Fig. 3.

Sensor placement and overview of the method of how the signals acquired are analyzed. (a) Four contact microphones are placed on the medial and lateral sides of the patella and superficially to the medial and lateral meniscus (b) The signal analysis workflow for knee joint sounds. The signals from the dominant knee of the subjects are filtered and standardized (to zero mean and unity variance) and windowed (frame length of 200 ms with 90% overlap). The features are extracted for all four mics and vertically concatenated where columns represent the features and rows represent all the windowed segments. The rows represent all the windows in microphone 1 to microphone 4 and the columns represent the 64 features. A k-Nearest Neighbor graph (kNN graph) is constructed from the matrix formed using data from the dominant knee and calculates the graph community factor (GCF) using the graph community detection algorithm.