Food intake is the main source of energy and nutrients required to maintain life. The study of food intake patterns and ingestive behavior is critical to human health, as inadequate or excessive energy intake may result in medical conditions such as a decrease in weight or malnutrition, or increase in weight and obesity respectively. Monitoring of ingestive behavior is also important in understanding food intake patterns which contribute to the development of eating disorders such as anorexia nervosa, bulimia, and binge eating. Traditionally, ingestive behavior is assessed and monitored through self-reporting methods such as dietary records, 24hrs recall, and food frequency questionnaire, etc. However, these methods suffer severely from underreporting which may be as high as 50%. Thus, there is a need for the development of solutions for objective, accurate and automatic monitoring of the ingestive behavior of individuals, especially under free-living conditions. This work investigates the use of wearable sensor system for automatic detection, characterization and modification of the eating behavior of individuals with minimal or no conscious effort from the individuals. Automatic detection of food intake is proposed via monitoring of chewing and swallowing associated with food intake. Chewing monitoring is performed by using a piezoelectric strain sensor. A study was performed for food intake detection via chewing monitoring in free-living conditions for 24 hrs where chewing was captured with a piezoelectric strain sensor. Swallowing was monitored by using Electroglottography (EGG) measurement for monitoring of ingestive behavior during ad-libitum food intake in a controlled setting. This work also presents a new sensor system for which can accurately detect eating episodes in the presence of excessive ambulation. Research suggests that modifying the chewing behavior might be helpful in reducing the energy intake. This work further explores the potential use of the presented wearable sensor system to provide just-in-time feedback on the progression (based on total chew counts) of a meal and test its ability to reduce the total mass intake.