A methodology of studying of ingestive behavior by non-invasive monitoring of swallowing (deglutition) and chewing (mastication) has been developed. The target application for the developed methodology is to study the behavioral patterns of food consumption and producing volumetric and weight estimates of energy intake. Monitoring is non-invasive based on detecting swallowing by a sound sensor located over laryngopharynx or by a bone conduction microphone and detecting chewing through a below-the-ear strain sensor. Proposed sensors may be implemented in a wearable monitoring device, thus enabling monitoring of ingestive behavior in free living individuals. In this paper, the goals in the development of this methodology are two-fold. First, a system comprised of sensors, related hardware and software for multimodal data capture is designed for data collection in a controlled environment. Second, a protocol is developed for manual scoring of chewing and swallowing for use as a gold standard. The multi-modal data capture was tested by measuring chewing and swallowing in twenty one volunteers during periods of food intake and quiet sitting (no food intake). Video footage and sensor signals were manually scored by trained raters. Inter-rater reliability study for three raters conducted on the sample set of 5 subjects resulted in high average intra-class correlation coefficients of 0.996 for bites, 0.988 for chews, and 0.98 for swallows. The collected sensor signals and the resulting manual scores will be used in future research as a gold standard for further assessment of sensor design, development of automatic pattern recognition routines, and study of the relationship between swallowing/chewing and ingestive behavior.

Our understanding of etiology of obesity and overweight is incomplete due to lack of objective and accurate methods for Monitoring of Ingestive Behavior (MIB) in the free living population. Our research has shown that frequency of swallowing may serve as a predictor for detecting food intake, differentiating liquids and solids, and estimating ingested mass. This paper proposes and compares two methods of acoustical swallowing detection from sounds contaminated by motion artifacts, speech and external noise. Methods based on mel-scale Fourier spectrum, wavelet packets, and support vector machines are studied considering the effects of epoch size, level of decomposition and lagging on classification accuracy. The methodology was tested on a large dataset (64.5 hours with a total of 9,966 swallows) collected from 20 human subjects with various degrees of adiposity. Average weighted epoch recognition accuracy for intra-visit individual models was 96.8% which resulted in 84.7% average weighted accuracy in detection of swallowing events. These results suggest high efficiency of the proposed methodology in separation of swallowing sounds from artifacts that originate from respiration, intrinsic speech, head movements, food ingestion, and ambient noise. The recognition accuracy was not related to body mass index, suggesting that the methodology is suitable for obese individuals. Index Terms NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript I IntroductionThe world is still losing in the battle with the obesity epidemic. According to WHO, in 2005 there were approximately 1.6 billion overweight and at least 400 million obese adults worldwide [1]. Current trends are unsettling: 2015 projections predict 2.3 billion overweight and 700 million obese adults worldwide. Obesity is one of the risk factors for development of chronic diseases and presents a serious health problem. A recent study [2] suggested that effects of obesity on global health may be comparable to those of cancer. Though the etiology of obesity is a topic of ongoing scientific debate, regulation of food intake may be an important factor for maintaining a healthy weight [3] in the environment that provides abundance of inexpensive, highly palatable and energy dense foods, while requiring only minimal levels of physical activity [4].While various methods have been developed for accurate and objective characterization of physical activity [5], at the present time, there is no accurate, inexpensive, non-intrusive way for objective Monitoring of Ingestive Behavior (MIB) in free living conditions. The most precise method of measuring energy intake is the Doubly-Labeled Water (DLW) technique which provides accurate estimates of caloric energy intake over long periods of time (10-14 days), if subjects remain weight stable. However, the DLW technique cannot identify daily intake patterns. Dietary self-report methods like food frequency questionnaires [6], selfreported diet diaries [7], and multimedia diaries [8] have been shown to be ...

Approximately one-third of people who recover from a stroke require some form of assistance to walk. Repetitive task-oriented rehabilitation interventions have been shown to improve motor control and function in people with stroke. Our long-term goal is to design and test an intensive task-oriented intervention that will utilize the two primary components of constrained-induced movement therapy: massed, task-oriented training and behavioral methods to increase use of the affected limb in the real world. The technological component of the intervention is based on a wearable footwear-based sensor system that monitors relative activity levels, functional utilization, and gait parameters of affected and unaffected lower extremities. The purpose of this study is to describe a methodology to automatically identify temporal gait parameters of poststroke individuals to be used in assessment of functional utilization of the affected lower extremity as a part of behavior enhancing feedback. An algorithm accounting for intersubject variability is capable of achieving estimation error in the range of 2.6–18.6% producing comparable results for healthy and poststroke subjects. The proposed methodology is based on inexpensive and user-friendly technology that will enable research and clinical applications for rehabilitation of people who have experienced a stroke.

IntroductIonRates of overweight and obesity are increasing globally. The World Health Organization estimated that there were ~1.6 billion overweight and at least 400 million obese adults worldwide in 2005 and that there will be 2.3 billion overweight and 700 million obese adults worldwide by 2015 (ref. 1). Overweight and obese individuals have an increased risk of developing chronic diseases such as type 2 diabetes, cardiovascular disease, and cancer (2-5).Overweight and obesity result from an imbalance between energy intake and energy expenditure, but the etiology of that imbalance and the underlying mechanisms are still incompletely understood. Our physiology, our behavior, and our environment must all be considered in understanding the etiology of obesity. There is a debate about the relative importance of genetic/physiological factors and environmental factors in the etiology of obesity. Clearly there are genetic/physiological contributions to obesity (6-9) but some weight gain can be attributed to an environment that provides an abundance of inexpensive, highly palatable, and energy dense foods, while requiring only minimal levels of physical activity (10-13). Part of our lack of understanding of the etiology of obesity is the fact that most weight gain likely occurs due to very small differences between energy intake and energy expenditure, necessitating very accurate measurements of energy intake and energy expenditure.A variety of methods are available for accurate and objective measurement of energy expenditure and its components, including doubly labeled water, indirect calorimetry, and accelerometry (14-16). These techniques can be used in the laboratory and in free-living populations. Energy and food intake can be accurately monitored in the laboratory by directly measuring consumed food. It is currently not possible to accurately monitor food intake in free-living populations. Several methods have been proposed to measure free-living food intake including observation, weighed food records, estimated records, diet history, food-frequency questionnaires, food recall methods, etc. (17). In a review of 43 studies comparing these methods to indirect measurement using doubly labeled water, the majority suffered from underestimation of energy intake on the order of 0.83 (ratio of intake estimate to energy expenditure) (18).There is an urgent need for innovative strategies for the accurate assessment of free-living energy intake and monitoring of

Background/Purpose Advances in sensory technologies provides a method to accurately assess activity levels of people with stroke in their community. This information could be used to determine the effectiveness of rehabilitation interventions as well as provide behavioral enhancing feedback. The purpose of this study was to assess the accuracy of a novel shoe-based sensor system (SmartShoe) to identify different functional postures and steps in people with stroke. The SmartShoe system consists of five force sensitive resistors built into a flexible insole and an accelerometer on the back of the shoe. Pressure and acceleration data are sent via Bluetooth to a smart phone. Methods Participants with stroke wore the SmartShoe while they performed activities of daily living (ADL) in sitting, standing and walking. Data from four participants were used to develop a multi-layer perceptron artificial neural network (ANN) to identify sitting, standing, and walking. A signal-processing algorithm used data from the pressure sensors to estimate number of steps taken while walking. The accuracy, precision and recall of the ANN for identifying the three functional postures were calculated using data from a different set of participants. Agreement between steps identified by SmartShoe and actual steps taken was analyzed using the Bland Altman method. Results The SmartShoe was able to accurately identify sitting, standing and walking. Accuracy, precision and recall were all greater than 95%. The mean difference between steps identified by SmartShoe and actual steps was less than 1 step. Discussion The SmartShoe was able to accurately identify different functional postures using a unique combination of pressure and acceleration data in people with stroke as they performed different ADLs. There was a strong level of agreement between actual steps taken and steps identified using the SmartShoe. Further study is needed to determine if the SmartShoe could be used to provide valid information on activity levels of people with stroke while they go about their daily lives in their home and community.