Table 1.

Summary of core information of devices and technologies for measuring eating and drinking behaviors. The commercial availability of the example devices and algorithms is indicated “Y”(Yes) and “N”(No). Letters a and b following the reference numbers indicate systems that combine devices for several different measurement targets, and therefore appear in two table rows (i.e., a and b).

Objective	Measurement Target	Device	Representative Paper	Method	Off-the-Shelf Device	Ready-to-Use Algorithm	Similar Papers
Eating/drinking activity detection	eating/drinking motion	motion sensor	[16]	eating and drinking detection from smartwatch IMU signal	Y	N	[42] a, [43] a, [44,45,46], [47] a, [41] a, [21,48], [49] a, [22,50,51], [52] a, [38] a, [53] a, [17,18,54,55], [37] a, [56], [19] a, [57,58,59], [60] a, [61], [62] a, [63], [20] a, [64] a, [42,65,66,67,68]
			[23]	detecting eating and drinking gestures from FMCW radar signal	N	N
			[24] a	eating activities and amount consumed measured by pressure-sensitive tablecloth and tray	N	N
		microphone	[47] b	eating detection from fused inertial-acoustic sensing using smartwatch with embedded IMU and microphone	Y	N	[26] a, [69], [60] b
		RGB-D image	[25] a	eating action detected from smartphone RGB-D image as 3D overlap between mouth and food	Y	Y
		video	[70]	eating detection from cap-mounted video camera	Y	N	[55] a
	liquid level	liquid sensor	[71]	capacitive liquid level sensor	N	N	[72]
	impedance change in mouth	dielectric sensor	[27]	RF coupled tooth-mounted dielectric sensor measures impedance changes due to food in mouth	N	N
	in-body glucose level	glucose sensor	[26] b	glucose level measured by ear-worn sensor	N	N	[28] a
	in-body alcohol level	microneedle sensor	[28] b	alcohol level measured by microneedle sensor on the upper arm	N	N
	user identification	PPG (photoplethysmography) sensor	[53] b	sensors on water bottle to identify the user from heart rate	N	N
Bite/chewing/swallowing detection	bites (count)	motion sensor	[73]	a gyroscope mounted on a finger to detect motions of picking up food and delivering it to the mouth	Y	N	[74,75]
		video	[29]	bite count by video analysis using OpenPose pose estimation software	Y	Y
	bite weight	weight sensor	[30] a	plate-type base station with embedded weight sensors to measure amount and location of bites	N	N	[55] a
		acoustic sensor	[31]	commercial earbuds, estimation model based on nonaudio and audio features	Y	N
	chewing/swallowing	motion sensor	[32] a	chewing detection from gyroscope, swallowing detection from accelerometer, hand-to-mouth gestures from proximity sensor	Y	Y	[76,77,78,79], [49] b, [80,81], [82] a, [83], [84] a, [85,86,87], [62] b, [20] b
		microphone	[88]	wearable microphone with minicomputer to detect chewing/swallowing sounds	Y	N	[43] b, [89,90,91], [82] b, [19] b, [92], [84] b, [93]
		video	[94]	classification of facial action units related to chewing from video	Y	N	[55] b
		EGG	[95]	swallowing detected by larynx-mounted EGG device	Y	N
		EMG	[96]	eyeglasses equipped with EMG to monitor temporalis muscles’ activity	N	N	[43] c, [97]
Portion size estimation	portion size food	motion sensor	[34]	acceleration sensor of smartphone, measuring vibration intensity	Y	Y	[98] a
		weight sensor	[33]	wireless pocket-sized kitchen scale connected to app	Y	Y	[99,100,101,102,103], [55] b, [104], [30] b, [98] b, [105], [106] a, [107], [64] b, [24] b
		image	[108]	AI-based system to calculate food leftovers	Y	Y	[32] b, [35,109,110,111,112,113,114,115,116], [117] b, [106] b, [106,118,119,120,121,122,123]
			[35]	measuring the distance from the camera to the food using smartphone images combined with microphone data	Y	N	[124]
			[125]	RGB-D image and AI-based system to estimate consumed food volume using before- and after-meal images	Y	Y	[25] b, [126,127,128,129,130,131,132]
		laser	[36]	360-degree scanned video; the system design includes a volume estimation algorithm and a hardware add-on that consists of a laser module and a diffraction lens	N	N	[133]
		EMG	[37] b	weight of food consumed from EMG data	N	N
	portion size drink	motion sensor	[38] b	volume from sip duration from IMU in smartwatch	Y	N	[42] b, [52] b
		infrared (IR) sensor	[40]	thermal image by IR sensor embedded in smart fridge	N	N
		liquid sensor	[39]	capacitive sensor, conductivity sensor, flow sensor, pressure sensor, force sensors embedded in different mug prototypes	N	N
		image	[41] b	smartphone camera attached to mug	N	N	[134]