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mORAL: An mHealth Model for Inferring Oral Hygiene Behaviors in-the-wild Using Wrist-worn Inertial Sensors
We address the open problem of reliably detecting oral health behaviors passively from wrist-worn inertial sensors. We present our model named mORAL (pronounced em oral) for detecting brushing and flossing behaviors, without the use of instrumented ...
Deep Multi-Task Learning Based Urban Air Quality Index Modelling
Obtaining comprehensive air quality information can help protect human health from air pollution. Existing spatially fine-grained estimation methods and forecasting methods have the following problems: 1) Only a part of data related to air quality is ...
Your Table Can Be an Input Panel: Acoustic-based Device-Free Interaction Recognition
This paper explores the possibility of extending the input and interactions beyond the small screen of the mobile device onto ad hoc adjacent surfaces, e.g., a wooden tabletop with acoustic signals. While the existing finger tracking approaches employ ...
CAP: Context-aware App Usage Prediction with Heterogeneous Graph Embedding
Context-aware mobile application (App) usage prediction benefits a variety of applications such as precise bandwidth allocation, App launch acceleration, etc. Prior works have explored this topic through individual data profiles and contextual ...
Understanding Parents' Perspectives on Mealtime Technology
For young children, family meals are an enjoyable and developmentally useful part of daily life. Although prior work has shown that ubiquitous computing solutions can enhance children's eating habits and mealtime experiences in valuable ways, other work ...
ICT: In-field Calibration Transfer for Air Quality Sensor Deployments
Recent years have witnessed a growing interest in urban air pollution monitoring, where hundreds of low-cost air quality sensors are deployed city-wide. To guarantee data accuracy and consistency, these sensors need periodic calibration after ...
Identifying and Planning for Individualized Change: Patient-Provider Collaboration Using Lightweight Food Diaries in Healthy Eating and Irritable Bowel Syndrome
Identifying and planning strategies that support a healthy lifestyle or manage a chronic disease often require patient-provider collaboration. For example, people with healthy eating goals often share everyday food, exercise, or sleep data with health ...
Modeling Biobehavioral Rhythms with Passive Sensing in the Wild: A Case Study to Predict Readmission Risk after Pancreatic Surgery
Biobehavioral rhythms are associated with numerous health and life outcomes. We study the feasibility of detecting rhythms in data that is passively collected from Fitbit devices and using the obtained model parameters to predict readmission risk after ...
Evaluating the Impact of Technology Assisted Hotspot Policing on Situational Awareness and Task-Load
Everyday field work of a police officer requires the perception, filtering and understanding of large amounts of information in highly dynamic situations. This presents opportunities for ICT to alleviate strain on officers by providing adequate ...
Perils of Zero-Interaction Security in the Internet of Things
The Internet of Things (IoT) demands authentication systems which can provide both security and usability. Recent research utilizes the rich sensing capabilities of smart devices to build security schemes operating without human interaction, such as ...
Privacy-preserving Cross-domain Location Recommendation
Cross-domain recommendation is a typical solution for data sparsity and cold start issue in the field of location recommendation. Specifically, data of an auxiliary domain is leveraged to improve the recommendation of the target domain. There is a ...
Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers
Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant ...
Using Unobtrusive Wearable Sensors to Measure the Physiological Synchrony Between Presenters and Audience Members
The widespread adoption of mobile and wearable devices enables new approaches for the unobtrusive and continuous monitoring of humans' behavior, physiological state, interactions and more. Within this line of research, we focus on the physiological ...
Beyond Control: Enabling Smart Thermostats for Leakage Detection
Smart thermostats, with multiple sensory abilities, are becoming pervasive and ubiquitous, in both residential and commercial buildings. By analyzing occupants' behavior, adjusting set temperature automatically, and adapting to temporal and spatial ...
A Deep Reinforcement Learning-Enabled Dynamic Redeployment System for Mobile Ambulances
Protecting citizens' lives from emergent accidents (e.g. traffic accidents) and diseases (e.g. heart attack) is of vital importance in urban computing. Every day many people are caught in emergent accidents or diseases and thus need ambulances to ...
GoalKeeper: Exploring Interaction Lockout Mechanisms for Regulating Smartphone Use
Many people often experience difficulties in achieving behavioral goals related to smartphone use. Most of prior studies approached this problem with various behavior change strategies such as self-reflection and social support. However, little is known ...
Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos
Over the years, activity sensing and recognition has been shown to play a key enabling role in a wide range of applications, from sustainability and human-computer interaction to health care. While many recognition tasks have traditionally employed ...
Animo: Sharing Biosignals on a Smartwatch for Lightweight Social Connection
We present Animo, a smartwatch app that enables people to share and view each other's biosignals. We designed and engineered Animo to explore new ground for smartwatch-based biosignals social computing systems: identifying opportunities where these ...
Reconstructing Human Joint Motion with Computational Fabrics
Accurate and continuous monitoring of joint rotational motion is crucial for a wide range of applications such as physical rehabilitation [6, 85] and motion training [22, 54, 68]. Existing motion capture systems, however, either need instrumentation of ...
Understanding Cycling Trip Purpose and Route Choice Using GPS Traces and Open Data
Many mobile applications such as Strava or Mapmyride allow cyclists to collect detailed GPS traces of their trips for health or route sharing purposes. However, cycling GPS traces also have a lot of potential from an urban planning perspective. In this ...
Using Built-In Sensors to Predict and Utilize User Satisfaction for CPU Settings on Smartphones
Understanding user experience/satisfaction with mobile systems in order to manage computational resources has become a popular approach in recent years. One of the key issues in this area is to gauge user satisfaction. In this paper, we propose and ...
Clinical Data in Context: Towards Sensemaking Tools for Interpreting Personal Health Data
Clinical data augmented with contextual data can help patients with chronic conditions make sense of their disease. However, existing tools do not support interpretation of multiple data streams. To better understand how individuals make sense of ...
W!NCE: Unobtrusive Sensing of Upper Facial Action Units with EOG-based Eyewear
The ability to unobtrusively and continuously monitor one's facial expressions has implications for a variety of application domains ranging from affective computing to health-care and the entertainment industry. The standard Facial Action Coding System ...
Measuring the Effects of Stress on Mobile Interaction
- Zhanna Sarsenbayeva,
- Niels van Berkel,
- Danula Hettiachchi,
- Weiwei Jiang,
- Tilman Dingler,
- Eduardo Velloso,
- Vassilis Kostakos,
- Jorge Goncalves
Research shows that environmental factors such as ambient noise and cold ambience can render users situationally impaired, adversely affecting interaction with mobile devices. However, an internal factor which is known to negatively impact cognitive ...
Batch Localization Based on OFDMA Backscatter
OFDMA Wi-Fi backscatter can significantly improve the communication efficiency and meanwhile maintain ultra-low power consumption; however, the ground-up reworking on the core mechanism of traditional Wi-Fi system revolutionizes the basis of many ...
From Fingerprint to Footprint: Cold-start Location Recommendation by Learning User Interest from App Data
With increasing diversity of user interest and preference, personalized location recommendation is essential and beneficial to our daily life. To achieve this, the most critical challenge is the cold-start recommendation problem, for we cannot learn ...
Modeling Spatio-Temporal App Usage for a Large User Population
With the wide adoption of mobile devices, it becomes increasingly important to understand how users use mobile apps. Knowing when and where certain apps are used is instrumental for app developers to improve app usability and for Internet service ...
LeakDoctor: Toward Automatically Diagnosing Privacy Leaks in Mobile Applications
With the enormous popularity of smartphones, millions of mobile apps are developed to provide rich functionalities for users by accessing certain personal data, leading to great privacy concerns. To address this problem, many approaches have been ...
RF-Focus: Computer Vision-assisted Region-of-interest RFID Tag Recognition and Localization in Multipath-prevalent Environments
Capturing RFID tags in the region of interest (ROI) is challenging. Many issues, such as multipath interference, frequency-dependent hardware characteristics and phase periodicity, make RF phase difficult to accurately indicate the tag-to-antenna ...
Revealing Urban Dynamics by Learning Online and Offline Behaviours Together
Urban problems and diseases accompanied by the pace of urbanization have drawn attention to the importance of understanding urban dynamics, while a deep and comprehensive understanding is challenging due to our diversified lifestyles in the modern city. ...