Michael R. Neuman

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.

Energy scavenging sources for biomedical sensors

Energy scavenging has increasingly become an interesting option for powering electronic devices because of the almost infinite lifetime and the non-dependence on fuels for energy generation. Moreover, the rise of wireless technologies promises new applications in medical monitoring systems, but these still face limitations due to battery lifetime and size. A trade-off of these two factors has typically governed the size, useful life and capabilities of an autonomous system. Energy generation from sources such as motion, light and temperature gradients has been established as commercially viable alternatives to batteries for human-powered flashlights, solar calculators, radio receivers and thermal-powered wristwatches, among others. Research on energy harvesting from human activities has also addressed the feasibility of powering wearable or implantable systems. Biomedical sensors can take advantage of human-based activities as the energy source for energy scavengers. This review describes the state of the art of energy scavenging technologies for powering sensors and instrumentation of physiological variables. After a short description of the human power and the energy generation limits, the different transduction mechanisms, recent developments and challenges faced are reviewed and discussed.

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.

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.

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.

Grand Challenges in Interfacing Engineering With Life Sciences and Medicine

This paper summarizes the discussions held during the First IEEE Life Sciences Grand Challenges Conference, held on October 4-5, 2012, at the National Academy of Sciences, Washington, DC, and the grand challenges identified by the conference participants. Despite tremendous efforts to develop the knowledge and ability that are essential in addressing biomedical and health problems using engineering methodologies, the optimization of this approach toward engineering the life sciences and healthcare remains a grand challenge. The conference was aimed at high-level discussions by participants representing various sectors, including academia, government, and industry. Grand challenges were identified by the conference participants in five areas including engineering the brain and nervous system; engineering the cardiovascular system; engineering of cancer diagnostics, therapeutics, and prevention; translation of discoveries to clinical applications; and education and training. A number of these challenges are identified and summarized in this paper.

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.

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 cm3 prototype has generated 3.9 µW of power while walking with the generator placed laterally on the ankle.

Rotational energy harvester for body motion

This paper presents a micro-rotational energy harvester topology for extracting electric energy from human body motion at joint locations. This was accomplished using an inertial-based axial flux machine constructed with multiple permanent magnet poles and stacked microfabricated planar coils. Several body locations were tested while walking and running on a motor-driven treadmill. An average power of 472µW was obtained when the 2cm3 device was placed on the ankle while walking at 4mph.

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.