Detection of chewing and swallowing to estimate eating patterns and energy intake

描述（由申请人提供）： 发达国家的肥胖症流行率正在以惊人的速度增加。肥胖导致心脏病、高血压、糖尿病和某些癌症的风险增加，现在被认为是心血管疾病的危险因素。本研究的目的是研究新型非侵入性设备和模式识别方法的应用，以进行人类食物摄入行为的研究，并对体积和热量的食物摄入量进行客观估计，这将有助于确定治疗或预防肥胖等疾病的有效措施。这样的装置和方法可以扩展我们对肥胖原因的理解，并且本研究中创建的监测装置可以用于监测肥胖患者，并且作为治疗的一部分，潜在地改善生活质量并降低与肥胖相关的发病率和死亡率。本研究的目的是设计并进行一项初步调查，该调查将提供初步数据，即通过可穿戴非侵入性监测设备对咀嚼（咀嚼）和吞咽（吞咽）进行客观观察，可以通过提供对咀嚼频率、咀嚼持续时间的客观测量，并以足够的灵敏度和特异性识别食物摄入时间，提供统计学上可靠的饮食习惯估计。 拟议的研究的目的包括可穿戴传感器和相关的信号处理方法的设计;模式识别方法的发展，将自动检测delution和咀嚼的情况下，从传感器记录;模式识别方法的发展，自动识别期间的食物摄入量的基础上检测咀嚼和吞咽;并验证这些设备和方法对一组人类受试者。现代计算智能方法，如人工神经网络和模糊逻辑将与统计方法沿着使用，以达到最高的模式识别精度。

DESCRIPTION (provided by applicant): The prevalence of obesity in developed countries is increasing at an alarming rate. Obesity contributes to an increased risk of heart disease, hypertension, diabetes, and some cancers and is now considered a risk factor for cardiovascular disease. The objective of this research is to investigate application of novel noninvasive devices and pattern recognition methods to perform studies of human food intake behavior and produce objective estimates of volumetric and caloric food intake that will be relevant for identifying effective measures to treat or prevent diseases like obesity. Such devices and methods could extend our understanding of causes of obesity, and the monitoring devices created in this study could be used for monitoring of obese patients and as a part of a therapy potentially improve quality of life and decrease the morbidity and mortality associated with obesity. The goal of this study is to design and perform a pilot investigation which will provide preliminary data that objective observations of mastication (chewing) and deglutition (swallowing) by a wearable, non-intrusive monitoring device can provide statistically reliable estimates of eating habits by providing objective measures of delution frequency, duration of mastication and identifying periods of food intake with sufficient sensitivity and specificity. The aims of the proposed research include design of the wearable sensors and associated signal processing methods; development of pattern recognition methodologies that will automatically detect instances of delution and mastication from sensor recordings; development of pattern recognition methods to automatically identify periods of food intake based on detected chewing and swallowing; and to validate these device and methodologies on a group of human subjects. Modern methods of computational intelligence such as artificial neural networks and fuzzy logic will be used along with statistical methods to achieve the highest 'accuracy of pattern recognition.