Retrieval and Timing Performance of ChewingBased Eating Event Detection in Wearable Sensors We present an eating detection algorithm for wearable sensors based on first detecting chewing cycles and subsequently estimating eating phases. We term the corresponding algorithm class as a bottomup approach. We evaluated the algorithm using electromyographic (EMG) recordings from dietmonitoring eyeglasses in freeliving and compared the bottomup approach against two topdown algorithms. We show that the F1 score was no longer the primary relevant evaluation metric when retrieval rates exceeded approx. 90%. Instead, detection timing errors provided more important insight into detection performance. In 122 hours of freeliving EMG data from 10 participants, a total of 44 eating occasions were detected, with a maximum F1 score of 99.2%. Average detection timing errors of the bottomup algorithm were 2.4 xc2xb1 0.4 s and 4.3 xc2xb1 0.4 s for the start and end of eating occasions, respectively. Our bottomup algorithm has the potential to work with different wearable sensors that provide chewing cycle data. We suggest that the research community report timing errors (e.g., using the metrics described in this work).

A Study on Linear Acceleration of the Wrist During FreeLiving Accelerometers have gained popularity in biomedical and mhealth applications such as actigraphy or automated dietary monitoring due to their ease of use and their ability to characterize motion. These sensors report raw acceleration from which gravity and linear acceleration must be separated, with commercial packages reporting raw acceleration, linear acceleration or both. New researchers to the field may often be confused when to use raw acceleration or linear acceleration, especially given the susceptibility of linear acceleration to noise, and the lack of published distributions of these signals. This paper provides a short tutorial on obtaining linear acceleration estimates. Using these methods we analyze a large dataset containing 4,680 hours of wrist tracking data, the largest such dataset known to us. We learn the range of wrist motion accelerations, and quantify the expected noise in the linear acceleration signal. We explain the sources of this noise, and a filtering technique to mitigate it. For the first time, we report the range of wrist acceleration values observed during freeliving, and quantify the expected range of noise in this wrist acceleration. We show that while previous work has reported average accelerations at the feet and body ranging from 0xe2x80x9315g during spotslike activities like walking, running or jumping, wrist acceleration in freeliving subjects during daily activities is often much lower, and ranges from 0xe2x80x930.2g. We show that noise in linear acceleration can range from 0xe2x80x930.06g, an overlap of 70%. This suggests that in applications where the wrist acceleration is in this range of noise, linear acceleration may not provide useful features, and researchers should only rely on raw acceleration instead.