Best Papers
Mohit Sethi, Elena Oat, Mario Di Francesco, Tuomas Aura
The layouts of the buildings we live in shape our everyday lives. In office environments, building spaces affect employees' communication, which is crucial for productivity and innovation. However, accurate measurement of how spatial layouts affect interactions is a major challenge and traditional techniques may not give an objective view. We measure the impact of building spaces on social interactions using wearable sensing devices. We study a single organization that moved between two different buildings, affording a unique opportunity to examine how space alone can affect interactions. The analysis is based on two large scale deployments of wireless sensing technologies: short-range, lightweight RFID tags capable of detecting face-to-face interactions. We analyze the traces to study the impact of the building change on social behavior, which represents a first example of using ubiquitous sensing technology to study how the physical design of two workplaces combines with organizational structure to shape contact patterns.
Chloe Brown, Christos Efstratiou, Ilias Leontiadis, Daniele Quercia, Cecilia Mascolo, James Scott, Peter Key
In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls made/received, applications, e.g. time spent on different apps, location and media, e.g. photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e. meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of mobile services and future markets of personal data.
Jacopo Staiano, Nuria Oliver, Bruno Lepri, Rodrigo de Oliveira, Michele Caraviello, Nicu Sebe
We investigated how household deployment of Internet-connected locks and security cameras could impact teenagers' privacy. In interviews with 13 teenagers and 11 parents, we investigated reactions to audit logs of family members' comings and goings. All parents wanted audit logs with photographs, whereas most teenagers preferred text-only logs or no logs at all. We unpack these attitudes by examining participants' parenting philosophies, concerns, and current monitoring practices. In a follow-up online study, 19 parents configured an Internet-connected lock and camera system they thought might be deployed in their home. All 19 participants chose to monitor their children either through unrestricted access to logs or through real-time notifications of access. We discuss directions for auditing interfaces that could improve home security without impacting privacy.
Blase Ur, Jaeyeon Jung, Stuart Schechter
Best Paper Nominations
Indoor object localization can enable many ubicomp applications, such as asset tracking and object-related activity recognition. Most location and tracking systems rely on either battery-powered devices which create cost and maintenance issues or cameras which have accuracy and privacy issues. This paper introduces a system that is able to detect the 3D position and motion of a battery-free RFID tag embedded with an ultrasound detector and an accelerometer. Combining tags' acceleration with location improves the system's power management and supports activity recognition. We characterize the system's localization performance in open space as well as implement it in a smart wet lab application. The system is used to track real-time location and motion of the tags in the wet lab as well as recognize pouring actions performed on the objects to which the tag is attached. The median localization accuracy is $7.6cm$ -- $(3.1,5,1.9)cm$ for each $(x,y,z)$ axis -- with max update rates of 15 Sample/s using single RFID reader antenna.
Yi Zhao, Anthony LaMarca, Joshua R Smith
This paper assesses the potential of ride-sharing for reducing traffic in a city -- based on mobility data extracted from 3G Call Description Records (CDRs), for the cities of Madrid and Barcelona (BCN), and from OSNs, such as Twitter and Foursquare (FSQ), collected for the cities of New York (NY) and Los Angeles (LA). First, we analyze these data sets to understand mobility patterns, home and work locations, and social ties between users. Then, we develop an efficient algorithm for matching users with similar mobility patterns, considering a range of constraints, including social distance. The solution provides an upper bound to the potential decrease in the number of cars in a city that can be achieved by ridesharing. Our results indicate that this decrease can be as high as 31%, when users are willing to ride with friends of friends.
Blerim Cici, Athina Markopoulou, Enrique Frias-Martinez, Nikolaos Laoutaris
The battery life of mobile devices is one of their most important resources. Much of the literature focuses on accurately profiling the power consumption of device components or enabling application developers to develop energy-efficient applications through fine-grained power profiling. However,there is a lack of tools to enable users to extend battery life on demand. What can users do if they need their device to last for a specific duration in order to perform a specific task? To this extent, we developed BatteryExtender, a user-guided power management tool that enables the reconfiguration of the device’s resources based on the workload requirement, similar to the principle of creating virtual machines in the cloud. It predicts the battery life savings based on the new configuration, in addition to predicting the impact of running applications on the battery life. Through our experimental analysis, BatteryExtender decreased the energy consumption between 10.03% and 20.21%, and in rare cases by up to 72.83%. The accuracy rate ranged between 92.37% and 99.72%.
Grace Metri, Weisong Shi, Monica Brockmeyer, Abhishek Agrawal
Health sensing through smartphones has received considerable attention in recent years because of the devices’ ubiquity and promise to lower the barrier for tracking medical conditions. In this paper, we focus on using smartphones to monitor newborn jaundice, which manifests as a yellow discoloration of the skin. Although a degree of jaundice is common in healthy newborns, early detection of extreme jaundice is essential to prevent permanent brain damage or death. Current detection techniques, however, require clinical tests with blood samples or other specialized equipment. Consequently, newborns often depend on visual assessments of their skin color at home, which is known to be unreliable. To this end, we present BiliCam, a low-cost system that uses smartphone cameras to assess newborn jaundice. We evaluated BiliCam on 100 newborns, yielding a 0.85 rank order correlation with the gold standard blood test. We also discuss usability challenges and design solutions to make the system practical.
Lilian de Greef, Mayank Goel, Min Joon Seo, Eric C Larson, James W Stout, James A Taylor, Shwetak N Patel
Smart objects within instrumented environments offer an always available and intuitive way of interacting with a system. Connecting these objects to other objects in range, or even to smartphones and computers, enables substantially innovative interaction and sensing approaches. In this paper, we investigate the concept of Capacitive Near-Field Communication to enable ubiquitous interaction with everyday objects in a short-range spatial context. Our central contribution is a generic framework describing and evaluating the communication method in Ubiquitous Computing. We prove the relevance of our approach by an open-source implementation of a low-cost object tag and a transceiver offering a high-quality communication link at typical distances up to 15 cm. Moreover, we present three case studies considering tangible interaction for the visually impaired, natural interaction with everyday objects, and sleeping behavior analysis.
Tobias Grosse-Puppendahl, Sebastian Herber, Raphael Wimmer, Frank Englert, Sebastian Beck, Julian von Wilmsdorff, Reiner Wichert, Arjan Kuijper
Color blindness is a highly prevalent vision impairment that inhibits people's ability to understand colors. Although classified as a mild disability, color blindness has important effects on the daily activity of people, preventing them from performing their tasks in the most natural and effective ways. In order to address this issue we developed Chroma, a wearable augmented-reality system based on Google Glass that allows users to see a filtered image of the current scene in real-time. Chroma automatically adapts the scene-view based on the type of color blindness, and features dedicated algorithms for color saliency. Based on interviews with 23 people with color blindness we implemented four modes to help colorblind individuals distinguish colors they usually can't see. Although Glass still has important limitations, initial tests of Chroma in the lab show that colorblind individuals using Chroma can improve their color recognition in a variety of real-world activities. The deployment of Chroma on a wearable augmented-reality device makes it an effective digital aid with the potential to augment everyday activities, effectively providing access to different color dimensions for colorblind people.
Enrico Tanuwidjaja, Derek Huynh, Kirsten Koa, Calvin Nguyen, Churen Shao, Patrick Torbett, Colleen Emmenegger, Nadir Weibel
Situated displays can support behavior management for children with behavioral challenges. However, existing tools are often static, rarely engaging, and tend to focus only on individual behavior. In this work, we designed and deployed a situated display to support teamwork and cooperation in children with behavioral challenges. We evaluated this tool in two classrooms of a public school specializing in behavioral interventions with 28 children over four weeks. The results of this work demonstrate that situated displays focused on collective behavioral performance can support reflection on individual performance, improve behavior for students with behavioral challenges, as well as encourage teamwork and cooperative behavior in classrooms. These results also indicate a variety of issues to be considered when designing situated displays for these environments, including considerations for the representation of ambiguity and failure as well as the relationship between novelty and engagement.
Aleksandar Matic, Gillian R Hayes, Monica Tentori, Maryam Abdullah, Sabrina Schuck
Nicholas D Lane, Li Pengyu, Lin Zhou, Feng Zhao
Domestic microgeneration is the onsite generation of low- and zero-carbon heat and electricity by private households to meet their own needs. In this paper we explore how an everyday household routine ’ that of doing laundry ’ can be augmented by digital technologies to help households with photovoltaic solar energy generation to make better use of self-generated energy. This paper presents an 8-month in-the-wild study that involved 18 UK households in longitudinal energy data collection, prototype deployment and participatory data analysis. Through a series of technology interventions mixing energy feedback, proactive suggestions and direct control the study uncovered opportunities, potential rewards and barriers for families to shift energy consuming household activities and highlights how digital technology can act as mediator between household laundry routines and energy demand-shifting behaviors. Finally, the study provides insights into how a ’smart’ energy-aware washing machine shapes organization of domestic life and how people ’communicate’ with their washing machine.
Jacky Bourgeois, Janet van der Linden, Gerd Kortuem, Blaine Price, Christopher Rimmer
Pedestrians have difficulty noticing hybrid vehicles (HVs) and electrical vehicles (EVs) quietly approaching from behind. We propose a vehicle detection scheme using a smartphone carried by a pedestrian. A notification of a vehicle approaching can be delivered to wearable devices such as Google Glass. We exploit the high-frequency switching noise generated by the motor unit in HVs and EVs. Although people are less sensitive to these high-frequency ranges, these sounds are prominent even on a busy street, and it is possible for a smartphone to detect these signs . The ambient sound captured at 48 kHz is converted to a feature vector in the frequency domain. A J48 classifier implemented on a smartphone can determine whether an EV or HV is approaching. We have collected a large amount of vehicle data at various locations. The false-positive and false-negative rates of our detection scheme are 1.2% and 4.95%, respectively. The first alarm was detected as early as 11.6 s before the vehicle approached the observer. The scheme can also determine the vehicle speed and vehicle type.
Masaru Takagi, Kosuke Fujimoto, Yoshihiro Kawahara, Tohru Asami
Today people have the opportunity to opt-in to usage-based automotive insurances for reduced premiums by allowing companies to monitor their driving behavior. Several companies claim to measure only speed data to preserve privacy. With our elastic pathing algorithm, we show that drivers can be tracked by merely collecting their speed data and knowing their home location, which insurance companies do, with an accuracy that constitutes privacy intrusion. To demonstrate the algorithm's real-world applicability, we evaluated its performance with datasets from central New Jersey and Seattle, Washington, representing suburban and urban areas. Our algorithm predicted destinations with error within 250 meters for 14% traces and within 500 meters for 24% traces in the New Jersey dataset (254 traces). For the Seattle dataset (691 traces), we similarly predicted destinations with error within 250 and 500 meters for 13% and 26% of the traces respectively. Our work shows that these insurance schemes enable a substantial breach of privacy.
Xianyi Gao, Bernhard Firner, Shridatt Sugrim, Victor Kaiser-Pendergrast, Yulong Yang, Janne Lindqvist
Spatiotemporal gait analysis with body worn inertial sensors improves diagnosis in clinical practice. Most of the gait performance measures are affected by walking speed. However, it has not been investigated that how much information foot clearance parameters share with the key parameters of gait performance domains. Using shoe-worn inertial sensors and previously validated algorithm we measured spatiotemporal as well as clearance gait parameters in a cohort of able-bodied adults over the age of 65 (N=879). Principal components analysis showed that variability of foot clearance parameters contribute to the main variability in gait data. Moreover, only weak to moderate correlation of gait speed and stride length with some clearance parameters has been observed. We recommend the assessment of clearance parameters during gait analysis in addition to parameters such as gait speed, bearing in mind the importance of foot clearance measures in obstacle negotiation, slipping and tripping related falls.
Kamiar Aminian, Farzin Dadashi, Benoit Mariani, Constanze Hoskovec, Brigitte Brigitte Santos-Eggimann, Christophe Büla
The mobile phone represents a unique platform for interactive applications that can harness the opportunity of an immediate contact with a user in order to increase the impact of the delivered information. However, this accessibility does not necessarily translate to reachability, as recipients might refuse an initiated contact or disfavor a message that comes in an inappropriate moment. In this paper we seek to answer whether, and how, suitable moments for interruption can be identified and utilized in a mobile system. We gather and analyze a real-world smartphone data trace and show that users' broader context, including their activity, location, time of day, emotions and engagement, determine different aspects of interruptibility. We then design and implement InterruptMe, an interruption management library for Android smartphones. An extensive experiment shows that, compared to a context-unaware approach, interruptions elicited through our library result in increased user satisfaction and shorter response times.
Veljko Pejovic, Mirco Musolesi
We investigated how household deployment of Internet-connected locks and security cameras could impact teenagers' privacy. In interviews with 13 teenagers and 11 parents, we investigated reactions to audit logs of family members' comings and goings. All parents wanted audit logs with photographs, whereas most teenagers preferred text-only logs or no logs at all. We unpack these attitudes by examining participants' parenting philosophies, concerns, and current monitoring practices. In a follow-up online study, 19 parents configured an Internet-connected lock and camera system they thought might be deployed in their home. All 19 participants chose to monitor their children either through unrestricted access to logs or through real-time notifications of access. We discuss directions for auditing interfaces that could improve home security without impacting privacy.
Blase Ur, Jaeyeon Jung, Stuart Schechter
Smartphones can collect considerable context data about the user, ranging from apps used to places visited. Frequent user patterns discovered from longitudinal, multi-modal context data could help personalize and improve overall user experience. Our long term goal is to develop novel middleware and algorithms to efficiently mine user behavior patterns entirely on the phone by utilizing idle processor cycles. Mining patterns on the mobile device provides better privacy guarantees to users, and reduces dependency on cloud connectivity. As an important step in this direction, we develop a novel general-purpose service called MobileMiner that runs on the phone and discovers frequent co-occurrence patterns indicating which context events frequently occur together. Using longitudinal context data collected from 106 users over 1-3 months, we show that MobileMiner efficiently generates patterns using limited phone resources. Further, we find interesting behavior patterns for individual users and across users, ranging from calling patterns to place visitation patterns. Finally, we show how our co-occurrence patterns can be used by developers to improve the phone UI for launching apps or calling contacts.
Vijay Srinivasan, Saeed Moghaddam, Abhishek Mukherji, Kiran K. Rachuri, Chenren Xu, Emmanuel Munguia Tapia
We present a device-free indoor tracking system that uses received signal strength (RSS) from radio frequency (RF) transceivers to estimate the location of a person. While many RSS-based tracking systems use a body-worn device or tag, this approach requires no such tag. The approach is based on the key principle that RF signals between wall-mounted transceivers reflect and absorb differently depending on a person’s movement within their home. A hierarchical neural network hidden Markov model (NN-HMM) classifier estimates both movement patterns and stand vs. walk conditions to accurately perform tracking. The algorithm and features used are specifically robust to changes in RSS mean shifts in the environment over time allowing for greater than 90% region level classification accuracy over an extended testing period. In addition to tracking, the system also estimates the number of people in different regions. It is currently being developed to support independent living and long-term monitoring of seniors.
Anindya Paul, Eric A Wan, Fatema Adenwala, Erich Schafermeyer, Nicholas Preiser, Jeffrey Kaye, Peter Jacobs
In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls made/received, applications, e.g. time spent on different apps, location and media, e.g. photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e. meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of mobile services and future markets of personal data.
Jacopo Staiano, Nuria Oliver, Bruno Lepri, Rodrigo de Oliveira, Michele Caraviello, Nicu Sebe
Power remains a challenge in the widespread deployment of long-lived wireless sensing systems, which has led researchers to consider power harvesting as a potential solution. In this paper, we present a thermal power harvester that utilizes naturally changing ambient temperature in the environment as the power source. In contrast to traditional thermoelectric power harvesters, our approach does not require a spatial temperature gradient; instead it relies on temperature fluctuations over time, enabling it to be used freestanding in any environment in which temperature changes throughout the day. By mechanically coupling linear motion harvesters with a temperature sensitive bellows, we show the capability of harvesting up to 21 mJ of energy per cycle of temperature variation within the range 5 ’ to 25 ’. We also demonstrate the ability to power a sensor node, transmit sensor data wirelessly, and update a bistable E-ink display after as little as a 0.25 ’ ambient temperature change.
Chen Zhao, Sam Yisrael, Josh R Smith, Shwetak Patel
Mohit Sethi, Elena Oat, Mario Di Francesco, Tuomas Aura
Multimodal and natural user interfaces offer an innovative approach to sensory integration therapies. We designed and developed SensoryPaint, a multimodal system that allows users to paint on a large display using physical objects, body-based interactions, and interactive audio. We evaluat-ed the impact of SensoryPaint through two user studies: a lab-based study of 15 children with neurodevelopmental disorders in which they used the system for up to one hour, and a deployment study with four children with autism, during which the system was integrated into existing daily sensory therapy sessions. Our results demonstrate that a multimodal large display, using whole body interactions combined with tangible interactions and interactive audio feedback, balances children’s attention between their own bodies and sensory stimuli, augments existing therapies, and promotes socialization. These results offer implications for the design of other ubicomp systems for children with neurodevelopmental disorders and for their integration into therapeutic interventions.
Kathryn E Ringland, Rodrigo Zalapa, Megan Neal, Lizbeth Escobedo, Monica Tentori, Gillian R Hayes
Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-today and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.
Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, Andrew Campbell
The layouts of the buildings we live in shape our everyday lives. In office environments, building spaces affect employees' communication, which is crucial for productivity and innovation. However, accurate measurement of how spatial layouts affect interactions is a major challenge and traditional techniques may not give an objective view. We measure the impact of building spaces on social interactions using wearable sensing devices. We study a single organization that moved between two different buildings, affording a unique opportunity to examine how space alone can affect interactions. The analysis is based on two large scale deployments of wireless sensing technologies: short-range, lightweight RFID tags capable of detecting face-to-face interactions. We analyze the traces to study the impact of the building change on social behavior, which represents a first example of using ubiquitous sensing technology to study how the physical design of two workplaces combines with organizational structure to shape contact patterns.
The layouts of the buildings we live in shape our everyday lives. In office environments, building spaces affect employees' communication, which is crucial for productivity and innovation. However, accurate measurement of how spatial layouts affect interactions is a major challenge and traditional techniques may not give an objective view. We measure the impact of building spaces on social interactions using wearable sensing devices. We study a single organization that moved between two different buildings, affording a unique opportunity to examine how space alone can affect interactions. The analysis is based on two large scale deployments of wireless sensing technologies: short-range, lightweight RFID tags capable of detecting face-to-face interactions. We analyze the traces to study the impact of the building change on social behavior, which represents a first example of using ubiquitous sensing technology to study how the physical design of two workplaces combines with organizational structure to shape contact patterns.
Chloe Brown, Christos Efstratiou, Ilias Leontiadis, Daniele Quercia, Cecilia Mascolo, James Scott, Peter Key
This paper presents Zero-Effort Payments (ZEP), a seamless mobile computing system designed to accept payments with no effort on the customer’s part beyond a one-time opt-in. With ZEP, customers need not present cards nor operate smartphones to convey their identities. ZEP uses three complementary identification technologies: face recognition, proximate device detection, and human assistance. We demonstrate that the combination of these technologies enables ZEP to scale to the level needed by our deployments. We designed and built ZEP, and demonstrated its usefulness across two real-world deployments lasting five months of continuous deployment, and serving 274 customers. The different nature of our deployments stressed different aspects of our system. These challenges led to several system design changes to improve scalability and fault-tolerance.
Christopher Smowton, Jacob R Lorch, David Molnar, Stefan Saroiu, Alec Wolman