LAUSR

Reliable, user-friendly and convenient sensing is highly desirable when the continuous monitoring of food intake is necessary. In this paper, food intake monitoring was during the processes of chewing and swallowing. Acoustic Doppler sonar (ADS) detected chewing and swallowing events that were non-contact and free from acoustic interference. When a 40 kHz ultrasonic beam was focused on the lower jaw and neck, movements of the chin and neck cause Doppler frequency shifts and an amplitude envelope modulation of ultrasonic signals. Hence, it was possible to detect chewing and swallowing events using Doppler frequency shifts in the received ultrasound signals. To prevent suspicious chew events caused by talking from being recognized as food intake events, the log-filter bank energy of the voice band was also taken into consideration. Automatic detection of chewing and swallowing events was achieved via an artificial neural network. The experimental results showed that the proposed ADS-based food intake detection method yielded promising results with maximum recognition rates of 91.4% and 78.4% for chewing and swallowing, respectively. As a result, it was confirmed that the proposed food intake detection method using ultrasonic Doppler yielded high rates of recognition without discomfort to the user from continuous skin contact.