Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study.

BACKGROUND: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. OBJECTIVE: We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches. METHODS: We leveraged our prototype therapeutic smartphone game, GuessWhat, which was designed in large part for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game. Independently, we created a secure web interface to gamify the human labeling effort, called HollywoodSquares, tailored for use by any qualified labeler. We gathered and labeled 2155 videos, 39,968 emotion frames, and 106,001 labels on all images. With this drastically expanded pediatric emotion-centric database (>30 times larger than existing public pediatric emotion data sets), we trained a convolutional neural network (CNN) computer vision classifier of happy, sad, surprised, fearful, angry, disgust, and neutral expressions evoked by children. RESULTS: The classifier achieved a 66.9% balanced accuracy and 67.4% F1-score on the entirety of the Child Affective Facial Expression (CAFE) as well as a 79.1% balanced accuracy and 78% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels. This performance is at least 10% higher than all previously developed classifiers evaluated against CAFE, the best of which reached a 56% balanced accuracy even when combining "anger" and "disgust" into a single class. CONCLUSIONS: This work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant data sets to train state-of-the-art classifiers to perform tasks helpful to precision health efforts.

Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection.

Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107. Two previously validated ASD logistic regression classifiers, evaluated against parent-reported diagnoses, were used to assess the accuracy of the trusted crowd's ratings of unstructured home videos. A representative balanced sample (N = 50 videos) of videos were evaluated with and without face box and pitch shift privacy alterations, with AUROC and AUPRC scores > 0.98. With both privacy-preserving modifications, sensitivity is preserved (96.0%) while maintaining specificity (80.0%) and accuracy (88.0%) at levels comparable to prior classification methods without alterations. We find that machine learning classification from features extracted by a certified nonexpert crowd achieves high performance for ASD detection from natural home videos of the child at risk and maintains high sensitivity when privacy-preserving mechanisms are applied. These results suggest that privacy-safeguarded crowdsourced analysis of short home videos can help enable rapid and mobile machine-learning detection of developmental delays in children.

Selection of trustworthy crowd workers for telemedical diagnosis of pediatric autism spectrum disorder.

Crowd-powered telemedicine has the potential to revolutionize healthcare, especially during times that require remote access to care. However, sharing private health data with strangers from around the world is not compatible with data privacy standards, requiring a stringent filtration process to recruit reliable and trustworthy workers who can go through the proper training and security steps. The key challenge, then, is to identify capable, trustworthy, and reliable workers through high-fidelity evaluation tasks without exposing any sensitive patient data during the evaluation process. We contribute a set of experimentally validated metrics for assessing the trustworthiness and reliability of crowd workers tasked with providing behavioral feature tags to unstructured videos of children with autism and matched neurotypical controls. The workers are blinded to diagnosis and blinded to the goal of using the features to diagnose autism. These behavioral labels are fed as input to a previously validated binary logistic regression classifier for detecting autism cases using categorical feature vectors. While the metrics do not incorporate any ground truth labels of child diagnosis, linear regression using the 3 correlative metrics as input can predict the mean probability of the correct class of each worker with a mean average error of 7.51% for performance on the same set of videos and 10.93% for performance on a distinct balanced video set with different children. These results indicate that crowd workers can be recruited for performance based largely on behavioral metrics on a crowdsourced task, enabling an affordable way to filter crowd workforces into a trustworthy and reliable diagnostic workforce.

Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels.

Background/Introduction: Emotion detection classifiers traditionally predict discrete emotions. However, emotion expressions are often subjective, thus requiring a method to handle compound and ambiguous labels. We explore the feasibility of using crowdsourcing to acquire reliable soft-target labels and evaluate an emotion detection classifier trained with these labels. We hypothesize that training with labels that are representative of the diversity of human interpretation of an image will result in predictions that are similarly representative on a disjoint test set. We also hypothesize that crowdsourcing can generate distributions which mirror those generated in a lab setting. Methods: We center our study on the Child Affective Facial Expression (CAFE) dataset, a gold standard collection of images depicting pediatric facial expressions along with 100 human labels per image. To test the feasibility of crowdsourcing to generate these labels, we used Microworkers to acquire labels for 207 CAFE images. We evaluate both unfiltered workers as well as workers selected through a short crowd filtration process. We then train two versions of a ResNet-152 neural network on soft-target CAFE labels using the original 100 annotations provided with the dataset: (1) a classifier trained with traditional one-hot encoded labels, and (2) a classifier trained with vector labels representing the distribution of CAFE annotator responses. We compare the resulting softmax output distributions of the two classifiers with a 2-sample independent t-test of L1 distances between the classifier's output probability distribution and the distribution of human labels. Results: While agreement with CAFE is weak for unfiltered crowd workers, the filtered crowd agree with the CAFE labels 100% of the time for happy, neutral, sad and "fear + surprise", and 88.8% for "anger + disgust". While the F1-score for a one-hot encoded classifier is much higher (94.33% vs. 78.68%) with respect to the ground truth CAFE labels, the output probability vector of the crowd-trained classifier more closely resembles the distribution of human labels (t=3.2827, p=0.0014). Conclusions: For many applications of affective computing, reporting an emotion probability distribution that accounts for the subjectivity of human interpretation can be more useful than an absolute label. Crowdsourcing, including a sufficient filtering mechanism for selecting reliable crowd workers, is a feasible solution for acquiring soft-target labels.

A Mobile Game for Automatic Emotion-Labeling of Images.

In this paper, we describe challenges in the development of a mobile charades-style game for delivery of social training to children with Autism Spectrum Disorder (ASD). Providing real-time feedback and adapting game difficulty in response to the child's performance necessitates the integration of emotion classifiers into the system. Due to the limited performance of existing emotion recognition platforms for children with ASD, we propose a novel technique to automatically extract emotion-labeled frames from video acquired from game sessions, which we hypothesize can be used to train new emotion classifiers to overcome these limitations. Our technique, which uses probability scores from three different classifiers and meta information from game sessions, correctly identified 83% of frames compared to a baseline of 51.6% from the best emotion classification API evaluated in our work.

The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study.

BACKGROUND: Autism spectrum disorder (ASD) is a developmental disorder characterized by deficits in social communication and interaction, and restricted and repetitive behaviors and interests. The incidence of ASD has increased in recent years; it is now estimated that approximately 1 in 40 children in the United States are affected. Due in part to increasing prevalence, access to treatment has become constrained. Hope lies in mobile solutions that provide therapy through artificial intelligence (AI) approaches, including facial and emotion detection AI models developed by mainstream cloud providers, available directly to consumers. However, these solutions may not be sufficiently trained for use in pediatric populations. OBJECTIVE: Emotion classifiers available off-the-shelf to the general public through Microsoft, Amazon, Google, and Sighthound are well-suited to the pediatric population, and could be used for developing mobile therapies targeting aspects of social communication and interaction, perhaps accelerating innovation in this space. This study aimed to test these classifiers directly with image data from children with parent-reported ASD recruited through crowdsourcing. METHODS: We used a mobile game called Guess What? that challenges a child to act out a series of prompts displayed on the screen of the smartphone held on the forehead of his or her care provider. The game is intended to be a fun and engaging way for the child and parent to interact socially, for example, the parent attempting to guess what emotion the child is acting out (eg, surprised, scared, or disgusted). During a 90-second game session, as many as 50 prompts are shown while the child acts, and the video records the actions and expressions of the child. Due in part to the fun nature of the game, it is a viable way to remotely engage pediatric populations, including the autism population through crowdsourcing. We recruited 21 children with ASD to play the game and gathered 2602 emotive frames following their game sessions. These data were used to evaluate the accuracy and performance of four state-of-the-art facial emotion classifiers to develop an understanding of the feasibility of these platforms for pediatric research. RESULTS: All classifiers performed poorly for every evaluated emotion except happy. None of the classifiers correctly labeled over 60.18% (1566/2602) of the evaluated frames. Moreover, none of the classifiers correctly identified more than 11% (6/51) of the angry frames and 14% (10/69) of the disgust frames. CONCLUSIONS: The findings suggest that commercial emotion classifiers may be insufficiently trained for use in digital approaches to autism treatment and treatment tracking. Secure, privacy-preserving methods to increase labeled training data are needed to boost the models' performance before they can be used in AI-enabled approaches to social therapy of the kind that is common in autism treatments.

Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry.

Data science and digital technologies have the potential to transform diagnostic classification. Digital technologies enable the collection of big data, and advances in machine learning and artificial intelligence enable scalable, rapid, and automated classification of medical conditions. In this review, we summarize and categorize various data-driven methods for diagnostic classification. In particular, we focus on autism as an example of a challenging disorder due to its highly heterogeneous nature. We begin by describing the frontier of data science methods for the neuropsychiatry of autism. We discuss early signs of autism as defined by existing pen-and-paper-based diagnostic instruments and describe data-driven feature selection techniques for determining the behaviors that are most salient for distinguishing children with autism from neurologically typical children. We then describe data-driven detection techniques, particularly computer vision and eye tracking, that provide a means of quantifying behavioral differences between cases and controls. We also describe methods of preserving the privacy of collected videos and prior efforts of incorporating humans in the diagnostic loop. Finally, we summarize existing digital therapeutic interventions that allow for data capture and longitudinal outcome tracking as the diagnosis moves along a positive trajectory. Digital phenotyping of autism is paving the way for quantitative psychiatry more broadly and will set the stage for more scalable, accessible, and precise diagnostic techniques in the field.

Feature Selection and Dimension Reduction of Social Autism Data.

Autism Spectrum Disorder (ASD) is a complex neuropsychiatric condition with a highly heterogeneous phenotype. Following the work of Duda et al., which uses a reduced feature set from the Social Responsiveness Scale, Second Edition (SRS) to distinguish ASD from ADHD, we performed item-level question selection on answers to the SRS to determine whether ASD can be distinguished from non-ASD using a similarly small subset of questions. To explore feature redundancies between the SRS questions, we performed filter, wrapper, and embedded feature selection analyses. To explore the linearity of the SRS-related ASD phenotype, we then compressed the 65-question SRS into low-dimension representations using PCA, t-SNE, and a denoising autoencoder. We measured the performance of a multilayer perceptron (MLP) classifier with the top-ranking questions as input. Classification using only the top-rated question resulted in an AUC of over 92% for SRS-derived diagnoses and an AUC of over 83% for dataset-specific diagnoses. High redundancy of features have implications towards replacing the social behaviors that are targeted in behavioral diagnostics and interventions, where digital quantification of certain features may be obfuscated due to privacy concerns. We similarly evaluated the performance of an MLP classifier trained on the low-dimension representations of the SRS, finding that the denoising autoencoder achieved slightly higher performance than the PCA and t-SNE representations.

Labeling images with facial emotion and the potential for pediatric healthcare.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by repetitive behaviors, narrow interests, and deficits in social interaction and communication ability. An increasing emphasis is being placed on the development of innovative digital and mobile systems for their potential in therapeutic applications outside of clinical environments. Due to recent advances in the field of computer vision, various emotion classifiers have been developed, which have potential to play a significant role in mobile screening and therapy for developmental delays that impair emotion recognition and expression. However, these classifiers are trained on datasets of predominantly neurotypical adults and can sometimes fail to generalize to children with autism. The need to improve existing classifiers and develop new systems that overcome these limitations necessitates novel methods to crowdsource labeled emotion data from children. In this paper, we present a mobile charades-style game, Guess What?, from which we derive egocentric video with a high density of varied emotion from a 90-second game session. We then present a framework for semi-automatic labeled frame extraction from these videos using meta information from the game session coupled with classification confidence scores. Results show that 94%, 81%, 92%, and 56% of frames were automatically labeled correctly for categories disgust, neutral, surprise, and scared respectively, though performance for angry and happy did not improve significantly from the baseline.

Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study.

BACKGROUND: Autism affects 1 in every 59 children in the United States, according to estimates from the Centers for Disease Control and Prevention's Autism and Developmental Disabilities Monitoring Network in 2018. Although similar rates of autism are reported in rural and urban areas, rural families report greater difficulty in accessing resources. An overwhelming number of families experience long waitlists for diagnostic and therapeutic services. OBJECTIVE: The objective of this study was to accurately identify gaps in access to autism care using GapMap, a mobile platform that connects families with local resources while continuously collecting up-to-date autism resource epidemiological information. METHODS: After being extracted from various databases, resources were deduplicated, validated, and allocated into 7 categories based on the keywords identified on the resource website. The average distance between the individuals from a simulated autism population and the nearest autism resource in our database was calculated for each US county. Resource load, an approximation of demand over supply for diagnostic resources, was calculated for each US county. RESULTS: There are approximately 28,000 US resources validated on the GapMap database, each allocated into 1 or more of the 7 categories. States with the greatest distances to autism resources included Alaska, Nevada, Wyoming, Montana, and Arizona. Of the 7 resource categories, diagnostic resources were the most underrepresented, comprising only 8.83% (2472/28,003) of all resources. Alarmingly, 83.86% (2635/3142) of all US counties lacked any diagnostic resources. States with the highest diagnostic resource load included West Virginia, Kentucky, Maine, Mississippi, and New Mexico. CONCLUSIONS: Results from this study demonstrate the sparsity and uneven distribution of diagnostic resources in the United States, which may contribute to the lengthy waitlists and travel distances-barriers to be overcome to be able to receive diagnosis in specific regions. More data are needed on autism diagnosis demand to better quantify resource needs across the United States.

Addendum to the Acknowledgements: Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks.

[This corrects the article DOI: 10.2196/13668.].

Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks.

BACKGROUND: Obtaining a diagnosis of neuropsychiatric disorders such as autism requires long waiting times that can exceed a year and can be prohibitively expensive. Crowdsourcing approaches may provide a scalable alternative that can accelerate general access to care and permit underserved populations to obtain an accurate diagnosis. OBJECTIVE: We aimed to perform a series of studies to explore whether paid crowd workers on Amazon Mechanical Turk (AMT) and citizen crowd workers on a public website shared on social media can provide accurate online detection of autism, conducted via crowdsourced ratings of short home video clips. METHODS: Three online studies were performed: (1) a paid crowdsourcing task on AMT (N=54) where crowd workers were asked to classify 10 short video clips of children as "Autism" or "Not autism," (2) a more complex paid crowdsourcing task (N=27) with only those raters who correctly rated ≥8 of the 10 videos during the first study, and (3) a public unpaid study (N=115) identical to the first study. RESULTS: For Study 1, the mean score of the participants who completed all questions was 7.50/10 (SD 1.46). When only analyzing the workers who scored ≥8/10 (n=27/54), there was a weak negative correlation between the time spent rating the videos and the sensitivity (ρ=-0.44, P=.02). For Study 2, the mean score of the participants rating new videos was 6.76/10 (SD 0.59). The average deviation between the crowdsourced answers and gold standard ratings provided by two expert clinical research coordinators was 0.56, with an SD of 0.51 (maximum possible SD is 3). All paid crowd workers who scored 8/10 in Study 1 either expressed enjoyment in performing the task in Study 2 or provided no negative comments. For Study 3, the mean score of the participants who completed all questions was 6.67/10 (SD 1.61). There were weak correlations between age and score (r=0.22, P=.014), age and sensitivity (r=-0.19, P=.04), number of family members with autism and sensitivity (r=-0.195, P=.04), and number of family members with autism and precision (r=-0.203, P=.03). A two-tailed t test between the scores of the paid workers in Study 1 and the unpaid workers in Study 3 showed a significant difference (P<.001). CONCLUSIONS: Many paid crowd workers on AMT enjoyed answering screening questions from videos, suggesting higher intrinsic motivation to make quality assessments. Paid crowdsourcing provides promising screening assessments of pediatric autism with an average deviation <20% from professional gold standard raters, which is potentially a clinically informative estimate for parents. Parents of children with autism likely overfit their intuition to their own affected child. This work provides preliminary demographic data on raters who may have higher ability to recognize and measure features of autism across its wide range of phenotypic manifestations.

Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study.

BACKGROUND: Autism spectrum disorder (ASD) is currently diagnosed using qualitative methods that measure between 20-100 behaviors, can span multiple appointments with trained clinicians, and take several hours to complete. In our previous work, we demonstrated the efficacy of machine learning classifiers to accelerate the process by collecting home videos of US-based children, identifying a reduced subset of behavioral features that are scored by untrained raters using a machine learning classifier to determine children's "risk scores" for autism. We achieved an accuracy of 92% (95% CI 88%-97%) on US videos using a classifier built on five features. OBJECTIVE: Using videos of Bangladeshi children collected from Dhaka Shishu Children's Hospital, we aim to scale our pipeline to another culture and other developmental delays, including speech and language conditions. METHODS: Although our previously published and validated pipeline and set of classifiers perform reasonably well on Bangladeshi videos (75% accuracy, 95% CI 71%-78%), this work improves on that accuracy through the development and application of a powerful new technique for adaptive aggregation of crowdsourced labels. We enhance both the utility and performance of our model by building two classification layers: The first layer distinguishes between typical and atypical behavior, and the second layer distinguishes between ASD and non-ASD. In each of the layers, we use a unique rater weighting scheme to aggregate classification scores from different raters based on their expertise. We also determine Shapley values for the most important features in the classifier to understand how the classifiers' process aligns with clinical intuition. RESULTS: Using these techniques, we achieved an accuracy (area under the curve [AUC]) of 76% (SD 3%) and sensitivity of 76% (SD 4%) for identifying atypical children from among developmentally delayed children, and an accuracy (AUC) of 85% (SD 5%) and sensitivity of 76% (SD 6%) for identifying children with ASD from those predicted to have other developmental delays. CONCLUSIONS: These results show promise for using a mobile video-based and machine learning-directed approach for early and remote detection of autism in Bangladeshi children. This strategy could provide important resources for developmental health in developing countries with few clinical resources for diagnosis, helping children get access to care at an early age. Future research aimed at extending the application of this approach to identify a range of other conditions and determine the population-level burden of developmental disabilities and impairments will be of high value.

Guess What?: Towards Understanding Autism from Structured Video Using Facial Affect.

Autism Spectrum Disorder (ASD) is a condition affecting an estimated 1 in 59 children in the United States. Due to delays in diagnosis and imbalances in coverage, it is necessary to develop new methods of care delivery that can appropriately empower children and caregivers by capitalizing on mobile tools and wearable devices for use outside of clinical settings. In this paper, we present a mobile charades-style game, Guess What?, used for the acquisition of structured video from children with ASD for behavioral disease research. We then apply face tracking and emotion recognition algorithms to videos acquired through Guess What? game play. By analyzing facial affect in response to various prompts, we demonstrate that engagement and facial affect can be quantified and measured using real-time image processing algorithms: an important first-step for future therapies, at-home screenings, and outcome measures based on home video. Our study of eight subjects demonstrates the efficacy of this system for deriving highly emotive structured video from children with ASD through an engaging gamified mobile platform, while revealing the most efficacious prompts and categories for producing diverse emotion in participants.

Mobile detection of autism through machine learning on home video: A development and prospective validation study.

BACKGROUND: The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy. We hypothesize that the use of machine learning analysis on home video can speed the diagnosis without compromising accuracy. We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy. In the present study, we prospectively test whether the features from these optimized models can be extracted by blinded nonexpert raters from 3-minute home videos of children with and without ASD to arrive at a rapid and accurate machine learning autism classification. METHODS AND FINDINGS: We created a mobile web portal for video raters to assess 30 behavioral features (e.g., eye contact, social smile) that are used by 8 independent machine learning models for identifying ASD, each with >94% accuracy in cross-validation testing and subsequent independent validation from previous work. We then collected 116 short home videos of children with autism (mean age = 4 years 10 months, SD = 2 years 3 months) and 46 videos of typically developing children (mean age = 2 years 11 months, SD = 1 year 2 months). Three raters blind to the diagnosis independently measured each of the 30 features from the 8 models, with a median time to completion of 4 minutes. Although several models (consisting of alternating decision trees, support vector machine [SVM], logistic regression (LR), radial kernel, and linear SVM) performed well, a sparse 5-feature LR classifier (LR5) yielded the highest accuracy (area under the curve [AUC]: 92% [95% CI 88%-97%]) across all ages tested. We used a prospectively collected independent validation set of 66 videos (33 ASD and 33 non-ASD) and 3 independent rater measurements to validate the outcome, achieving lower but comparable accuracy (AUC: 89% [95% CI 81%-95%]). Finally, we applied LR to the 162-video-feature matrix to construct an 8-feature model, which achieved 0.93 AUC (95% CI 0.90-0.97) on the held-out test set and 0.86 on the validation set of 66 videos. Validation on children with an existing diagnosis limited the ability to generalize the performance to undiagnosed populations. CONCLUSIONS: These results support the hypothesis that feature tagging of home videos for machine learning classification of autism can yield accurate outcomes in short time frames, using mobile devices. Further work will be needed to confirm that this approach can accelerate autism diagnosis at scale.

Feasibility of a Secure Wireless Sensing Smartwatch Application for the Self-Management of Pediatric Asthma.

To address the need for asthma self-management in pediatrics, the authors present the feasibility of a mobile health (mHealth) platform built on their prior work in an asthmatic adult and child. Real-time asthma attack risk was assessed through physiological and environmental sensors. Data were sent to a cloud via a smartwatch application (app) using Health Insurance Portability and Accountability Act (HIPAA)-compliant cryptography and combined with online source data. A risk level (high, medium or low) was determined using a random forest classifier and then sent to the app to be visualized as animated dragon graphics for easy interpretation by children. The feasibility of the system was first tested on an adult with moderate asthma, then usability was examined on a child with mild asthma over several weeks. It was found during feasibility testing that the system is able to assess asthma risk with 80.10 ± 14.13% accuracy. During usability testing, it was able to continuously collect sensor data, and the child was able to wear, easily understand and enjoy the use of the system. If tested in more individuals, this system may lead to an effective self-management program that can reduce hospitalization in those who suffer from asthma.

Dynamic Computation Offloading for Low-Power Wearable Health Monitoring Systems.

OBJECTIVE: The objective of this paper is to describe and evaluate an algorithm to reduce power usage and increase battery lifetime for wearable health-monitoring devices. METHODS: We describe a novel dynamic computation offloading scheme for real-time wearable health monitoring devices that adjusts the partitioning of data processing between the wearable device and mobile application as a function of desired classification accuracy. RESULTS: By making the correct offloading decision based on current system parameters, we show that we are able to reduce system power by as much as 20%. CONCLUSION: We demonstrate that computation offloading can be applied to real-time monitoring systems, and yields significant power savings. SIGNIFICANCE: Making correct offloading decisions for health monitoring devices can extend battery life and improve adherence.

Remote Health Monitoring Outcome Success Prediction Using Baseline and First Month Intervention Data.

Remote health monitoring (RHM) systems are becoming more widely adopted by clinicians and hospitals to remotely monitor and communicate with patients while optimizing clinician time, decreasing hospital costs, and improving quality of care. In the Women's heart health study (WHHS), we developed Wanda-cardiovascular disease (CVD), where participants received healthy lifestyle education followed by six months of technology support and reinforcement. Wanda-CVD is a smartphone-based RHM system designed to assist participants in reducing identified CVD risk factors through wireless coaching using feedback and prompts as social support. Many participants benefitted from this RHM system. In response to the variance in participants' success, we developed a framework to identify classification schemes that predicted successful and unsuccessful participants. We analyzed both contextual baseline features and data from the first month of intervention such as activity, blood pressure, and questionnaire responses transmitted through the smartphone. A prediction tool can aid clinicians and scientists in identifying participants who may optimally benefit from the RHM system. Targeting therapies could potentially save healthcare costs, clinician, and participant time and resources. Our classification scheme yields RHM outcome success predictions with an F-measure of 91.9%, and identifies behaviors during the first month of intervention that help determine outcome success. We also show an improvement in prediction by using intervention-based smartphone data. Results from the WHHS study demonstrates that factors such as the variation in first month intervention response to the consumption of nuts, beans, and seeds in the diet help predict patient RHM protocol outcome success in a group of young Black women ages 25-45.

A Hierarchical Classification and Segmentation Scheme for Processing Sensor Data.

Detecting short-duration events from continuous sensor signals is a significant challenge in the domain of wearable devices and health monitoring systems. Time-series segmentation refers to the challenge of subdividing a continuous stream of data into discrete windows, which can be individually processed using statistical classifiers or other algorithms. In this paper, we propose an algorithm for segmenting time-series signals and detecting short-duration data in the domain of lightweight embedded systems with real-time constraints. First, we demonstrate an approach for signal segmentation using a simple binary classifier. Next, we show how a novel two-stage classification algorithm can reduce computational overhead compared to a single-stage approach. Our proposed scheme is benchmarked using an audio-based nutrition-monitoring case study.

Temperature and Humidity Calibration of a Low-Cost Wireless Dust Sensor for Real-Time Monitoring.

This paper introduces the design, calibration, and validation of a low-cost portable sensor for the real-time measurement of dust particles within the environment. The proposed design consists of low hardware cost and calibration based on temperature and humidity sensing to achieve accurate processing of airborne dust density. Using commercial particulate matter sensors, a highly accurate air quality monitoring sensor was designed and calibrated using real world variations in humidity and temperature for indoor and outdoor applications. Furthermore, to provide a low-cost secure solution for real-time data transfer and monitoring, an onboard Bluetooth module with AES data encryption protocol was implemented. The wireless sensor was tested against a Dylos DC1100 Pro Air Quality Monitor, as well as an Alphasense OPC-N2 optical air quality monitoring sensor for accuracy. The sensor was also tested for reliability by comparing the sensor to an exact copy of itself under indoor and outdoor conditions. It was found that accurate measurements under real-world humid and temperature varying and dynamically changing conditions were achievable using the proposed sensor when compared to the commercially available sensors. In addition to accurate and reliable sensing, this sensor was designed to be wearable and perform real-time data collection and transmission, making it easy to collect and analyze data for air quality monitoring and real-time feedback in remote health monitoring applications. Thus, the proposed device achieves high quality measurements at lower-cost solutions than commercially available wireless sensors for air quality.