Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error.

Objectives: Dietary assessment methods not relying on self-report are needed. The Automatic Ingestion Monitor 2 (AIM-2) combines a wearable camera that captures food images with sensors that detect food intake. We compared energy intake (EI) estimates of meals derived from AIM-2 chewing sensor signals, AIM-2 images, and an internet-based diet diary, with researcher conducted weighed food records (WFR) as the gold standard.

"Automatic Ingestion Monitor Version 2" - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images.

Use of food image capture and/or wearable sensors for dietary assessment has grown in popularity. "Active" methods rely on the user to take an image of each eating episode. "Passive" methods use wearable cameras that continuously capture images.

Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment.

Video observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated.

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.

A Systematic Review of Technology-Driven Methodologies for Estimation of Energy Intake.

Accurate measurement of energy intake (EI) is important for estimation of energy balance, and, correspondingly, body weight dynamics. Traditional measurements of EI rely on self-report, which may be inaccurate and underestimate EI. The imperfections in traditional methodologies such as 24-hour dietary recall, dietary record, and food frequency questionnaire stipulate development of technology-driven methods that rely on wearable sensors and imaging devices to achieve an objective and accurate assessment of EI.

The importance of field experiments in testing of sensors for dietary assessment and eating behavior monitoring.

The field of sensor-based dietary assessment and behavioral monitoring is rapidly expanding. New devices and methods for detection for food intake and characterization of ingestive behavior, energy intake and nutrition have been introduced. Quite often the testing of new devices is limited to restricted meals in laboratory setting, which has the advantage of being controlled, but may not be representative of real life conditions.

Early Detection of the Initiation of Sit-to-Stand Posture Transitions Using Orthosis-Mounted Sensors.

Assistance during sit-to-stand (SiSt) transitions for frail elderly may be provided by powered orthotic devices. The control of the powered orthosis may be performed by the means of electromyography (EMG), which requires direct contact of measurement electrodes to the skin. The purpose of this study was to determine if a non-EMG-based method that uses inertial sensors placed at different positions on the orthosis, and a lightweight pattern recognition algorithm may accurately identify SiSt transitions without false positives.

Meal Microstructure Characterization from Sensor-Based Food Intake Detection.

To avoid the pitfalls of self-reported dietary intake, wearable sensors can be used. Many food ingestion sensors offer the ability to automatically detect food intake using time resolutions that range from 23 ms to 8 min. There is no defined standard time resolution to accurately measure ingestive behavior or a meal microstructure.

A method for early detection of the initiation of sit-to-stand posture transitions.

A powered lower extremity orthotic brace can potentially be used to assist frail elderly during daily activities. This paper presents a method for an early detection of the initiation of sit-to-stand (SiSt) posture transition that can be used in the control of the powered orthosis. Unlike the methods used in prosthetic devices that rely on surface electromyography (EMG), the proposed method uses only sensors embedded into the orthosis brace attached to the limb.

DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification.

A feature extraction scheme based on discrete cosine transform (DCT) of electromyography (EMG) signals is proposed for the classification of normal event and a neuromuscular disease, namely the amyotrophic lateral sclerosis. Instead of employing DCT directly on EMG data, it is employed on the motor unit action potentials (MUAPs) extracted from the EMG signal via a template matching-based decomposition technique. Unlike conventional MUAP-based methods, only one MUAP with maximum dynamic range is selected for DCT-based feature extraction.