Posture and activity recognition and energy expenditure estimation in a wearable platform.

The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here, we describe the use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time recognition of various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we compare the use of support vector machines (SVM), multinomial logistic discrimination (MLD), and multilayer perceptrons (MLP) for posture and activity classification followed by activity-branched EE estimation.

Posture and activity recognition and energy expenditure prediction in a wearable platform.

The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here we describe use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time prediction and display of time spent in various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we introduce new algorithms that require substantially less computational power.

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.

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.

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.

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.

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.

Detection and prediction of concentrations of neurotransmitters using voltammetry and pattern recognition.

Neurotransmitters (NTs) are substances in the brain which are responsible for the transmission of neurological impulses. Changes in their concentrations are associated with numerous behavioral and physiological processes and neurological disorders. As opposed to the traditional chromatographic and capillary electrophoresis, using electrochemical sensors is a fast and inexpensive way to determine concentrations of NTs.

Algorithm for haplotype resolution and block partitioning for partial XOR-genotype data.

Problems of haplotyping and block partitioning have been extensively studied with regard to the regular genotype data, but more cost-efficient data called XOR-genotypes remain under-investigated. Previous studies developed methods for haplotyping of short-sequence partial XOR-genotypes. In this paper we propose a new algorithm that performs haplotyping of long-range partial XOR-genotype data with possibility of missing entries, and in addition simultaneously finds the block structure for the given data.

Haplotype inference and block partitioning in mixed population samples.

Multi-population haplotype inference and block partitioning is a difficult task when dealing with mixed genotype samples. A number of studies have shown that the haplotype block structures, as well as the collections of common haplotypes and their frequencies, vary significantly among world populations. These differences are more extreme when the geographical locations for the populations are more distant.

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.

Sleep state scoring in infants from respiratory and activity measurements.

Sleep state scoring usually relies on polysomnographic measurements, which include electroencephalogram (EEG), electromyogram (EMG), electro-oculogram (EOG), two or three lead chest electrocardiogram (ECG), and may include other measurements. Overall, polysomnography is an intrusive procedure not well tolerated by infants and elderly. The goal of this research is to study possibility of automatic sleep state scoring from less intrusive measurements such as activity measurements and respiratory measurements by inductive plethysmography.

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.