Human action recognition using wireless wearable in-ear microphone

To realize the ubiquitous eating habits monitoring, we proposed the use of sounds sensed by an in-ear placed wireless wearable microphone. A prototype of wireless wearable in-ear microphone was developed by utilizing a common Bluetooth headset. We proposed a robust chewing action recognition algorithm which consists of two recognition stages: "chew-like" signal detection and chewing sound verification stages. We also provide empirical results on other action recognition using in-ear sound including swallowing, cough, belch, and etc. The average chewing number counting error rate of 1.93% is achieved. Lastly, chewing sound mapping is proposed as a new prototypical approach to provide an additional intuitive feedback on food groups to be able to infer the eating habits in their daily life context.