Detection and classification of intra boby acoustic wave signals for wearable applications

Wearable Devices (WD) are systems designed to do a specific task, these system are embedded in daily life personal objects. Usual transducers in wearable devices involve accelerometers gyroscopes, cameras etc. In WD is required to design efficiently in terms of power. Therefore, a new trend incorporates acoustic transducer that does not need a power supply to sense. They are very cheap and easy to replace. In WD, these transducers detect acoustic wave signals traveling in human tissue and bones. The processing of these kind of signals is related with many areas such as medical, gesture recognition, haptics etc. The proposed system uses a set of three sensors to capture Intra Body Acoustic Wave Signals (IBAWS) from the wrist of the right hand. A signal database is constructed using 18 users repeating 30 times each one of five gestures proposed. The patterns are composed by six features per sensor, including Spectral Flux, Spectral Centroid and Short Time Energy. Complete proposed patterns contains 18 features. Classifiers results shown 75.37% of accuracy using Bayesian classifier with Gaussian Kernel, 80% of accuracy using Knn classifier, and 85.56% of accuracy with Artificial Neural Networks. All this for a set of 18 users, supporting the hypothesis that classify IBAWS is independent from the user and could be generalized to use them in WD.