Nadezhda Sazonova

Non-invasive monitoring of chewing and swallowing for objective quantification of ingestive behavior

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 comprising sensors, related hardware and software for multi-modal 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 21 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 five 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.

A comparison of energy expenditure estimation of several physical activity monitors.

INTRODUCTION Accurately and precisely estimating free-living energy expenditure (EE) is important for monitoring energy balance and quantifying physical activity. Recently, single and multisensor devices have been developed that can classify physical activities, potentially resulting in improved estimates of EE. PURPOSE This study aimed to determine the validity of EE estimation of a footwear-based physical activity monitor and to compare this validity against a variety of research and consumer physical activity monitors. METHODS Nineteen healthy young adults (10 men, 9 women) completed a 4-h stay in a room calorimeter. Participants wore a footwear-based physical activity monitor as well as Actical, ActiGraph, IDEEA, DirectLife, and Fitbit devices. Each individual performed a series of postures/activities. We developed models to estimate EE from the footwear-based device, and we used the manufacturers software to estimate EE for all other devices. RESULTS Estimated EE using the shoe-based device was not significantly different than measured EE (mean ± SE; 476 ± 20 vs 478 ± 18 kcal, respectively) and had a root-mean-square error of 29.6 kcal (6.2%). The IDEEA and the DirectLlife estimates of EE were not significantly different than the measured EE, but the ActiGraph and the Fitbit devices significantly underestimated EE. Root-mean-square errors were 93.5 (19%), 62.1 kcal (14%), 88.2 kcal (18%), 136.6 kcal (27%), 130.1 kcal (26%), and 143.2 kcal (28%) for Actical, DirectLife, IDEEA, ActiGraph, and Fitbit, respectively. CONCLUSIONS The shoe-based physical activity monitor provides a valid estimate of EE, whereas the other physical activity monitors tested have a wide range of validity when estimating EE. Our results also demonstrate that estimating EE based on classification of physical activities can be more accurate and precise than estimating EE based on total physical activity.

Activity-based sleep–wake identification in infants

Actigraphy offers one of the best-known alternatives to polysomnography for sleep-wake identification. The advantages of actigraphy include high accuracy, simplicity of use and low intrusiveness. These features allow the use of actigraphy for determining sleep-wake states in such highly sensitive groups as infants. This study utilizes a motion sensor (accelerometer) for a dual purpose: to determine an infants position in the crib and to identify sleep-wake states. The accelerometer was positioned over the sacral region on the infants diaper, unlike commonly used attachment to an ankle. Opposed to broadly used discriminant analysis, this study utilized logistic regression and neural networks as predictors. The accuracy of predicted sleep-wake states was established in comparison to the sleep-wake states recorded by technicians in a polysomnograph study. Both statistical and neural predictors of this study provide an accuracy of approximately 77-92% which is comparable to similar studies achieving prediction rates of 85-95%, thus validating the suggested methodology. The results support the use of body motion as a simple and reliable method for determining sleep-wake states in infants. Nonlinear mapping capabilities of the neural network benefit the accuracy of sleep-wake state identification. Utilization of the accelerometer for the dual purpose allows us to minimize intrusiveness of home infant monitors.

Accurate prediction of energy expenditure using a shoe-based activity monitor.

PURPOSE The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. METHODS We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. RESULTS Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. CONCLUSIONS These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

Automatic Recognition of postures and activities in stroke patients

Stroke is the leading cause of disability in the United States. It is estimated that 700,000 people in the United States will experience a stroke each year and that there are over 5 million Americans living with a stroke. In this paper we describe a novel methodology for automatic recognition of postures and activities in patients with stroke that may be used to provide behavioral enhancing feedback to patients with stroke as part of a rehabilitation program and potentially enhance rehabilitation outcomes. The recognition methodology is based on Support Vector classification of the sensor data provided by a wearable shoe-based device. The proposed methodology was validated in a case study involving an individual with a chronic stroke with impaired motor function of the affected lower extremity and impaired walking ability. The results suggest that recognition of postures and activities may be performed with very high accuracy.

Prediction of Bodyweight and Energy Expenditure Using Point Pressure and Foot Acceleration Measurements

Bodyweight (BW) is an essential outcome measure for weight management and is also a major predictor in the estimation of daily energy expenditure (EE). Many individuals, particularly those who are overweight, tend to underreport their BW, posing a challenge for monitors that track physical activity and estimate EE. The ability to automatically estimate BW can potentially increase the practicality and accuracy of these monitoring systems. This paper investigates the feasibility of automatically estimating BW and using this BW to estimate energy expenditure with a footwear-based, multisensor activity monitor. The SmartShoe device uses small pressure sensors embedded in key weight support locations of the insole and a heel-mounted 3D accelerometer. Bodyweight estimates for 9 subjects are computed from pressure sensor measurements when an automatic classification algorithm recognizes a standing posture. We compared the accuracy of EE prediction using estimated BW compared to that of using the measured BW. The results show that point pressure measurement is capable of providing rough estimates of body weight (root-mean squared error of 10.52 kg) which in turn provide a sufficient replacement of manually-entered bodyweight for the purpose of EE prediction (root-mean squared error of 0.7456 METs vs. 0.6972 METs). Advances in the pressure sensor technology should enable better accuracy of body weight estimation and further improvement in accuracy of EE prediction using automatic BW estimates.

Quality in face and iris research ensemble (Q-FIRE)

Identification of individuals using biometric information has found great success in many security and law enforcement applications. Up until the present time, most research in the field has been focused on ideal conditions and most available databases are constructed in these ideal conditions. There has been a growing interest in the perfection of these technologies at a distance and in less than ideal conditions, i.e. low lighting, out-of-focus blur, off angles, etc. This paper presents a dataset consisting of face and iris videos obtained at distances of 5 to 25 feet and in conditions of varying quality. The purpose of this database is to set a standard for quality measurement in face and iris data and to provide a means for analyzing biométrie systems in less than ideal conditions. The structure of the dataset as well as a quantified metric for quality measurement based on a 25 subject subset of the dataset is presented.

Impact of out-of-focus blur on face recognition performance based on modular transfer function

It is well recognized that face recognition performance is impacted by the image quality. As face recognition is increasingly used in semi-cooperative or unconstrained applications, quantifying the impact of degraded image quality can provide the basis for improving recognition performance. This study uses a range of real out-of-focus blur obtained by controlled changes of the focal plane across face video sequences during acquisition from the Q-FIRE dataset. The modulation transfer function (MTF) method for measuring sharpness is presented and compared with other sharpness measurements with a reference of the co-located optical chart. Face recognition performance is then examined at eleven sharpness levels based on the MTF quality metrics. Experimental results show the MTF quality metrics better quantify a range of blur compared to the optical chart and offer a useful range of interest for face recognition performance. This paper demonstrates the applicability of an image blur quality metric as auxiliary information to supplement face recognition systems through the analysis of a unique database.

A study on quality-adjusted impact of time lapse on iris recognition

Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris recognition regression indicating the variability in match scores. According to the regression analysis, our study in this paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors.

Transcriptome-wide gene expression in a rat model of attention deficit hyperactivity disorder symptoms: rats developmentally exposed to polychlorinated biphenyls.

Polychlorinated biphenyls (PCB) exposure in rodents provides a useful model for the symptoms of Attention deficit hyperactivity disorder (ADHD). The goal of this study is to identify genes whose expression levels are altered in response to PCB exposure. The brains from 48 rats separated into two age groups of 24 animals each (4 males and 4 females for each PCB exposure level (control, PCB utero, and PCB lactational)) were harvested at postnatal days 23 and 35, respectively. The RNA was isolated from three brain regions of interest and was analyzed for differences in expression of a set of 27,342 transcripts. Two hundred seventy‐nine transcripts showed significant differential expression due to PCB exposure mostly due to the difference between PCB lactational and control groups. The cluster analysis applied to these transcripts revealed that significant changes in gene expression levels in PFC area due to PCB lactational exposure. Our pathway analyses implicated 27 significant canonical pathways and 38 significant functional pathways. Our transcriptome‐wide analysis of the effects of PCB exposure shows that the expression of many genes is dysregulated by lactational PCB exposure, but not gestational exposure and has highlighted biological pathways that might mediate the effects of PCB exposure on ADHD‐like behaviors seen in exposed animals. Our work should further motivate studies of fatty acids in ADHD, and further suggests that another potentially druggable pathway, oxidative stress, may play a role in PCB induced ADHD behaviors.