Multi-distance motion vector clustering algorithm for video-based sleep analysis

Overall health and daily functioning deteriorate with poor sleep. To improve one's sleep, sleep monitoring can help identifying causes of sleep problems. As an advantage over traditional wrist actigraphy used in home sleep monitoring solutions, video contains more comprehensive movement information. Particularly, different body movements can be distinguished which is beneficial for a more detailed sleep analysis. We developed an efficient K-Means clustering method with a multi-distance seeding technique to find the dominant cluster candidates. An integrated multi-distance dissimilarity measure was used for the subsequent clustering. We present an automatic content-dependent weight tuning method for the dissimilarity measure to balance between different distance descriptors. This discriminative algorithm partitions similar body movements in the same cluster. We were able to produce several dissimilarity measures producing clusters that agreed 67% with manual clustering of motion vectors by one expert. Similar clustering characteristics were preferred by both the five expert annotators and the suggested clustering algorithm. This gives us confidence that the proposed optimization method can be used in the future.