A Review of Digital Innovations for Diet Monitoring and Precision Nutrition.

This article provides an up-to-date review of technological advances in 3 key areas related to diet monitoring and precision nutrition. First, we review developments in mobile applications, with a focus on food photography and artificial intelligence to facilitate the process of diet monitoring. Second, we review advances in 2 types of wearable and handheld sensors that can potentially be used to fully automate certain aspects of diet logging: physical sensors to detect moments of dietary intake, and chemical sensors to estimate the composition of diets and meals. Finally, we review new programs that can generate personalized/precision nutrition recommendations based on measurements of gut microbiota and continuous glucose monitors with artificial intelligence. The article concludes with a discussion of potential pitfalls of some of these technologies.

Modeling Individual Differences in Food Metabolism through Alternating Least Squares.

Understanding how macronutrients (e.g., carbohydrates, protein, fat) affect blood glucose is of broad interest in health and dietary research. The general effects are well known, e.g., adding protein and fat to a carbohydrate-based meal tend to reduce blood glucose. However, there are large individual differences in food metabolism, to where the same meal can lead to different glucose responses across individuals. To address this problem, we present a technique that can be used to simultaneously (1) model macronutrients' effects on glucose levels over time and (2) capture inter-individual differences in sensitivity to macronutrients. The model assumes that each macronutrient adds a basis function to the differences in macronutrient metabolism. The technique performs a linear decomposition of glucose responses, alternating between estimating the macronutrients' effect over time and capturing an individual's sensitivity to macronutrients. On an experimental dataset containing glucose responses to a variety of mixed meals, the technique is able to extract basis functions for the macronutrients that are consistent with their hypothesized effects on PPGRs, and also characterize how macronutrients affect individuals differently.

Mid-Infrared Serial Microring Resonator Array for Real-Time Detection of Vapor-Phase Volatile Organic Compounds.

Chip-scale infrared spectrometers consisting of a microring resonator array (MRA) were developed for volatile organic compound (VOC) detection. The MRA is serially positioned to serve as a wavelength sorting element that enables wavelength demultiplexing. Unlike conventional devices operated by a single microring, our MRA can perform multiwavelength mid-infrared (mid-IR) sensing by routing the resonant wavelength light from a broadband mid-IR source into different sensing channels. Miniaturized spectrometer devices were fabricated on mid-IR transparent silicon-rich silicon nitride (SiNx) thin films through complementary metal-oxide-semiconductor (CMOS) processes, thus enabling wafer-level manufacturing and packaging. The spectral distribution of the resonance lines and the optimization of the microring structures were designed using finite-difference time-domain (FDTD) modeling and then verified by laser spectrum scanning. Using small microring structures, the spectrum showed a large free spectral range (FSR) of 100 nm and held four spectral channels without crosstalk. Unlike near-infrared microrings using refractive index sensing, our MRA can detect hexane and ethanol vapor pulses by monitoring the intensity variation at their characteristic mid-IR absorption bands, thus providing high specificity. Applying multiwavelength detection, the sensor module can discriminate among various VOC vapors. Hence, our mid-IR MRA could be an essential component to achieve a compact spectroscopic sensing module that has the potential for applications such as remote environmental monitoring and portable health care devices.

Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: ECG and Accelerometry.

BACKGROUND: Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes. METHODS: In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine-learning approaches to predict glycemic excursions: a classification model and a regression model. RESULTS: The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone. CONCLUSIONS: Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.

Detection of volatile organic compounds using mid-infrared silicon nitride waveguide sensors.

Mid-infrared (mid-IR) sensors consisting of silicon nitride (SiN) waveguides were designed and tested to detect volatile organic compounds (VOCs). SiN thin films, prepared by low-pressure chemical vapor deposition (LPCVD), have a broad mid-IR transparent region and a lower refractive index (nSiN = 2.0) than conventional materials such as Si (nSi = 3.4), which leads to a stronger evanescent wave and therefore higher sensitivity, as confirmed by a finite-difference eigenmode (FDE) calculation. Further, in-situ monitoring of three VOCs (acetone, ethanol, and isoprene) was experimentally demonstrated through characteristic absorption measurements at wavelengths λ = 3.0-3.6 µm. The SiN waveguide showed a five-fold sensitivity improvement over the Si waveguide due to its stronger evanescent field. To our knowledge, this is the first time SiN waveguides are used to perform on-chip mid-IR spectral measurements for VOC detection. Thus, the developed waveguide sensor has the potential to be used as a compact device module capable of monitoring multiple gaseous analytes for health, agricultural and environmental applications.

Predicting the Macronutrient Composition of Mixed Meals From Dietary Biomarkers in Blood.

Diet monitoring is an essential intervention component for a number of diseases, from type 2 diabetes to cardiovascular diseases. However, current methods for diet monitoring are burdensome and often inaccurate. In prior work, we showed that continuous glucose monitors (CGMs) may be used to predict meal macronutrients (e.g., carbohydrates, protein, fat) by analyzing the shape of the post-prandial glucose response. In this study, we examine a number of additional dietary biomarkers in blood by their ability to improve macronutrient prediction, compared to using CGMs alone. For this purpose, we conducted a nutritional study where (n = 10) participants consumed nine different mixed meals with varied but known macronutrient amounts, and we analyzed the concentration of 33 dietary biomarkers (including amino acids, insulin, triglycerides, and glucose) at various times post-prandially. Then, we built machine learning models to predict macronutrient amounts from (1) individual biomarkers and (2) their combinations. We find that the additional blood biomarkers provide complementary information, and more importantly, achieve lower normalized root mean squared error (NRMSE) for the three macronutrients (carbohydrates: 22.9%; protein: 23.4%; fat: 32.3%) than CGMs alone (carbohydrates: 28.9%, t(18) =1.64, p =0.060; protein: 46.4%, t(18) =5.38, p 0.001; fat: 40.0%, t(18) =2.09, p =0.025). Our main conclusion is that augmenting CGMs to measure these additional dietary biomarkers improves macronutrient prediction performance, and may ultimately lead to the development of automated methods to monitor nutritional intake. This work is significant to biomedical research as it provides a potential solution to the long-standing problem of diet monitoring, facilitating new interventions for a number of diseases.

Evaluating the Role of Breathing Guidance on Game-Based Interventions for Relaxation Training.

Working in a fast-paced environment can lead to shallow breathing, which can exacerbate stress and anxiety. To address this issue, this study aimed to develop micro-interventions that can promote deep breathing in the presence of stressors. First, we examined two types of breathing guides to help individuals learn deep breathing: providing their breathing rate as a biofeedback signal, and providing a pacing signal to which they can synchronize their breathing. Second, we examined the extent to which these two breathing guides can be integrated into a casual game, to increase enjoyment and skill transfer. We used a 2 × 2 factorial design, with breathing guide (biofeedback vs. pacing) and gaming (game vs. no game) as independent factors. This led to four experimental groups: biofeedback alone, biofeedback integrated into a game, pacing alone, and pacing integrated into a game. In a first experiment, we evaluated the four experimental treatments in a laboratory setting, where 30 healthy participants completed a stressful task before and after performing one of the four treatments (or a control condition) while wearing a chest strap that measured their breathing rate. Two-way ANOVA of breathing rates, with treatment (5 groups) and time (pre-test, post-test) as independent factors shows a significant effect for time [F(4, 50) = 18.49, p < 0.001, η t i m e 2 = 0 . 27 ] and treatment [F(4, 50) = 2.54, p = 0.05, η2 = 0.17], but no interaction effects. Post-hoc t-tests between pre and post-test breathing rates shows statistical significance for the game with biofeedback group [t(5) = 5.94, p = 0.001, d = 2.68], but not for the other four groups, indicating that only game with biofeedback led to skill transfer at post-test. Further, two-way ANOVA of self-reported enjoyment scores on the four experimental treatments, with breathing guide and game as independent factors, found a main effect for game [ F ( 1 , 20 ) = 24 . 49 , p < 0 . 001 ,   η g a m e 2 = 0 . 55 ], indicating that the game-based interventions were more enjoyable than the non-game interventions. In a second experiment, conducted in an ambulatory setting, 36 healthy participants practiced one of the four experimental treatments as they saw fit over the course of a day. We found that the game-based interventions were practiced more often than the non-game interventions [t (34) = 1.99, p = 0.027, d = 0.67]. However, we also found that participants in the game-based interventions could only achieve deep breathing 50% of the times, whereas participants in the non-game groups succeeded 85% of the times, which indicated that the former need adequate training time to be effective. Finally, participant feedback indicated that the non-game interventions were better at promoting in-the-moment relaxation, whereas the game-based interventions were more successful at promoting deep breathing during stressful tasks.

Postprandial concentration of circulating branched chain amino acids are able to predict the carbohydrate content of the ingested mixed meal.

BACKGROUND: The amount of the macronutrients protein and carbohydrate (CHO) in a mixed meal is known to affect each other's digestion, absorption, and subsequent metabolism. While the effect of the amount of dietary protein and fat on the glycemic response is well studied, the ability of postprandial plasma amino acid patterns to predict the meal composition is unknown. OBJECTIVE: To study the postprandial plasma amino acid patterns in relation to the protein, CHO, and fat content of different mixed meals and to investigate if these patterns can predict the macronutrient meal composition. DESIGN: Ten older adults were given 9 meals with 3 different levels (low, medium, and high) of protein, CHO, and fat in different combinations, taking the medium content as that of a standardized western meal. We monitored the postprandial plasma response for amino acids, glucose, insulin, and triglycerides for 8 h and the areas under the curve (AUC) were subsequently calculated. Multiple regression analysis was performed to determine if amino acid patterns could predict the meal composition. RESULTS: Increasing meal CHO content reduced the postprandial plasma response of several amino acids including all branched chain amino acids (BCAA) (leucine; q < 0.0001, isoleucine; q = 0.0035, valine; q = 0.0022). The plasma BCAA patterns after the meal significantly predicted the meal's CHO content (leucine; p < 0.0001, isoleucine; p = 0.0003, valine; p = 0.0008) along with aspartate (p < 0.0001), tyrosine (p < 0.0001), methionine (p = 0.0159) and phenylalanine (p = 0.0332). Plasma citrulline predicted best the fat content of the meal (p = 0.0024). CONCLUSIONS: The postprandial plasma BCAA patterns are lower with increasing meal CHO content and are strong predictors of a mixed meal protein and CHO composition, as are plasma citrulline for the fat content. We hypothesize that postprandial plasma amino acid concentrations can be used to predict the meal's macronutrient composition.

All-nanoparticle layer-by-layer coatings for Mid-IR on-chip gas sensing.

Functionalization of optical waveguides with submicron coatings of zinc peroxide (ZnO2) and silica (SiO2) nanoparticles (NPs) is reported that enabled selective concentration of acetone vapors in the vicinity of the waveguide, boosting the sensitivity of a mid infrared (MIR) on-chip detector. Controlled thickness was achieved by introducing precise control of the substrate withdrawal speed to the layer-by-layer (LbL) deposition technique.

The influence of type of feedback during tablet-based delivery of intensive treatment for childhood apraxia of speech.

PURPOSE: One of the key principles of motor learning supports using knowledge of results feedback (KR, i.e., whether a response was correct / incorrect only) during high intensity motor practice, rather than knowledge of performance (KP, i.e., whether and how a response was correct/incorrect). In the future, mobile technology equipped with automatic speech recognition (ASR) could provide KR feedback, enabling this practice to move outside the clinic, supplementing speech pathology sessions and reducing burden on already stretched speech-language pathology resources. Here, we employ a randomized controlled trial design to test the impact of KR vs KP feedback on children's response to the Nuffield Dyspraxia Programme 3, delivered through an android tablet. At the time of testing, ASR was not feasible and so correctness of responses was decided by the treating clinician. METHOD: Fourteen children with CAS, aged 4-10 years, participated in a parallel group design, matched for age and severity of CAS. Both groups attended a university clinic for 1-hr therapy sessions 4 days a week for 3 weeks. One group received high frequency feedback comprised of both KR and KP, in the style of traditional, face-to-face intensive intervention on all days. The other group received high frequency KR + KP feedback on 1 day per week and high frequency KR feedback on the other 3 days per week, simulating the service delivery model of one clinic session per week supported by tablet-based home practice. RESULTS: Both groups had significantly improved speech outcomes at 4-months post-treatment. Post-hoc comparisons suggested that only the KP group showed a significant change from pre- to immediately post-treatment but the group difference had dissipated by 1-month post-treatment. Heterogeneity in response to intervention within the groups suggests that other factors, not measured here, may be having a substantive influence on response to intervention and feedback type. CONCLUSION: Mobile technology has the potential to increase motivation and engagement with therapy and to mitigate barriers associated with distance and access to speech pathology services. Further research is needed to explore the influence of type and frequency of feedback on motor learning, optimal timing for transitioning from KP to KR feedback, and how these parameters interact with task, child and context-related factors.

Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities.

We describe a controlled experiment, aiming to study productivity and stress effects of email interruptions and activity interactions in the modern office. The measurement set includes multimodal data for n = 63 knowledge workers who volunteered for this experiment and were randomly assigned into four groups: (G1/G2) Batch email interruptions with/without exogenous stress. (G3/G4) Continual email interruptions with/without exogenous stress. To provide context, the experiment's email treatments were surrounded by typical office tasks. The captured variables include physiological indicators of stress, measures of report writing quality and keystroke dynamics, as well as psychometric scores and biographic information detailing participants' profiles. Investigations powered by this dataset are expected to lead to personalized recommendations for handling email interruptions and a deeper understanding of synergistic and antagonistic office activities. Given the centrality of email in the modern office, and the importance of office work to people's lives and the economy, the present data have a valuable role to play.

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use.

Knowledge-driven dictionaries for sparse representation of continuous glucose monitoring signals.

Continuous glucose monitoring (CGM) of patients with diabetes allows the effective management of the disease and reduces the risk of hypoglycemic or hyperglycemic episodes. Towards this goal, the development of reliable CGM models is essential for representing the corresponding signals and interpreting them with respect to factors and outcomes of interest. We propose a sparse decomposition model to approximate CGM time-series as a linear combination of a small set of exemplar atoms, appropriately designed through parametric functions to capture the main fluctuations of the CGM signal. Sparse decomposition is performed through the orthogonal matching pursuit (OMP). Results indicate that the proposed model provides 0.1 relative reconstruction error with 0.8 compression rate on a publicly available dataset containing 25 patients diagnosed with Type 1 diabetes. The atoms selected from the OMP procedure can be further interpreted in relation to the clinically meaningful components of the CGM signal (e.g. glucose spikes, hypoglycemic episodes, etc.

Speech-driven mobile games for speech therapy: User experiences and feasibility.

Purpose: To assist in remote treatment, speech-language pathologists (SLPs) rely on mobile games, which though entertaining, lack feedback mechanisms. Games integrated with automatic speech recognition (ASR) offer a solution where speech productions control gameplay. We therefore performed a feasibility study to assess children's and SLPs' experiences towards speech-controlled games, game feature preferences and ASR accuracy. Method: Ten children with childhood apraxia of speech (CAS), six typically developing (TD) children and seven SLPs trialled five games and answered questionnaires. Researchers also compared the results of ASR to perceptual judgment. Result: Children and SLPs found speech-controlled games interesting and fun, despite ASR-human disagreements. They preferred games with rewards, challenge and multiple difficulty levels. Automatic speech recognition-human agreement was higher for SLPs than children, similar between TD and CAS and unaffected by CAS severity (77% TD, 75% CAS - incorrect; 51% TD, 47% CAS, 71% SLP - correct). Manual stop recording yielded higher agreement than automatic. Word length did not influence agreement. Conclusion: Children's and SLPs' positive responses towards speech-controlled games suggest that they can engage children in higher intensity practice. Our findings can guide future improvements to the ASR, recording methods and game features to improve the user experience and therapy adherence.

Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities.

Real-time gas analysis on-a-chip was demonstrated using a mid-infrared (mid-IR) microcavity. Optical apertures for the microcavity were made of ultrathin silicate membranes embedded in a silicon chip using the complementary metal-oxide-semiconductor (CMOS) process. Fourier transform infrared spectroscopy (FTIR) shows that the silicate membrane is transparent in the range of 2.5-6.0 µm, a region that overlaps with multiple characteristic gas absorption lines and therefore enables gas detection applications. A test station integrating a mid-IR tunable laser, a microgas delivery system, and a mid-IR camera was assembled to evaluate the gas detection performance. CH4, CO2, and N2O were selected as analytes due to their strong absorption bands at λ = 3.25-3.50, 4.20-4.35, and 4.40-4.65 µm, which correspond to C-H, C-O, and O-N stretching, respectively. A short subsecond response time and high gas identification accuracy were achieved. Therefore, our chip-scale mid-IR sensor provides a new platform for an in situ, remote, and embedded gas monitoring system.

BioPad: Leveraging off-the-Shelf Video Games for Stress Self-Regulation.

This paper presents an approach to use commercial videogames for biofeedback training. It consists of intercepting signals from the game controller and adapting them in real-time based on physiological measurements from the player. We present three sample implementations and a case study for teaching stress self-regulation via an immersive car racing game. We use a crossover gaming device to manipulate controller signals, and a respiratory sensor to monitor the players' breathing rate. We then alter the speed of the car to encourage slow deep breathing, in this way, allowing players to reduce their arousal while playing the game. We evaluate the approach against an alternative form of biofeedback that uses a graphic overlay to convey physiological information, and a control condition (playing the game without biofeedback). Experimental results show that our approach can promote deep breathing during gameplay, and also during a subsequent task, once biofeedback is removed. Our results also indicate that delivering biofeedback through subtle changes in gameplay can be as effective as delivering them directly through a visual display. These results open the possibility to develop low-cost and engaging biofeedback interventions using a variety of commercial videogames to promote adherence.

Physiological Modalities for Relaxation Skill Transfer in Biofeedback Games.

We present an adaptive biofeedback game for teaching self-regulation of stress. Our approach consists of monitoring the user's physiology during gameplay and adapting the game using a positive feedback loop that rewards relaxing behaviors and penalizes states of high arousal. We evaluate the approach using a casual game under three biofeedback modalities: electrodermal activity, heart rate variability, and breathing rate. The three biosignals can be measured noninvasively with wearable sensors, and represent different degrees of voluntary control and selectivity toward arousal. We conducted an experiment trial with 25 participants to compare the three modalities against a standard treatment (deep breathing) and a control condition (the game without biofeedback). Our results indicate that breathing-based game biofeedback is more effective in inducing relaxation during treatment than the other four groups. Participants in this group also showed greater retention of the relaxation skills (without biofeedback) during a subsequent stressor.

Active wavelength selection for mixture identification with tunable mid-infrared detectors.

This article presents a wavelength selection framework for mixture identification problems. In contrast with multivariate calibration, where the mixture constituents are known and the goal is to estimate their concentration, in mixture identification the goal is to determine which of a large number of chemicals is present. Due to the combinatorial nature of this problem, traditional wavelength selection algorithms are unsuitable because the optimal set of wavelengths is mixture dependent. To address this issue, our framework interleaves wavelength selection with the sensing process, such that each subsequent wavelength is determined on-the-fly based on previous measurements. To avoid early convergence, our approach starts with an exploratory criterion that samples the spectrum broadly, then switches to an exploitative criterion that selects increasingly more relevant wavelengths as the solution approaches the true constituents of the mixture. We compare this "active" wavelength selection algorithm against a state-of-the-art passive algorithm (successive projection algorithm), both experimentally using a tunable spectrometer and in simulation using a large spectral library of chemicals. Our results show that our active method can converge to the true solution more frequently and with fewer measurements than the passive algorithm. The active method also leads to more compact solutions with fewer false positives.

Reduction of non-native accents through statistical parametric articulatory synthesis.

This paper presents an articulatory synthesis method to transform utterances from a second language (L2) learner to appear as if they had been produced by the same speaker but with a native (L1) accent. The approach consists of building a probabilistic articulatory synthesizer (a mapping from articulators to acoustics) for the L2 speaker, then driving the model with articulatory gestures from a reference L1 speaker. To account for differences in the vocal tract of the two speakers, a Procrustes transform is used to bring their articulatory spaces into registration. In a series of listening tests, accent conversions were rated as being more intelligible and less accented than L2 utterances while preserving the voice identity of the L2 speaker. No significant effect was found between the intelligibility of accent-converted utterances and the proportion of phones outside the L2 inventory. Because the latter is a strong predictor of pronunciation variability in L2 speech, these results suggest that articulatory resynthesis can decouple those aspects of an utterance that are due to the speaker's physiology from those that are due to their linguistic gestures.

Using an ambulatory stress monitoring device to identify relaxation due to untrained deep breathing.

The objective of this paper is to assess the efficacy of deep breathing as a relaxation activity using a wearable stress monitor. For this purpose, we developed a protocol with different mentally stressful activities interleaved with regular sessions of deep breathing. We used three physiological sensors: a heart rate monitor, a respiration sensor, and an electrodermal activity sensor, to extract parameters that are consistent with the dominance of the sympathetic nervous system. Our results indicate that a large number of subjects were not able to perform the paced deep breathing exercise properly, which caused their stress levels to increase rather than to decrease. The study also showed that our wearable stress monitor can be used to monitor breathing technique and assess its effectiveness in relaxing individuals.