Feature Selection Analysis of Chewing Activity Based on Contactless Food Intake Detection

This paper presents the feature selection methods for chewing activity detection. Chewing detection typically used for food intake monitoring applications. The work aims to analyze the effect of implementing optimum feature selection that can improve the accuracy of the chewing detection.  The raw chewing data is collected using a proximity sensor. Pre-process procedures are implemented on the data using normalization and bandpass filters. The searching of a suitable combination of bandpass filter parameters such as lower cut-off frequency (Fc1) and steepness targeted for best accuracy was also included. The Fc1 was 0,5Hz, 1.0Hz and 1.2H, while the steepness varied from 0.75 to 0.9 with an interval of 0.5. By using the bandpass filter with the value of [1Hz, 5Hz] with a steepness of 0.8, the system’s accuracy improves by 1.2% compared to the previous work, which uses [0.5Hz, 5Hz] with a steepness of 0.85. The accuracy of using all 40 extracted features is 98.5%. Two feature selection methods based on feature domain and feature ranking are analyzed. The features domain gives an accuracy of 95.8% using 10 features of the time domain, while the combination of time domain and frequency domain gives an accuracy of 98% with 13 features. Three feature ranking methods were used in this paper: minimum redundancy maximum relevance (MRMR), t-Test, and receiver operating characteristic (ROC). The analysis of the feature ranking method has the accuracy of 98.2%, 85.8%, and 98% for MRMR, t-Test, and ROC with 10 features, respectively. While the accuracy of using 20 features is 98.3%, 97.9%, and 98.3% for MRMR, t-Test, and ROC, respectively. It can be concluded that the feature selection method helps to reduce the number of features while giving a good accuracy

Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor

Accurate and objective assessment of energy intake remains an ongoing problem. We used features derived from annotated video observation and a chewing sensor to predict mass and energy intake during a meal without participant self-report. 30 participants each consumed 4 different meals in a laboratory setting and wore a chewing sensor while being videotaped. Subject-independent models were derived from bite, chew, and swallow features obtained from either video observation or information extracted from the chewing sensor. With multiple regression analysis, a forward selection procedure was used to choose the best model. The best estimates of meal mass and energy intake had (mean ± standard deviation) absolute percentage errors of 25.2% ± 18.9% and 30.1% ± 33.8%, respectively, and mean ± standard deviation estimation errors of −17.7 ± 226.9 g and −6.1 ± 273.8 kcal using features derived from both video observations and sensor data. Both video annotation and sensor-derived features may be utilized to objectively quantify energy intake.DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)Published versio

안경에서 기계적으로 증폭된 힘 측정을 통한 측두근 활동의 감지

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 이건우.Recently, the form of a pair of glasses is broadly utilized as a wearable device that provides the virtual and augmented reality in addition to its natural functionality as a visual aid. These approaches, however, have lacked the use of its inherent kinematic structure, which is composed of both the temple and the hinge. When we equip the glasses, the force is concentrated at the hinge, which connects the head piece and the temple, from the law of the lever. In addition, since the temple passes through a temporalis muscle, chewing and wink activity, anatomically activated by the contraction and relaxation of the temporalis muscle, can be detected from the mechanically amplified force measurement at the hinge. This study presents a new and effective method for automatic and objective measurement of the temporalis muscle activity through the natural-born lever mechanism of the glasses. From the implementation of the load cell-integrated wireless circuit module inserted into the both hinges of a 3D printed glasses frame, we developed the system that responds to the temporalis muscle activity persistently regardless of various form factor different from each person. This offers the potential to improve previous studies by avoiding the morphological, behavioral, and environmental constraints of using skin-attached, proximity, and sound sensors. In this study, we collected data featured as sedentary rest, chewing, walking, chewing while walking, talking and wink from 10-subject user study. The collected data were transferred to a series of 84-dimentional feature vectors, each of which was composed of the statistical features of both temporal and spectral domain. These feature vectors, then, were used to define a classifier model implemented by the support vector machine (SVM) algorithm. The model classified the featured activities (chewing, wink, and physical activity) as the average F1 score of 93.7%. This study provides a novel approach on the monitoring of ingestive behavior (MIB) in a non-intrusive and un-obtrusive manner. It supplies the possibility to apply the MIB into daily life by distinguishing the food intake from the other physical activities such as walking, talking, and wink with higher accuracy and wearability. Furthermore, through applying this approach to a sensor-integrated hair band, it can be potentially used for the medical monitoring of the sleep bruxism or temporomandibular dysfunction.Abstract Chapter 1. Introduction 1.1. Motivation 1.1.1. Law of the Lever 1.1.2. Lever Mechanism in Human Body 1.1.3. Mechanical Advantage in Auditory Ossicle 1.1.4. Mechanical Advantage in Glasses 1.2. Background 1.2.1. Biological Information from Temporalis Muscle 1.2.2. Detection of Temporalis Muscle Activity 1.2.3. Monitoring of Ingestive Behavior 1.3. Research Scope and Objectives Chapter 2. Proof-of-Concept Validation 2.1. Experimental Apparatus 2.2. Measurement Results 2.3. Discussion Chapter 3. Implementation of GlasSense 3.1. Hardware Prototyping 3.1.1. Preparation 3.1.2. Load Cell-Integrated Circuit Module 3.1.3. 3D Printed Frame of Glasses 3.1.4. Hardware Integration 3.2. Data Acquisition System 3.2.1. Wireless Data Transmission 3.2.2. Data Collecting Module Chapter 4. Data Collection through User Study 4.1. Preparation for Experiment 4.2. Activity Recording Chapter 5. Feature Extraction 5.1. Signal Preprocessing and Segmentation 5.1.1. Temporal Frame 5.1.2. Spectral Frame 5.2. Feature Extraction 5.2.1. Temporal Features 5.2.2. Spectral Features 5.2.3. Feature Vector Generation Chapter 6. Classification of Featured Activity 6.1. Support Vector Machine (SVM) 6.2. Design of Classifier Model 6.2.1. Grid-Search 6.2.2. Cross-Validation 6.3. Classification Result 6.4. Performance Improvement 6.5. Discussion Chapter 7. Conclusions Bibliography 초록Docto

Advancement in Dietary Assessment and Self-Monitoring Using Technology

Although methods to assess or self-monitor intake may be considered similar, the intended function of each is quite distinct. For the assessment of dietary intake, methods aim to measure food and nutrient intake and/or to derive dietary patterns for determining diet-disease relationships, population surveillance or the effectiveness of interventions. In comparison, dietary self-monitoring primarily aims to create awareness of and reinforce individual eating behaviours, in addition to tracking foods consumed. Advancements in the capabilities of technologies, such as smartphones and wearable devices, have enhanced the collection, analysis and interpretation of dietary intake data in both contexts. This Special Issue invites submissions on the use of novel technology-based approaches for the assessment of food and/or nutrient intake and for self-monitoring eating behaviours. Submissions may document any part of the development and evaluation of the technology-based approaches. Examples may include: web adaption of existing dietary assessment or self-monitoring tools (e.g., food frequency questionnaires, screeners) image-based or image-assisted methods mobile/smartphone applications for capturing intake for assessment or self-monitoring wearable cameras to record dietary intake or eating behaviours body sensors to measure eating behaviours and/or dietary intake use of technology-based methods to complement aspects of traditional dietary assessment or self-monitoring, such as portion size estimation