. Author manuscript; available in PMC: 2020 Jun 2.

Published in final edited form as: IEEE Access. 2019 Apr 11;7:49653–49668. doi: 10.1109/access.2019.2910308

TABLE 5.

Systematic review of wearable sensor based method towards EI estimation.

Study	Sensor used/sensor location	Food types/No. of foods included in study	Regression method used for analysis	Estimated equation	Group model (Gr) / Individual model (Ind)	Significant variables	Mass estimation	Energy density estimation	Compared against^*	Accuracy/ performance
Sazonov (2009) [61]	Miniature microphone, Piezoelectric strain sensor	Meal, solid and liquids /NA	Linear	$M_{S} = 0.5 ({\bar{M}}_{S W}^{S} \times N_{S W} + {\bar{M}}_{C H E W} \times N_{C H E W})$ $M_{L} = {\bar{M}}_{S W}^{L} \times N_{S W}$ M_S : Predicted mass of solid food (g) ${\bar{M}}_{S W}^{S}$ : Subject’s average mass per swallow of solid food (g) N_SW : Total number of swallow for solid or liquid food intake ${\bar{M}}_{C H E W}$ : Average mass per chew (g) N_CHEW : Total number of chews M_L : Predicted mass of liquid food (g) ${\bar{M}}_{S W}^{L}$ : Subject’s average mass per swallow of liquid food (g)	Gr	Average mass per swallow, Average mass per chew, Number of swallows, Number of chews	Y	N	WFR	Average accuracy of mass (g) model for solid food intake : 91.79% Average accuracy of mass (g) model for liquid food intake: 83.76%
Amft (2009) [62]	Ear-pad chewing sound sensor	Solid/ 3	Multiple linear	$W_{i} = a_{0} + \sum_{k = 1}^{N_{V}} a_{k} v_{i k}$ W_i : Bite weight prediction (g) a : Food-specific coefficients found by least-square fit v : microstructure variables N_V : Total number of variables	Ind	Number of chewing event, Chewing duration	N	N	WFR	Food classification accuracy : 94% Lowest mean weight (g) prediction: 19.4% Largest mean weight (g) prediction: 31%
Liu (2012) [63]	Miniature camera and microphone	Meal	NA	NA	NA	Sound features: Energy entropy, Short time energy, Spectral roll-off, spectral centroid, spectral flux, spectral average of sub-bands, Zero crossing rate, Peak gaps between energy peaks.	N	N	NA	Not specified
Fontana (2015) [34]	Throat microphone, Piezoelectric strain sensor	Meal, solid and liquids /45	Linear	Total mass ingested M_T = M_S + M_L; $M_{S} = w_{s} \times M P S w_{S} \times N_{s w}^{s} + w_{c}$ × (MPChew × c_f) × N_chew; $M_{L} = M P S w_{L} . N_{s w}^{L}$ ; $E I = \sum_{i}^{N} m_{T_{i}} . C D_{i}$ M_T : Total mass ingested (g) M_S : Mass of solid food ingested (g) M_L : Mass of liquid food ingested (g) w_s : weight parameter for mass prediction using number of swallows w_c : weight parameter for mass prediction using number of chews MPSw_S : subject's average mass per swallow of solid food (g) MPChew : subject's average mass per chew (g) $N_{s w}^{s}$ : total number of swallows for solid food intake N_chew : total number of chews c_f : correction factor MPSw_L : subject's average mass per swallow of liquid (g) $N_{s w}^{L}$ : total number of swallows for liquid intake m_{T_i} : consumed mass for the distinct food type i (g) CD_i : caloric density associated to the same food type i (kcals/g) N : total number of distinct foods types consumed in the meal	Ind	Counts of chews and swallows	Y		WFR	Best accuracy: El (kcal) model based on chews counts Reporting error (%): 30.42 ± 23.08
Alshurafa (2015) [64]	piezoelectric sensor	Liquid, solid, hot and cold drinks, hard and soft foods.	NA	NA	NA	NA	N	N	NA	Food type classification F-measure 90%
Salley (2016) [35]	Hand gesture sensor	Meal Solid, mixed and non-mixed /1844	Linear	Estimated kilocalories per bite = −0.128 × age + 6.167 × sex(females = 0) + 0.034 × height + 0.035 × weight −12.012 × WHR + 22.294 age : Age in years height : Height in inches weight : Weight in lb. WHR : Waist-to-hip ratio	Gr	Age, Sex, Height, Weight, Waist to hip ratio	N	N	WFR	Mean estimation error: −71.21±562.14 kcal
Mirtchouk (2016) [65]	Motion (head, both wrists) and acoustic sensors (customized earbud)	Meal Solid, mixed and non-mixed / 1489 food and 285 drink Intakes	Random forest 40 trees	NA	Gr	NA	Y	N	WFR	Food classes classification accuracy: 82.7% Amount consumed (g) estimation error: 35.4%
Hezarjaribi (2017) [66]	Audio recording in mobile app	Not specified	NA	NA	NA	Frequency domain features (energy, fundamental frequency	N	N	DB	EI (kcal) accuracy: 92.2%
Thong (2017) [67]	near-infrared (NIR) scanner	Liquid	Support vector And Partial least square	E[Y∣X] = f(X, β) E : Energy content (KJ) X : an n × m matrix; n number of scans; m number of absorption; Y : observed values of energy (KJ) , carbohydrates (g) in food samples; f : Linear function β : least square errors;	NA	NA	N	N	DB	For energy, the prediction error is less 2 KJ. For carbohydrate, the prediction error is around 0.12 g.

Notes: WFR: weighed food record; DB: Nutrition database/ Nutritional Fact labels