Area of research
Public Health, Environmental and Occupational Health · Biomedical Engineering
Research interest
Research interests include Nutritional Studies and Diet, Advanced Sensor and Energy Harvesting Materials, Obesity, Physical Activity, Diet, and Eating Disorders and Behaviors.
Detection and characterization of food intake by wearable sensors
Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment
Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor
Accelerometer-Based Detection of Food Intake in Free-Living Individuals
Meal Microstructure Characterization from Sensor-Based Food Intake Detection
Feature Extraction Using Deep Learning for Food Type Recognition
Reduction of energy intake using just‐in‐time feedback from a wearable sensor system
Real time monitoring and recognition of eating and physical activity with a wearable device connected to the eyeglass
A Novel Wearable Device for Food Intake and Physical Activity Recognition
Automatic Measurement of Chew Count and Chewing Rate during Food Intake
Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses With Piezoelectric Sensor
Detection of chewing from piezoelectric film sensor signals using ensemble classifiers
Linear regression models for chew count estimation from piezoelectric sensor signals
Comparative testing of piezoelectric and printed strain sensors in characterization of chewing
Monitoring of Infant Feeding Behavior Using a Jaw Motion Sensor
Strain Sensors in Wearable Devices
A wireless sensor system for quantification of infant feeding behavior
Automatic Ingestion Monitor: A Novel Wearable Device for Monitoring of Ingestive Behavior
A novel approach for food intake detection using electroglottography
Estimation of feature importance for food intake detection based on Random Forests classification
A Comparative Study of Food Intake Detection Using Artificial Neural Network and Support Vector Machine
Damage detection and identification in smart structures using SVM and ANN