CA : A Cancer Journal for Clinicians

https://science.sciencemag.org/content/sci/5/110/206.full.pdf

The american psychological association.

Men and Women of the Corporation

BACKGROUND\nObesity in childhood is associated with increased cardiovascular risk. It is uncertain whether this risk is attenuated in persons who are overweight or obese as children but not obese as adults.\n\n\nMETHODS\nWe analyzed data from four prospective cohort studies that measured childhood and adult body-mass index (BMI, the weight in kilograms divided by the square of the height in meters). The mean length of follow-up was 23 years. To define high adiposity status, international age-specific and sex-specific BMI cutoff points for overweight and obesity were used for children, and a BMI cutoff point of 30 was used for adults.\n\n\nRESULTS\nData were available for 6328 subjects. Subjects with consistently high adiposity status from childhood to adulthood, as compared with persons who had a normal BMI as children and were nonobese as adults, had an increased risk of type 2 diabetes (relative risk, 5.4; 95% confidence interval [CI], 3.4 to 8.5), hypertension (relative risk, 2.7; 95% CI, 2.2 to 3.3), elevated low-density lipoprotein cholesterol levels (relative risk, 1.8; 95% CI, 1.4 to 2.3), reduced high-density lipoprotein cholesterol levels (relative risk, 2.1; 95% CI, 1.8 to 2.5), elevated triglyceride levels (relative risk, 3.0; 95% CI, 2.4 to 3.8), and carotid-artery atherosclerosis (increased intima-media thickness of the carotid artery) (relative risk, 1.7; 95% CI, 1.4 to 2.2) (P ≤ 0.002 for all comparisons). Persons who were overweight or obese during childhood but were nonobese as adults had risks of the outcomes that were similar to those of persons who had a normal BMI consistently from childhood to adulthood (P>0.20 for all comparisons).\n\n\nCONCLUSIONS\nOverweight or obese children who were obese as adults had increased risks of type 2 diabetes, hypertension, dyslipidemia, and carotid-artery atherosclerosis. The risks of these outcomes among overweight or obese children who became nonobese by adulthood were similar to those among persons who were never obese. (Funded by the Academy of Finland and others.).

Childhood Adiposity, Adult Adiposity, and Cardiovascular Risk Factors EDITORIAL COMMENT

Referring as a Collaborative Process

In the context of dietary management, accurate monitoring of eating habits is receiving increased attention. Wearable sensors, combined with the connectivity and processing of modern smartphones, can be used to robustly extract objective and real-time measurements of human behavior. In particular, for the task of chewing detection, several approaches based on an in-ear microphone can be found in the literature, while other types of sensors have also been reported, such as strain sensors. In this paper, performed in the context of the SPLENDID project, we propose to combine an in-ear microphone with a photoplethysmography (PPG) sensor placed in the ear concha, in a new high accuracy and low sampling rate prototype chewing detection system. We propose a pipeline that initially processes each sensor signal separately, and then fuses both to perform the final detection. Features are extracted from each modality, and support vector machine (SVM) classifiers are used separately to perform snacking detection. Finally, we combine the SVM scores from both signals in a late-fusion scheme, which leads to increased eating detection accuracy. We evaluate the proposed eating monitoring system on a challenging, semifree living dataset of 14 subjects, which includes more than 60 h of audio and PPG signal recordings. Results show that fusing the audio and PPG signals significantly improves the effectiveness of eating event detection, achieving accuracy up to 0.938 and class-weighted accuracy up to 0.892.

A Novel Chewing Detection System Based on PPG, Audio, and Accelerometry

Overweight and obesity are both associated with in-meal eating parameters such as eating speed. Recently, the plethora of available wearable devices in the market ignited the interest of both the scientific community and the industry toward unobtrusive solutions for eating behavior monitoring. In this paper, we present an algorithm for automatically detecting the in-meal food intake cycles using the inertial signals (acceleration and orientation velocity) from an off-the-shelf smartwatch. We use five specific wrist micromovements to model the series of actions leading to and following an intake event (i.e., bite). Food intake detection is performed in two steps. In the first step, we process windows of raw sensor streams and estimate their micromovement probability distributions by means of a convolutional neural network. In the second step, we use a long short-term memory network to capture the temporal evolution and classify sequences of windows as food intake cycles. Evaluation is performed using a challenging dataset of 21 meals from 12 subjects. In our experiments, we compare the performance of our algorithm against three state-of-the-art approaches, where our approach achieves the highest F1 detection score (0.913 in the leave-one-subject-out experiment). The dataset used in the experiments is available at https://mug.ee.auth.gr/intake-cycle-detection/ .

Modeling Wrist Micromovements to Measure In-Meal Eating Behavior From Inertial Sensor Data

Automatic image annotation using supervised learning is performed by concept classifiers trained on labelled example images. This work proposes the use of clickthrough data collected from search logs as a source for the automatic generation of concept training data, thus avoiding the expensive manual annotation effort. We investigate and evaluate this approach using a collection of 97,628 photographic images. The results indicate that the contribution of search log based training data is positive; in particular, the combination of manual and automatically generated training data outperforms the use of manual data alone. It is therefore possible to use clickthrough data to perform large-scale image annotation with little manual annotation effort or, depending on performance, using only the automatically generated training data. The datasets used as well as an extensive presentation of the experimental results can be accessed at http://olympus.ee.auth.gr/~diou/civr2009/.

/pdf/image-annotation-using-clickthrough-data-42q5e9v27d.pdf

Image annotation using clickthrough data

Automatic objective monitoring of eating behavior using inertial sensors is a research problem that has received a lot of attention recently, mainly due to the mass availability of IMUs and the evidence on the importance of quantifying and monitoring eating patterns. In this paper we propose a method for detecting food intake cycles during the course of a meal using a commercially available wristband. We first model micro-movements that are part of the intake cycle and then use HMMs to model the sequences of micro-movements leading to mouthfuls. Evaluation is carried out on an annotated dataset of 8 subjects where the proposed method achieves 0:78 precision and 0:77 recall. The evaluation dataset is publicly available at http://mug.ee.auth.gr/intake-cycle-detection/.

Automated analysis of in meal eating behavior using a commercial wristband IMU sensor

Unobtrusive analysis of eating behavior based on Inertial Measurement Unit (IMU) sensors (e.g. accelerometer) is a topic that has attracted the interest of both the industry and the research community over the past years. This work presents a method for detecting food intake moments that occur during a meal session using the accelerometer and gyroscope signals of an off-the-shelf smartwatch. We propose a two step approach. First, we model the hand micro-movements that take place while eating using an array of binary Support Vector Machines (SVMs); then the detection of intake moments is achieved by processing the sequence of SVM score vectors by a Long Short Term Memory (LSTM) network. Evaluation is performed on a publicly available dataset with 10 subjects, where the proposed method outperforms similar approaches by achieving an F1 score of 0.892.

Christos Diou

Papers

A Novel Chewing Detection System Based on PPG, Audio, and Accelerometry

Modeling Wrist Micromovements to Measure In-Meal Eating Behavior From Inertial Sensor Data

Image annotation using clickthrough data

Automated analysis of in meal eating behavior using a commercial wristband IMU sensor

Food Intake Detection from Inertial Sensors Using LSTM Networks