Energy intake estimation from counts of chews and swallows.

Current, validated methods for dietary assessment rely on self-report, which tends to be inaccurate, time-consuming, and burdensome. The objective of this work was to demonstrate the suitability of estimating energy intake using individually-calibrated models based on Counts of Chews and Swallows (CCS models). In a laboratory setting, subjects consumed three identical meals (training meals) and a fourth meal with different content (validation meal). Energy intake was estimated by four different methods: weighed food records (gold standard), diet diaries, photographic food records, and CCS models. Counts of chews and swallows were measured using wearable sensors and video analysis. Results for the training meals demonstrated that CCS models presented the lowest reporting bias and a lower error as compared to diet diaries. For the validation meal, CCS models showed reporting errors that were not different from the diary or the photographic method. The increase in error for the validation meal may be attributed to differences in the physical properties of foods consumed during training and validation meals. However, this may be potentially compensated for by including correction factors into the models. This study suggests that estimation of energy intake from CCS may offer a promising alternative to overcome limitations of self-report.

Thin-film microelectronic wearable body sensors.

This review of various applications of well-established thin-film processing techniques to wearable body sensors gives examples of work done in the author's laboratory over many years. Sensors for the vital signs of body temperature, electrocardiogram, heart rate, breathing pattern and breathing rate are presented along with other applications. Thin-film based sensors have the advantage of small size, high surface area to mass ratio, flexibility, capability for batch production, and compatibility with other microelectronic technologies.

Neonatal heartbeat annunciator for use in the developing world.

The majority of neonatal deaths occur in the developing countries. In many cases, unresponsive infants at birth are alive although birth attendants think they are dead and do not attempt to resuscitate them. In order to address this problem, training for birth attendant skills for resuscitation and having a device to determine the newborn heartbeat are necessary. In this project, a neonatal heartbeat annunciator has been designed and undergone preliminary evaluation. The device is quickly attached and uses electrodes to pick up the ECG signal from the infant's chest. Following electronic processing, the heartbeat is indicated as a flash of LED light and the sound of a high-frequency buzzer. It is hoped that this device will encourage birth attendants to attempt to resuscitate unresponsive babies and help to reduce neonatal mortality.

Powering biomedical devices with body motion.

Energy harvesting from body motion is an alternative power source that can be used to energize miniature electronic biomedical devices. This technology can make it possible to recharge batteries to reduce the frequency of or eliminate surgeries to replace depleted cells. Power availability evaluation from walking and running at several body locations and different speeds is presented. Treadmill tests were performed on 11 healthy subjects to measure the accelerations at the ankle, knee, hip, chest, wrist, elbow, upper arm, and side of the head. Power was estimated from the treadmill results since it is proportional to the acceleration magnitudes and the frequency of occurrence. Available power output from walking was found to be more than 0.5 mW/cm(3) for all body locations while being more than 10 mW/cm(3) for the ankle and knee. Running results were at least 10 times higher than those from walking. An axial flux miniature electric dynamo using electromagnetic induction was evaluated for power generation. The device was composed of a rotor with multiple-pole permanent magnets positioned on an annular ring having an eccentric mass, and stacked planar coils as a stator. A 2 cm(3) prototype was found to generate 117 microW of power from the generator placed laterally on the ankle while walking.

Detection of food intake from swallowing sequences by supervised and unsupervised methods.

Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone.

Automatic detection of swallowing events by acoustical means for applications of monitoring of 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 h with a total of 9966 swallows) collected from 20 human subjects with various degrees of adiposity. Average weighted epoch-recognition accuracy for intravisit 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.

Body motion for powering biomedical devices.

Kinetic energy harvesting has been demonstrated as a useful technique for powering portable electronic devices. Body motion can be used to generate energy to power small electronic devices for biomedical applications. These scavengers can recharge batteries, extending their operation lifetime or even replace them. This paper addresses the generation of energy from human activities. An axial flux generator is presented using body motion for powering miniature biomedical devices. This generator presents a gear-shaped planar coil and a multipole NdFeB permanent magnet (PM) ring with an attached eccentric weight. The device generates energy by electromagnetic induction on the planar coil when subject to a changing magnetic flux due to the generator oscillations produced by body motion. A 1.5 cm(3) prototype has generated 3.9 microW of power while walking with the generator placed laterally on the ankle.

Toward objective monitoring of ingestive behavior in free-living population.

Understanding of eating behaviors associated with obesity requires objective and accurate monitoring of food intake patterns. Accurate methods are available for measuring total energy expenditure and its components in free-living populations, but methods for measuring food intake in free-living people are far less accurate and involve self-reporting or subjective monitoring. We suggest that chews and swallows can be used for objective monitoring of ingestive behavior. This hypothesis was verified in a human study involving 20 subjects. Chews and swallows were captured during periods of quiet resting, talking, and meals of varying size. The counts of chews and swallows along with other derived metrics were used to build prediction models for detection of food intake, differentiation between liquids and solids, and for estimation of the mass of ingested food. The proposed prediction models were able to detect periods of food intake with >95% accuracy and a fine time resolution of 30 s, differentiate solid foods from liquids with >91% accuracy, and predict mass of ingested food with >91% accuracy for solids and >83% accuracy for liquids. In earlier publications, we have shown that chews and swallows can be captured by noninvasive sensors that could be developed into a wearable device. Thus, the proposed methodology could lead to the development of an innovative new way of assessing human eating behavior in free-living conditions.