Monitoring of food intake and eating habits is important for managing and understanding obesity, diabetes, and eating disorders. It can be cumbersome and tedious for individuals to self-report their eating habits, making wearable devices that automatically monitor and record dietary habits an attractive alternative. The challenge is that these devices store or transmit raw data for offline processing. This is a power-consumptive approach that requires a bulky battery or frequent charging, both of which intrude on the user’s normal daily activities and thus make the devices prone to poor user adherence and acceptance.
In this paper, we present a novel analog integrated circuit long short-term memory (LSTM) neural network for embedded eating event detection that eliminates the need for a power-consumptive analog-to-digital converter (ADC) in devices. Unlike previous analog LSTM implementations, our solution contains no internal DACs, ADCs, pampas or Hadamard multiplications. Our novel approach is based on a current-mode adaptive filter, and it eliminates over 90% of the power requirements of a more conventional solution. This opens up the possibility of unobtrusive, battery-less wearable devices that can be used for long-term monitoring of dietary habits.
You can find this paper along with other publications from the Auracle group on Zotero.
Odame, Kofi, Maria Nyamukuru, Mohsen Shahghasemi, Shengjie Bi, and David Kotz. “Analog Gated Recurrent Neural Network for Detecting Chewing Events.” IEEE Transactions on Biomedical Circuits and Systems 16, no. 6 (December 2022): 1106–15. https://doi.org/10.1109/TBCAS.2022.3218889.
In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9% and F1 score 78.7% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or mobile platforms, in terms of computation, memory, and power. We believe this paper is the first work to experiment with video-based (rather than image-based) eating detection in free-living scenarios.
To see more from the Auracle research group, check out our publications on Zotero.
We are proud to announce another Auracle team member’s successful dissertation defense, and to share his doctoral thesis. Dr. Byron Lowens’ dissertation focuses on understanding how to develop privacy control mechanisms that provide adopters (and potential adopters) of wearables with integrated, in-the-moment control over personal information collected by wearables. Lowens describes the four different studies he conducted, on individual preferences on data sharing, the impact of the location of privacy control and decision timing, device-independent interactions to control data privacy, and on noticeability of identified interactions. His findings offer privacy researchers and designers of wearable technologies insight into the future development of wearables.
To learn more, check out Lowens’ dissertation below.
The Auracle device previously enabled us to automatically and unobtrusively recognize eating behavior in adults. The Auracle team recognized the need for adapting such technology to measure children’s eating behavior and to bolster research efforts focusing on adolescents’ eating behaviors.
We identified and addressed several challenges pertaining to monitoring eating behavior in children, paying particular attention to device fit and comfort. We also improved the accuracy and robustness of the eating-activity detection algorithms.
Check out the 4-minute video below to see graduate student Shengjie Bi’s presentation of our research at IEEE’s International Conference on Healthcare Informatics (ICHI). To read the paper, check out the link at the bottom of this post.