A Novel Wearable Device for Food Intake and Physical Activity Recognition

Abstract

Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure.

Wearable Food Intake Monitoring Technologies: A Comprehensive Review

“…This study proves a comparatively similar effectiveness in chewing detection between piezoelectric and printed strain sensors (error rates of 8.09% and 8.29%, respectively). Their latter work on this piezoelectric approach specifies the jaw movements on the temporalis muscle as the source of the input signal to the piezoelectric film sensor [128]. Additional works on the piezoelectric method merge it with other approaches for expanding the detected aspects besides chewing and swallowing.…”

A Mobile-Based Diet Monitoring System for Obesity Management

“…Additionally, we believe the increased application of wearable sensor devices, especially those can be integrated into smartphone, will revolutionize this line of research and as a whole the food monitoring system will be useful for effective health promotion and disease prevention. For example, eating episodes detected by several wearable devices, such as glasses with load cells [46], glasses connected to sensors on temporalis muscle and accelerometer [47], and wrist motion track [48], can provide more food intake information in addition to the image-based strategy. We believe that such information collected by multi-monitoring technologies [49], pertinent to users’ diet habit pattern, can serve as starting point for more precise food consumption analysis and diet interventions.…”

Mobile Ecological Momentary Diet Assessment Methods for Behavioral Research: Systematic Review (Preprint)

“…Because of the known limitation of existing dietary assessment methods, the research community is motivated to develop new solutions aimed at (semi-)automating the assessment of dietary intake. While the automated methods of real-time image-based detection [14][15][16][17][18][19][20][21][22][23][24] and real-time detection of food intake by biomechanical sensors or hand-held devices [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] have seen significant progress [15,24] in terms of identifying foods and estimating portion sizes [14][15][16][17][18][19][20][21][22][23][24] detecting wrist or hand motion [25][26][27][28]…”

“…image-assisted and image-based assessment [14][15][16][17][18][19][20][21][22][23][24] and the detection of food intake by biomechanical sensors or hand-held devices [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. Significant progress has been made in image-assisted and image-based food recording that has resulted in the improved accuracy of dietary self-report [15,24].…”

“…However, design and proof-of-concept data suggest the automation of dietary assessment remains out of reach for the time being. For instance, the mean detection accuracy of image detection and wearable devices have been acceptable in controlled settings (range = 73-99%) [18,26,30,31,34,35,[38][39][40][41][42][43][44][45][46][47][48] but limited testing has been done in natural settings [27].…”