Table 1.

Supervised Human Activity Recognition (HAR) works that recognize the eating activity among other Activities of Daily Living (ADL).

Work	Summary of Proceedings
[15]	Recognized activities	Walking, walking carrying items, sitting & relaxing, working on computer, standing still, eating or drinking, watching TV, reading, running, bicycling, stretching, strength-training, scrubbing, vacuuming, folding laundry, lying down & relaxing, brushing teeth, climbing stairs, riding elevator and riding escalator
	Sensors	Accelerometers
	Features	Mean, energy, frequency-domain entropy, and correlation
	Classifiers	Decision tree C4.5
	Eating recognition metrics	Accuracy: 89%
[16]	Recognized activities	Sitting, sit-to-stand, stand-to-sit, standing, walking, typing on keyboard, using the mouse, flipping a page, cooking, eating
	Sensors	Accelerometers and location tracker
	Features	Mean and variance of the 3D acceleration
	Classifiers	Dynamic Bayesian Network
	Eating recognition metrics	Accuracy: 80%
[17]	Recognized activities	Brushing teeth, dressing/undressing, eating, sweeping, sleeping, ironing, walking, washing dishes, watching TV
	Sensors	Accelerometer, thermometer and altimeter
	Features	Mean, minimum, maximum, standard deviation, variance, range, root-mean-square, correlation, difference, main axis, spectral energy, spectral entropy, key coefficient
	Classifiers	Support Vector Machines
	Eating recognition metrics	Accuracy: 93%
[12]	Recognized activities	Standing, jogging, sitting, biking, writing, walking, walking upstairs, walking downstairs, drinking coffee, talking, smoking, eating
	Sensors	Accelerometer, gyroscope and linear acceleration sensor
	Features	Mean, standard deviation, minimum, maximum, semi-quantile, median, sum of the first ten FFT coefficients
	Classifiers	Naive Bayes, k-Nearest Neighbors, Decision Tree
	Eating recognition metrics	F1-score: up to 87%