Reproducibility of Dietary Intake Measurement From Diet Diaries, Photographic Food Records, and a Novel Sensor Method.

No data currently exist on the reproducibility of photographic food records compared to diet diaries, two commonly used methods to measure dietary intake. Our aim was to examine the reproducibility of diet diaries, photographic food records, and a novel electronic sensor, consisting of counts of chews and swallows using wearable sensors and video analysis, for estimating energy intake. This was a retrospective analysis of data from a previous study, in which 30 participants (15 female), aged 29 ± 12 y and having a BMI of 27.

Energy intake estimation from counts of chews and swallows.

Current, validated methods for dietary assessment rely on self-report, which tends to be inaccurate, time-consuming, and burdensome. The objective of this work was to demonstrate the suitability of estimating energy intake using individually-calibrated models based on Counts of Chews and Swallows (CCS models). In a laboratory setting, subjects consumed three identical meals (training meals) and a fourth meal with different content (validation meal).

Analysis of electrode shift effects on wavelet features embedded in a myoelectric pattern recognition system.

Myoelectric pattern recognition systems can translate muscle contractions into prosthesis commands; however, the lack of long-term robustness of such systems has resulted in low acceptability. Specifically, socket misalignment may cause disturbances related to electrodes shifting from their original recording location, which affects the myoelectric signals (MES) and produce degradation of the classification performance. In this work, the impact of such disturbances on wavelet features extracted from MES was evaluated in terms of classification accuracy.

Automatic ingestion monitor: a novel wearable device for monitoring of ingestive behavior.

Objective monitoring of food intake and ingestive behavior in a free-living environment remains an open problem that has significant implications in study and treatment of obesity and eating disorders. In this paper, a novel wearable sensor system (automatic ingestion monitor, AIM) is presented for objective monitoring of ingestive behavior in free living. The proposed device integrates three sensor modalities that wirelessly interface to a smartphone: a jaw motion sensor, a hand gesture sensor, and an accelerometer.

A novel approach for food intake detection using electroglottography.

Many methods for monitoring diet and food intake rely on subjects self-reporting their daily intake. These methods are subjective, potentially inaccurate and need to be replaced by more accurate and objective methods. This paper presents a novel approach that uses an electroglottograph (EGG) device for an objective and automatic detection of food intake.

Estimation of feature importance for food intake detection based on Random Forests classification.

Selection of the most representative features is important for any pattern recognition system. This paper investigates the importance of time domain (TD) and frequency domain (FD) features used for automatic food intake detection in a wearable sensor system by using Random Forests classification. Features were extracted from signals collected using 3 different sensor modalities integrated into the Automatic Ingestion Monitor (AIM): a jaw motion sensor, a hand gesture sensor and an accelerometer.

Evaluation of Chewing and Swallowing Sensors for Monitoring Ingestive Behavior.

Monitoring Ingestive Behavior (MIB) of individuals is of special importance to identify and treat eating patterns associated with obesity and eating disorders. Current methods for MIB require subjects reporting every meal consumed, which is burdensome and tend to increase the reporting bias over time. This study presents an evaluation of the burden imposed by two wearable sensors for MIB during unrestricted food intake: a strain sensor to detect chewing events and a throat microphone to detect swallowing sounds.

A robust classification scheme for detection of food intake through non-invasive monitoring of chewing.

Automatic methods for food intake detection are needed to objectively monitor ingestive behavior of individuals in a free living environment. In this study, a pattern recognition system was developed for detection of food intake through the classification of jaw motion. A total of 7 subjects participated in laboratory experiments that involved several activities of daily living: talking, walking, reading, resting and food intake while being instrumented with a wearable jaw motion sensor.

Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing.

The number of distinct foods consumed in a meal is of significant clinical concern in the study of obesity and other eating disorders. This paper proposes the use of information contained in chewing and swallowing sequences for meal segmentation by food types. Data collected from experiments of 17 volunteers were analyzed using two different clustering techniques.

A Sensor System for Automatic Detection of Food Intake Through Non-Invasive Monitoring of Chewing.

Objective and automatic sensor systems to monitor ingestive behavior of individuals arise as a potential solution to replace inaccurate method of self-report. This paper presents a simple sensor system and related signal processing and pattern recognition methodologies to detect periods of food intake based on non-invasive monitoring of chewing. A piezoelectric strain gauge sensor was used to capture movement of the lower jaw from 20 volunteers during periods of quiet sitting, talking and food consumption.

Swallowing detection by sonic and subsonic frequencies: a comparison.

The detection of swallowing events by acoustic means represents an important tool to assess and diagnose swallowing disorders as well as to objectively monitor ingestive behavior of individuals. Acoustic sensors used to register swallowing sounds may also capture sound artifacts arising from intrinsic speech and external noise affecting the detection. In this paper we tested if subsonic frequencies are less prone to artifacts from speech, chewing and other intrinsic sounds than sonic frequencies.

Design of a instrumentation module for monitoring ingestive behavior in laboratory studies.

The development of accurate and objective tools for monitoring of ingestive behavior (MIB) is one of the most important needs facing studies of obesity and eating disorders. This paper presents the design of an instrumentation module for non-invasive monitoring of food ingestion in laboratory studies. The system can capture signals from a variety of sensors that characterize ingestion process (such as acoustical and other swallowing sensors, strain sensor for chewing detection and self-report buttons).