Date of Award

December 2021

Document Type


Degree Name

Master of Science (MS)


Computer Engineering

Committee Member

Adam Hoover

Committee Member

Melissa Smith

Committee Member

Jon Calhoun


This thesis considers the problem of detecting periods of eating in free-living conditions by analyzing wrist motion data collected using sensors embedded within a typical smartwatch. Previous work by our research group included the collection of a dataset containing 354 days of recorded wrist motion data from 351 different people (approximately one day of data per person) [42]. A machine learning model was then trained to classify this wrist motion data as either eating or non-eating [40]. We refer to this model as the group model. Subsequent work in our research group collected approximately ten days of data each for eight new individuals and trained a model for each person solely using their own data [51]. We refer to these models as individual models. It was observed that, in most cases, the individual models outperformed the group model when evaluating the data of their corresponding individual, but at the cost of requiring each individual to collect two weeks of additional data. The novelty of this work is using transfer learning to leverage features learned within the group model and apply them to new individual models to further increase performance and possibly reduce the amount of individual data needed.

Two datasets were used in this work. The first was the Clemson All Day (CAD) dataset, which contains 354 days of recorded wrist motion data from 351 different participants (approximately one day of data per participant). The CAD dataset includes a total of 4,680 hours of data, including 1,063 meals. The second dataset used was the Multiday dataset, which is comprised of at least ten days of free-living wrist motion data each for eight individuals. Both datasets were pre-processed using smoothing and normalization techniques. Training samples were then generated using a sliding window approach with a window size of six minutes.

All group, individual, and transfer learning models evaluated in this work utilized an identical convolutional neural network (CNN) architecture. For a given window, the classifier generated a value that represented the probability of eating (P(E)) in the window. Entire days of wrist motion data were passed to the network to produce a continuous P(E) sequence for an entire day. This sequence was processed using a dual thresholding technique to locate predicted segments of eating within the recording.

In our results, the transfer learning model achieved an eating episode true positive rate (TPR) of 81% with a false positive per true positive ratio (FP/TP) of 1.40. Compared to the individual model, this was a 6% decrease in episode TPR but a 43% improvement in FP/TP. The transfer learning model showed a time weighted accuracy (AccW) of 80%, which was only a 1% decrease relative to the individual model. After removing an outlier from the Multiday dataset and rerunning our experiments, the transfer learning model showed an episode TPR of 86% with an FP/TP of 1.34. Compared to the individual model, this was only a 3% decrease in TPR and a 46% improvement in FP/TP. By excluding the outlier, the transfer learning model also showed an 83% AccW, which was a 1% increase relative to the individual model. Furthermore, the transfer learning model was able to reduce training times by 12% compared to the individual model. In conclusion, we were able to find evidence that transfer learning could be utilized in order to improve individualized eating detection models by increasing weighted accuracy and decreasing false detections.



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