability of the automated method to detect this phenomenon in humans.

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