Meiyi Ma
Assistant Professor Computer Science Vanderbilt University Office: 401E, 1025 16th Ave S, Nashville, TN 37212 Email: meiyi.ma AT vanderbilt.edu
2025
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2024
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Abstract:
Emergency and non-emergency response systems are essential services provided by local governments and critical to protecting lives, the environment, and property. The effective handling of (non-)emergency calls is critical for public safety and well-being. By reducing the burden through non-emergency callers, residents in critical need of assistance through 911 will receive a fast and effective response. Collaborating with the Department of Emergency Communications (DEC) in Nashville, we analyzed 11,796 non-emergency call recordings and developed Auto311, the first automated system to handle 311 non-emergency calls, which (1) effectively and dynamically predicts ongoing non-emergency incident types to generate tailored case reports during the call; (2) itemizes essential information from dialogue contexts to complete the generated reports; and (3) strategically structures system-caller dialogues with optimized confidence. We used real-world data to evaluate the system's effectiveness and deployability. The experimental results indicate that the system effectively predicts incident type with an average F-1 score of 92.54%. Moreover, the system successfully itemizes critical information from relevant contexts to complete reports, evincing a 0.93 average consistency score compared to the ground truth. Additionally, emulations demonstrate that the system effectively decreases conversation turns as the utterance size gets more extensive and categorizes the ongoing call with 94.49% mean accuracy. |
2023
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Abstract:
Physical therapy (PT) is crucial for patients to restore and maintain mobility, function, and well-being. Many on-site activities and body exercises are performed under the supervision of therapists or clinicians. However, the postures of some exercises at home cannot be performed accurately due to the lack of supervision, quality assessment, and self-correction. Therefore, in this paper, we design a new framework, PhysiQ, that continuously tracks and quantitatively measures people's off-site exercise activity through passive sensory detection. In the framework, we create a novel multi-task spatio-temporal Siamese Neural Network that measures the absolute quality through classification and relative quality based on an individual's PT progress through similarity comparison. PhysiQ digitizes and evaluates exercises in three different metrics: range of motions, stability, and repetition. |
Abstract:
Video capturing devices with limited storage capacity have become increasingly common in recent years. As a result, there is a growing demand for techniques that can effectively analyze and understand these videos. While existing approaches based on data-driven methods have shown promise, they are often constrained by the availability of training data. In this paper, we focus on dashboard camera videos and propose a novel technique for recognizing important events, detecting traffic accidents, and trimming accident video evidence based on anomaly detection results. By leveraging meaningful high-level time-series abstraction and logical reasoning methods with state-of-the-art data-driven techniques, we aim to pinpoint critical evidence of traffic accidents in driving videos captured under various traffic conditions with promising accuracy, continuity, and integrity. Our approach highlights the importance of utilizing a formal system of logic specifications to deduce the relational features extracted from a sequence of video frames and meets the practical limitations of real-time deployment. |
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2022
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Abstract:
Rigorous approaches based on formal methods have the potential to fundamentally improve many aspects of deep learning. This article discusses the challenges and future directions of formal methods enhanced deep learning for smart cities. |
Abstract:
As various smart services are increasingly deployed in modern cities, many unexpected conflicts arise due to various physical world couplings. Existing solutions for conflict resolution often rely on centralized control to enforce predetermined and fixed priorities of different services, which is challenging due to the inconsistent and private objectives of the services. Also, the centralized solutions miss opportunities to more effectively resolve conflicts according to their spatiotemporal locality of the conflicts. To address this issue, we design a decentralized negotiation and conflict resolution framework named DeResolver, which allows services to resolve conflicts by communicating and negotiating with each other to reach a Pareto-optimal agreement autonomously and efficiently. Our design features a two-step self-supervised learning-based algorithm to predict acceptable proposals and their rankings of each opponent through the negotiation. Our design is evaluated with a smart city case study of three services: intelligent traffic light control, pedestrian service, and environmental control. In this case study, a data-driven evaluation is conducted using a large dataset consisting of the GPS locations of 246 surveillance cameras and an automatic traffic monitoring system with more than 3 million records per day to extract real-world vehicle routes. The evaluation results show that our solution achieves much more balanced results, i.e., only increasing the average waiting time of vehicles, the measurement metric of intelligent traffic light control service, by 6.8% while reducing the weighted sum of air pollutant emission, measured for environment control service, by 12.1%, and the pedestrian waiting time, the measurement metric of pedestrian service, by 33.1%, compared to priority-based solution. |
Abstract:
Background: The field of dietary assessment has a long history, marked by both controversies and advances. Emerging technologies may be a potential solution to address the limitations of self-report dietary assessment methods. The Monitoring and Modeling Family Eating Dynamics (M2FED) study uses wrist-worn smartwatches to automatically detect real-time eating activity in the field. The ecological momentary assessment (EMA) methodology was also used to confirm whether eating occurred (ie, ground truth) and to measure other contextual information, including positive and negative affect, hunger, satiety, mindful eating, and social context. Objective: This study aims to report on participant compliance (feasibility) to the 2 distinct EMA protocols of the M2FED study (hourly time-triggered and eating event-triggered assessments) and on the performance (validity) of the smartwatch algorithm in automatically detecting eating events in a family-based study. Methods: In all, 20 families (58 participants) participated in the 2-week, observational, M2FED study. All participants wore a smartwatch on their dominant hand and responded to time-triggered and eating event-triggered mobile questionnaires via EMA while at home. Compliance to EMA was calculated overall, for hourly time-triggered mobile questionnaires, and for eating event-triggered mobile questionnaires. The predictors of compliance were determined using a logistic regression model. The number of true and false positive eating events was calculated, as well as the precision of the smartwatch algorithm. The Mann-Whitney U test, Kruskal-Wallis test, and Spearman rank correlation were used to determine whether there were differences in the detection of eating events by participant age, gender, family role, and height. Results: The overall compliance rate across the 20 deployments was 89.26% (3723/4171) for all EMAs, 89.7% (3328/3710) for time-triggered EMAs, and 85.7% (395/461) for eating event-triggered EMAs. Time of day (afternoon odds ratio [OR] 0.60, 95% CI 0.42-0.85; evening OR 0.53, 95% CI 0.38-0.74) and whether other family members had also answered an EMA (OR 2.07, 95% CI 1.66-2.58) were significant predictors of compliance to time-triggered EMAs. Weekend status (OR 2.40, 95% CI 1.25-4.91) and deployment day (OR 0.92, 95% CI 0.86-0.97) were significant predictors of compliance to eating event-triggered EMAs. Participants confirmed that 76.5% (302/395) of the detected events were true eating events (ie, true positives), and the precision was 0.77. The proportion of correctly detected eating events did not significantly differ by participant age, gender, family role, or height (P>.05). Conclusions: This study demonstrates that EMA is a feasible tool to collect ground-truth eating activity and thus evaluate the performance of wearable sensors in the field. The combination of a wrist-worn smartwatch to automatically detect eating and a mobile device to capture ground-truth eating activity offers key advantages for the user and makes mobile health technologies more accessible to nonengineering behavioral researchers. |
Abstract:
An increasing number of monitoring systems have been developed in smart cities to ensure that real-time operations of a city satisfy safety and performance requirements. However, many existing city requirements are written in English with missing, inaccurate, or ambiguous information. There is a high demand for assisting city policy makers in converting human-specified requirements to machine-understandable formal specifications for monitoring systems. To tackle this limitation, we build CitySpec, the first intelligent assistant system for requirement specification in smart cities. To create CitySpec, we first collect over 1,500 real-world city requirements across different domains from over 100 cities and extract city-specific knowledge to generate a dataset of city vocabulary with 3,061 words. We also build a translation model and enhance it through requirement synthesis and develop a novel online learning framework with validation under uncertainty. The evaluation results on real-world city requirements show that CitySpec increases the sentence-level accuracy of requirement specification from 59.02% to 86.64%, and has strong adaptability to a new city and a new domain (e.g., F1 score for requirements in Seattle increases from 77.6% to 93.75% with online learning). |
Abstract:
Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In addition to responding to frequent incidents each day (about 240 million emergency medical services calls and over 5 million road accidents in the US each year), these systems also support response during natural hazards. Recently, there has been a consistent interest in building decision support and optimization tools that can help emergency responders provide more efficient and effective response. This includes a number of principled subsystems that implement early incident detection, incident likelihood forecasting and strategic resource allocation and dispatch policies. In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with our community partners. |
Abstract:
As more and more monitoring systems have been deployed to smart cities, there comes a higher demand for converting new human-specified requirements to machineunderstandable formal specifications automatically. However, these human-specific requirements are often written in English and bring missing, inaccurate, or ambiguous information. In this paper, we present CitySpec [1], an intelligent assistant system for requirement specification in smart cities. CitySpec not only helps overcome the language differences brought by English requirements and formal specifications, but also offers solutions to those missing, inaccurate, or ambiguous information. The goal of this paper is to demonstrate how CitySpec works. Specifically, we present three demos: (1) interactive completion of requirements in CitySpec; (2) human-in-the-loop correction while CitySepc encounters exceptions; (3) online learning in CitySpec. |
2021
Abstract:
Predictive monitoring¡ªmaking predictions about future states and monitoring if the predicted states satisfy requirements¡ªoffers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named Signal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on whether all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world CPS datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines. |
Abstract:
How can the advantages of formal methods be brought to emerging smart cities? We discuss several core challenges and our recent efforts as the first step toward developing novel formal methods to ensure safety and performance in smart cities. |
Abstract:
With the development of the Internet of Things, millions of sensors are being deployed in cities to collect real-time data. This leads to a need for checking city states against city requirements at runtime. In this paper, we develop a novel spatial-temporal specification-based monitoring system for smart cities. We first describe a study of over 1,000 smart city requirements, some of which cannot be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop SaSTL -- a novel Spatial Aggregation Signal Temporal Logic -- for the efficient runtime monitoring of safety and performance requirements in smart cities. We develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We define Boolean and \newcontent{quantitative semantics}~for SaSTL in support of the analysis of city performance across different periods and locations. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). Additionally, we build a SaSTL-based monitoring tool to support decision making of different stakeholders to specify and runtime monitor their requirements in smart cities. We evaluate our SaSTL monitor by applying it to three case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one study). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logic, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor improves the safety and performance of smart cities via simulated experiments. |
Abstract:
COVID-19 has caused many disruptions in conducting smart health research. Both in-lab sessions and in-home deployments had to be delayed or canceled because in-person meetings were no longer allowed. Our research project on ¡°in-home monitoring with personalized recommendations to reduce the stress of caregivers of Alzheimer¡¯s patients¡± was affected. To enable continued research without any person-to-person contact, we created an out-of-the-box deployment solution. The solution is multifaceted and deals with everything from technical adjustments, deployment documentation, EMA additions, additional monitoring software, use of videos, Zoom and TeamViewer, budget changes, new logistics, and changes to IRBs. This article briefly describes the purpose and design of the original system and then articulates the necessitated changes. We also provide lessons learned and an initial evaluation of the effectiveness of the solutions after the changes. The evaluation surveys the opinions of seven people that assembled, initialized, and deployed our system in home environments. We believe that the various solutions we developed can be applied to other similar projects, and will be helpful to new projects even when personal contact returns. |
Abstract:
Wearable devices, such as smartwatches and head-mounted devices (HMD), demand new input devices for a natural, subtle, and easy-to-use way to input commands and text. In this paper, we propose and investigate ViFin, a new technique for input commands and text entry, which harness finger movement induced vibration to track continuous micro finger-level writing with a commodity smartwatch. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing, works across different users, and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. We quantify our approach's accuracy through real-time system experiments in different arm positions, writing speeds, and smartwatch position displacements. Finally, a real-time writing system and two user studies on real-world tasks are implemented and assessed. |
Abstract:
Aim: The aim of this study is to develop a Smarthealth system of monitoring, modelling, and interactive recommendation solutions (for caregivers) for in-home dementia patient care that focuses on caregiver-patient relationships. Design: This descriptive study employs a single-group, non-randomized trial to examine functionality, effectiveness, feasibility, and acceptability of the novel Smarthealth system. Methods: Thirty persons with Alzheimer's Disease or related dementia and their family caregivers (N = 30 dyads) will receive and install Smarthealth technology in their home. There will be a 1-month observation phase for collecting baseline mood states and a 2-month implementation phase when caregivers will receive stress management techniques for each detected, negative mood state. Caregivers will report technique implementation and usefulness, sent via Ecological Momentary Assessment system to the study-provided smartphone. Caregivers will provide daily, self-reported mood and health ratings. Instruments measuring caregiver assessment of disruptive behaviours and their effect on caregivers; caregiver depressive symptoms, anxiety and stress; caregiver strain; and family functioning will be completed at baseline and 3 months. The study received funding in 2018 and ethics board approval in 2019. Discussion: This study will develop and test novel in-home technology to improve family caregiving relationships. Results from this study will help develop and improve the Smarthealth recommendation system and determine its usefulness, feasibility, and acceptability for persons with dementia and their family caregiver. Impact: The Smarthealth technology discussed will provide in-home stress reduction resources at a time when older adults may be experiencing increasingly high rates of isolation and anxiety and caregiver dyads may be experiencing high levels of relationship strain. Trial registration: This study was registered with Clinical Trials.gov (Identifier NCT04536701). |
Abstract:
Healthcare cognitive assistants (HCAs) are intelligent systems or agents that interact with users in a context-aware and adaptive manner to improve their health outcomes by augmenting their cognitive abilities or complementing a cognitive impairment. They assist a wide variety of users ranging from patients to their healthcare providers (e.g., general practitioner, specialist, surgeon) in several situations (e.g., remote patient monitoring, emergency response, robotic surgery). While HCAs are critical to ensure personalized, scalable, and efficient healthcare, there exists a knowledge gap in finding the emerging trends, key challenges, design guidelines, and state-of-the-art technologies suitable for developing HCAs. This survey aims to bridge this gap for researchers from multiple domains, including but not limited to cyber-physical systems, artificial intelligence, human-computer interaction, robotics, and smart health. It provides a comprehensive definition of HCAs and outlines a novel, practical categorization of existing HCAs according to their target user role and the underlying application goals. This survey summarizes and assorts existing HCAs based on their characteristic features (i.e., interactive, context-aware, and adaptive) and enabling technological aspects (i.e., sensing, actuation, control, and computation). Finally, it identifies critical research questions and design recommendations to accelerate the development of the next generation of cognitive assistants for healthcare. |
Abstract:
Sensing is becoming more and more pervasive. New sensing modalities are enabling the collection of data not previously available. Artificial Intelligence (AI) and cognitive assistance technologies are improving rapidly. Cyber Physical Systems (CPS) are making significant progress in utilizing AI and Machine Learning (ML). This confluence of technologies is giving rise to the potential to achieve the vision of ambient intelligence. This paper describes some of the main challenges and research directions for ambient intelligence from a CPS perspective. |
Abstract:
As various smart services are increasingly deployed in modern cities, many unexpected conflicts arise due to various physical world couplings. Existing solutions for conflict resolution often rely on centralized control to enforce predetermined and fixed priorities of different services, which is challenging due to the inconsistent and private objectives of the services. Also, the centralized solutions miss opportunities to more effectively resolve conflicts according to their spatiotemporal locality of the conflicts. To address this issue, we design a decentralized negotiation and conflict resolution framework named DeResolver, which allows services to resolve conflicts by communicating and negotiating with each other to reach a Pareto-optimal agreement autonomously and efficiently. Our design features a two-level semi-supervised learning-based algorithm to predict acceptable proposals and their rankings of each opponent through the negotiation. Our design is evaluated with a smart city case study of three services: intelligent traffic light control, pedestrian service, and environmental control. In this case study, a data-driven evaluation is conducted using a large data set consisting of the GPS locations of 246 surveillance cameras and an automatic traffic monitoring system with more than 3 million records per day to extract real-world vehicle routes. The evaluation results show that our solution achieves much more balanced results, i.e., only increasing the average waiting time of vehicles, the measurement metric of intelligent traffic light control service, by 6.8% while reducing the weighted sum of air pollutant emission, measured for environment control service, by 12.1%, and the pedestrian waiting time, the measurement metric of pedestrian service, by 33.1%, compared to priority-based solution. |
Abstract:
Obesity is a risk factor for many health issues, including heart disease, diabetes, osteoarthritis, and certain cancers. One of the primary behavioral causes, dietary intake, has proven particularly challenging to measure and track. Current behavioral science suggests that family eating dynamics (FED) have high potential to impact child and parent dietary intake, and ultimately the risk of obesity. Monitoring FED requires information about when and where eating events are occurring, the presence or absence of family members during eating events, and some person-level states such as stress, mood, and hunger. To date, there exists no system for real-time monitoring of FED. This paper presents MFED, the first of its kind of system for monitoring FED in the wild in real-time. Smart wearables and Bluetooth beacons are used to monitor and detect eating activities and the location of the users at home. A smartphone is used for the Ecological Momentary Assessment (EMA) of a number of behaviors, states, and situations. While the system itself is novel, we also present a novel and efficient algorithm for detecting eating events from wrist-worn accelerometer data. The algorithm improves eating gesture detection F1-score by 19% with less than 20% computation compared to the state-of-the-art methods. To date, the MFED system has been deployed in 20 homes with a total of 74 participants, and responses from 4750 EMA surveys have been collected. This paper describes the system components, reports on the eating detection results from the deployments, proposes two techniques for improving ground truth collection after the system is deployed, and provides an overview of the FED data, generated from the multi-component system, that can be used to model and more comprehensively understand insights into the monitoring of family eating dynamics. |
Abstract:
Smart city simulators are useful tools for simulating various scenarios with the impact of human/non-human factors, and evaluating efficiency and influence of services. Although current simulation platforms have made achievements in aspects like energy consumption modeling and urban planning, there are still gaps in integrating various domains together and involving the randomness of reality at the same time. Thus, we propose an open-source smart city simulator to provide more comprehensive features to satisfy diverse demands in scenario construction, information exchange, and urban services with more similarity to the real world, which can suit the needs of both city designers in plan evaluation and researchers in multi-domain data generation for data-driven models. Functions of the proposed simulator including three main components: (1) basic structure layers for constructing networks with map data, (2) event layers for receiving and exchanging information of (emergency) events, and (3) service layers for providing urban services in transportation, emission, and energy domains and responding to events. To better simulate unexpected circumstances in the real world, the randomness of reality is embodied in the uncertainty of sensing, devices, and events. Finally, two cases are designed in the demo to present functions of different layers and data generation with uncertainty. |
2020
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Abstract:
Recurrent Neural Networks (RNNs) have made great achievements for sequential prediction tasks. In practice, the target sequence often follows certain model properties or patterns (e.g., reasonable ranges, consecutive changes, resource constraint, temporal correlations between multiple variables, existence, unusual cases, etc.). However, RNNs cannot guarantee their learned distributions satisfy these model properties. It is even more challenging for predicting large-scale and complex Cyber-Physical Systems. Failure to produce outcomes that meet these model properties will result in inaccurate and even meaningless results. In this paper, we develop a new temporal logic-based learning framework, STLnet, which guides the RNN learning process with auxiliary knowledge of model properties, and produces a more robust model for improved future predictions. Our framework can be applied to general sequential deep learning models, and trained in an end-to-end manner with back-propagation. We evaluate the performance of STLnet using large-scale real-world city data. The experimental results show STLnet not only improves the accuracy of predictions, but importantly also guarantees the satisfaction of model properties and increases the robustness of RNNs. |
Abstract:
We present SaSTL---a novel Spatial Aggregation Signal Temporal Logic---for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments. |
Abstract:
In order to prevent safety violations, predictive monitoring with uncertainty is crucial for deep learning-enabled services in smart cities. We develop a novel predictive monitoring system for smart city applications, which consists of an RNN-based predictor with uncertainty estimation and a new specification language, named Signal Temporal Logic with Uncertainty. The solution first predicts a sequence of distributions representing city's future states with uncertainty estimation and then checks the predicted results against STL-U specified safety and performance requirements. The system supports decision making by providing a quantitative satisfaction degree with confidence guarantees. We receive promising results from evaluations on two large-scale city datasets, and on a case study on real-time predictive monitoring in a simulated smart city. |
Abstract:
Family caregivers often report increased anxiety and depression. In order to improve the interactions between in-home patients and caregivers, and reduce strain on caregivers, we build a monitoring, modeling, and interactive recommendation system for caregivers for in-home dementia patient care. The system includes monitoring for mood by speech, building classifiers that work in realistic home settings, and supporting an adaptive recommendation system to reduce stress of the caregiver. This demo shows how our system supports caregivers in practice through several scenarios. |
Abstract:
Input is a significant problem for wearable devices, particularly for head-mounted virtual and augmented reality systems. Contemporary AR/VR systems use in-air gestures or handheld controllers for interactivity. However, mid-air handwriting provides a natural, subtle, and easy-to-use way to input commands and text. In this demo, we propose and investigate ViFin, a new technique for input commands and text entry which tracks continuous micro finger-level writing with a commodity smartwatch through vibrations. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing and works across different users and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. Finally, a real-time writing system with two specific applications using AR smartglasses are implemented. |
2019
Abstract:
Cities are deploying tens of thousands of sensors and actuators and developing a large array of smart services. The smart services use sophisticated models and decision-making policies supported by Cyber Physical Systems and Internet of Things technologies. The increasing number of sensors collects a large amount of city data across multiple domains. The collected data have great potential value, but has not yet been fully exploited. This survey focuses on the domains of transportation, environment, emergency and public safety, energy, and social sensing. This article carefully reviews both the data sets being collected across 14 smart cities and the state-of-the-art work in modeling and decision making methodologies. The article also points out the characteristics, challenges faced today, and those challenges that will be exacerbated in the future. Key data issues addressed include heterogeneity, interdisciplinary, integrity, completeness, real-timeliness, and interdependencies. Key decision making issues include safety and service conflicts, security, uncertainty, humans in the loop, and privacy. |
Abstract:
Family relationships influence eating behavior and health outcomes (e.g., obesity). Because eating is often habitual (i.e., automatically driven by external cues), unconscious behavioral mimicry may be a key interpersonal influence mechanism for eating within families. This pilot study extends existing literature on eating mimicry by examining whether multiple family members mimicked each other's bites during natural meals. Thirty-three participants from 10 families were videotaped while eating an unstructured family meal in a kitchen lab setting. Videotapes were coded for participants' bite occurrences and times. We tested whether the likelihood of a participant taking a bite increased when s/he was externally cued by a family eating partner who had recently taken a bite (i.e., bite mimicry). A paired-sample t-test indicated that participants had a significantly faster eating rate within the 5 s following a bite by their eating partner, compared to their bite rate at other times (t = 7.32, p < .0001). Nonparametric permutation testing identified five of 78 dyads in which there was significant evidence of eating mimicry; and 19 of 78 dyads that had p values < .1. This pilot study provides preliminary evidence that suggests eating mimicry may occur among a subset of family members, and that there may be types of family ties more prone to this type of interpersonal influence during meals. |
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2018
Abstract:
Resolution of conflicts across services in smart cities is an important yet challenging problem. We present CityResolver - a decision support system for conflict resolution in smart cities. CityResolver uses an Integer Linear Programming based method to generate a small set of resolution options, and a Signal Temporal Logic based verification approach to compute these resolution options' impact on city performance. The trade-offs between resolution options are shown in a dashboard to support decision makers in selecting the best resolution. We demonstrate the effectiveness of CityResolver by comparing the performance with two baselines: a smart city without conflict resolution, and CityGuard which uses a priority rule-based conflict resolution. Experimental results show that CityResolver can reduce the number of requirement violations and improve the city performance significantly. |
2017
Abstract:
Conflicting health information is a primary barrier of self-management of chronic diseases. Increasing number of people now rely on mobile health apps and online health websites to meet their information needs and often receive conflicting health advice from these sources. This problem is more prevalent and severe in the setting of multi-morbidities. In addition, often medical information can be conflicting with regular activity patterns of an individual. In this work, we formulate the problem of finding conflicts in heterogeneous health applications including health websites, health apps, online drug usage guidelines, and daily activity logging applications. We develop a comprehensive taxonomy of conflicts based on the semantics of textual health advice and activities of daily living. Finding conflicts in health applications poses its own unique lexical and semantic challenges. These include large structural variation between pairs of textual advice, finding conceptual overlap between pairs of advice, inferring the semantics of an advice (i.e., what to do, why and how) and activities, and aligning activities suggested in advice with the activities of daily living based on their underlying dependencies and polarity. Hence, we develop Preclude2, a novel semantic rule-based solution to detect conflicts in activities and health advice derived from heterogeneous sources. Preclude2 utilizes linguistic rules and external knowledge bases to infer advice. In addition, Preclude2 considers personalization and context-awareness while detecting conflicts. We evaluate Preclude2 using 1156 real advice statements covering 8 important health topics, 90 online drug usage guidelines, 1124 online disease specific health advice covering 34 chronic diseases, and 2 activity datasets. The evaluation is personalized based on 34 real prescriptions. Preclude2 detects direct, conditional, sub-typical, quantitative, and temporal conflicts from 2129 advice statements with 0.91, 0.83, 0.98, 0.85 and 0.98 recall, respectively. Overall, it results in 0.88 recall for detecting inter advice conflicts and 0.89 recall for detecting activity¨Cadvice conflicts. We also demonstrate the effects of personalization and context-awareness in conflict detection from heterogeneous health applications. |
Abstract:
Research in the area of internet-of-things, cyberphysical-systems, and smart health often employ sensor systems at residences for continuous monitoring. Such research-oriented residential monitoring systems (RRMSs) usually face two major challenges, long-term reliable operation management and validation of system functionality with minimal human effort. Targeting these two challenges, this paper describes a monitor of monitoring systems with ground-truth validation capabilities, M 2 G. It consists of two subsystems, the Monitor 2 system and the Ground-truth validation system. The Monitor 2 system encapsulates a flexible set of general-purpose components to monitor the operation and connectivity of heterogeneous sensor devices (e.g. smart watches, smart phones, microphones, beacons, etc.), a local base-station, as well as a cloud server. It provides a user-friendly interface and supports different types of RRMSs in various contexts. The system also features a ground truth validation system to support obtaining ground truth in the field. Additionally, customized alerts can be sent to remote administrators and other personnel to report any dysfunction or inaccuracy of the system in real time. M 2 G is applied to three very different case studies: the M2FED system which monitors family eating dynamics [1], an in-home wireless sensing system for monitoring nighttime agitation [2], and the BESI system which monitors behavioral and environmental parameters to predict health events and to provide interventions [3]. The results indicate that M 2 G is a comprehensive system that (i) requires small cost in time and effort to adapt to an existing RRMS, (ii) provides reliable data collection and reduction in data loss by detecting faults in real-time, and (iii) provides a convenient and timely ground truth validation facility. |
Abstract:
Nowadays, increasing number of smart services are being developed and deployed in cities around the world. IoT platforms have emerged to integrate smart city services and city resources, and thus improve city performance in the domains of transportation, emergency, environment, public safety, etc. Despite the increasing intelligence of smart services and the sophistication of platforms, the safety issues in smart cities are not addressed adequately, especially the safety issues arising from the integration of smart services. Therefore, CityGuard, a safety-aware watchdog architecture is developed. To the best of our knowledge, it is the first architecture that detects and resolves conflicts among actions of different services considering both safety and performance requirements. Prior to developing CityGuard, safety and performance requirements and a spectrum of conflicts are specified. Sophisticated models are used to analyze secondary effects, and detect device and environmental conflicts. A simulation based on New York City is used for the evaluation. The results show that CityGuard (i) identifies unsafe actions and thus helps to prevent the city from safety hazards, (ii) detects and resolves two major types of conflicts, i.e., device and environmental conflicts, and (iii) improves the overall city performance. |
Abstract:
With the rapid digitalization of the health sector, people often turn to mobile apps and online health websites for health advice. Health advice generated from different sources can be conflicting as they address different aspects of health (e.g., weight loss, diet, disease) or as they are unaware of the context of a user (e.g., age, gender, physiological condition). Conflicts can occur due to lexical features, (such as, negation, antonyms, or numerical mismatch) or can be conditioned upon time and/or physiological status. We formulate the problem of finding conflicting health advice and develop a comprehensive taxonomy of conflicts. While a similar research area in the natural language processing domain explores the problem of textual contradiction identification, finding conflicts in health advice poses its own unique lexical and semantic challenges. These include large structural variation between text and hypothesis pairs, finding conceptual overlap between pairs of advice, and inference of the semantics of an advice (i.e., what to do, why and how). Hence, we develop Preclude, a novel semantic rule-based solution to detect conflicting health advice derived from heterogeneous sources utilizing linguistic rules and external knowledge bases. As our solution is interpretable and comprehensive, it can guide users towards conflict resolution too. We evaluate Preclude using 1156 real advice statements covering 8 important health topics that are collected from smart phone health apps and popular health websites. Preclude results in 90% accuracy and outperforms the accuracy and F1 score of the baseline approach by about 1.5 times and 3 times, respectively. |
Abstract:
With the increasing number of smart services implemented in smart cities, it is important yet challenging to dynamically detect service conflicts with respect to safety and performance requirements. In this paper, we propose a framework for monitoring the operation of smart cities and services at runtime. We formalize a set of typical safety and performance requirements from different domains in smart cities (e.g., transportation, emergency, and environment) using Signal Temporal Logic. We present a case study based on a smart city simulator, in which actions of smart services and their predicted effects on city states are converted into signal traces over time and monitored continuously using formal specifications. The experimental results demonstrate the feasibility of using runtime monitoring to detect various conflicts of smart services. |
Abstract:
Despite the increasing intelligence of smart services and sophistication of IoT platforms, the safety issues in smart cities are not addressed adequately, especially the safety issues arising from the integration of smart services. Therefore, in this demo abstract, we present CityGuard, a safety-aware watchdog architecture to detect conflicts among actions of heterogeneous services considering both safety and performance requirements. This demo simulates parts of New York City to depict how CityGuard identifies unsafe actions and thus helps to prevent the city from safety hazards, detects two major types of conflicts, i.e., device and environmental conflicts, and improves the overall city performance in terms of multiple performance metrics. This demo complements the full paper on CityGuard that appears in this conference. |
Abstract:
Textual health advice generated from different online sources (e.g., health apps and websites) can be conflicting. Conflicts can occur due to lexical features, (such as, negation, antonyms, or numerical mismatch) or can be conditioned upon time and/or physiological status. Detecting conflicts from textual health advice poses several challenges, including, large structural variation between text and hypothesis pairs, finding conceptual overlap between pairs of advice, and inference of the semantics of an advice (i.e., what to do, why, and how). In this demonstration, we present a semantic rule-based system to detect different types of conflicts in online textual health advice statements in a context-aware and interpretable manner. |
2016
Abstract:
The populations of large cities around the world are growing rapidly. Cities are beginning to address this problem by implementing significant sensing and actuation infrastructure and building services on this infrastructure. However, as the density of sensing and actuation increases and as the complexities of services grow there is an increasing potential for conflicts across Smart City services. These conflicts can cause unsafe situations and disrupt the benefits that the services were originally intended to provide. Although some of the conflicts can be detected and avoided during designing the services, many can still occur unpredictably during runtime. This paper carefully defines and enumerates the main issues regarding the detection and resolution of runtime conflicts in smart cities. In particular, it focuses on conflicts that arise across services. This issue is becoming more and more important as Smart City designs attempt to integrate services from different domains (transportation, energy, public safety, emergency, medical, and many others). Research challenges are identified and then addressed that deal with uncertainty, dynamism, real-time, mobility and spatio-temporal availability, duration and scale of effect, efficiency, and ownership. A watchdog architecture is also described that oversees the services operating in a Smart City. This watchdog solution detects and resolves conflicts, it learns and adapts, and it provides additional inputs to decision making aspects of services. Using data from a Smart City dataset, an emulated set of services and activities using those services are created to perform a conflict analysis. A second analysis hypothesizes 41 future services across 5 domains. Both of these evaluations demonstrate the high probability of conflicts in smart cities of the future. |
2015
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2011
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