With the growing ubiquity of wearable devices, sensed physiological responses provide new means to connect with others. While recent research demonstrates the expressive potential for biosignals, the value of sharing these personal data remains unclear. To understand their role in communication, we created Significant Otter, an Apple Watch/iPhone app that enables romantic partners to share and respond to each other’s biosignals in the form of animated otter avatars. In a one-month study with 20 couples, participants used Significant Otter with biosignals sensing OFF and ON. We found that while sensing OFF enabled couples to keep in touch, sensing ON enabled easier and more authentic communication that fostered social connection. However, the addition of biosignals introduced concerns about autonomy and agency over the messages they sent. We discuss design implications and future directions for communication systems that recommend messages based on biosignals.

Unpredictable ML model behavior on unseen data, especially in the health domain, raises serious concerns about its safety as repercussions for mistakes can be fatal. In this paper, we explore the feasibility of using state-of-the-art out-of-distribution detectors for reliable and trustworthy diagnostic predictions. We select publicly available deep learning models relating to various health conditions (e.g., skin cancer, lung sound, and Parkinson's disease) using various input data types (e.g., image, audio, and motion data). We demonstrate that these models show unreasonable predictions on out-of-distribution datasets. We show that Mahalanobis distance- and Gram matrices-based out-of-distribution detection methods are able to detect out-of-distribution data with high accuracy for the health models that operate on different modalities. We then translate the out-of-distribution score into a human interpretable \textsc{confidence score} to investigate its effect on the users' interaction with health ML applications. Our user study shows that the \textsc{confidence score} helped the participants only trust the results with a high score to make a medical decision and disregard results with a low score. Through this work, we demonstrate that dataset shift is a critical piece of information for high-stake ML applications, such as medical diagnosis and healthcare, to provide reliable and trustworthy predictions to the users.

AirConstellations supports a unique semi-fixed style of cross-device interactions via multiple self-spatially-aware armatures to which users can easily attach (or detach) tablets and other devices. In particular, AirConstellations affords highly flexible and dynamic device formations where the users can bring multiple devices together in-air — with 2–5 armatures poseable in 7DoF within the same workspace — to suit the demands of their current task, social situation, app scenario, or mobility needs. This affords an interaction metaphor where relative orientation, proximity, attaching (or detaching) devices, and continuous movement into and out of ad-hoc ensembles can drive context-sensitive interactions. Yet all devices remain self-stable in useful configurations even when released in mid-air.

Sleep is critical to human function, mediating factors like memory, mood, energy, and alertness; therefore, it is commonly conjectured that a good night’s sleep is important for job performance. However, both real-world sleep behavior and job performance are difficult to measure at scale. In this work, we demonstrate that people’s everyday interactions with online mobile apps can reveal insights into their job performance in real-world contexts. We present an observational study in which we objectively tracked the sleep behavior and job performance of salespeople (N = 15) and athletes (N = 19) for 18 months, leveraging a mattress sensor and online mobile app to conduct the largest study of this kind to date. We first demonstrate that cumulative sleep measures are significantly correlated with job performance metrics, showing that an hour of daily sleep loss for a week was associated with a 9.0% average reduction in contracts established for salespeople and a 9.5% average reduction in game grade for the athletes. We then investigate the utility of online app interaction time as a passively collectible and scalable performance indicator. We show that app interaction time is correlated with the job performance of the athletes, but not the salespeople. To support that our app-based performance indicator truly captures meaningful variation in psychomotor function as it relates to sleep and is robust against potential confounds, we conducted a second study to evaluate the relationship between sleep behavior and app interaction time in a cohort of 274 participants. Using a generalized additive model to control for per-participant random effects, we demonstrate that participants who lost one hour of daily sleep for a week exhibited average app interaction times that were 5.0% slower. We also find that app interaction time exhibits meaningful chronobiologically consistent correlations with sleep history, time awake, and circadian rhythms. The findings from this work reveal an opportunity for online app developers to generate new insights regarding cognition and productivity.

Rapid diagnostic tests (RDTs) provide point-of-care medical diagnosis without sophisticated laboratory equipment, making them especially useful for community health workers (CHWs). Because the procedure for completing a malaria RDT is error-prone, CHWs are often asked to carry completed RDTs back to their supervisors. Doing so makes RDTs susceptible to deterioration and introduces inefficiencies in the CHWs' workflow. In this work, we propose a smartphone-based RDT capture app, RDTScan, that facilitates the collection of high-quality RDT images to support CHWs in the field. RDTScan does not require an external adapter to control the image capture environment, but instead provides real-time guidance using image processing to obtain the best image possible. During our evaluation study, we found that RDTScan had 98.1% sensitivity and 99.7% specificity against visual inspection of the RDTs. RDTScan helped CHWs capture high-quality RDT images within 18 seconds while enabling a better RDT workflow.

Conversational agents are rich in content today. However, they are entirely oblivious to users’ situational context, limiting their ability to adapt their response and interaction style. To this end, we explore the design space for a context augmented conversational agent, including analysis of input segment dynamics and computational alternatives. Building on these, we propose a solution that redesigns the input segment intelligently for ambient context recognition, achieved in a two-step inference pipeline. We first separate the non-speech segment from acoustic signals and then use a neural network to infer diverse ambient contexts. To build the network, we curated a public audio dataset through crowdsourcing. Our experimental results demonstrate that the proposed network can distinguish between 9 ambient contexts with an average F1 score of 0.80 with a computational latency of 3 milliseconds. We also build a compressed neural network for on-device processing, optimised for both accuracy and latency. Finally, we present a concrete manifestation of our solution in designing a context-aware conversational agent and demonstrate use cases.

Overheating smartphones could hamper user experiences. While there have been numerous reports on smartphone overheating, a systematic measurement and user experience study on the thermal aspect of smartphones is missing. Using thermal imaging cameras, we measure and analyze the temperatures of various smartphones running diverse application workloads such as voice calling, video recording, video chatting, and 3D online gaming. Our experiments show that running popular applications such as video chat, could raise the smartphone's surface temperature to over 50$^\circ$C in only 10 minutes, which could easily cause thermal pain to users. Recent ubiquitous scenarios such as augmented reality and mobile deep learning also have considerable thermal issues. We then perform a user study to examine when the users perceive heat discomfort from the smartphones and how they react to overheating. Most of our user study participants reported considerable thermal discomfort while playing a mobile game, and that overheating disrupted interaction flows. With this in mind, we devise a smartphone surface temperature prediction model, by using only system statistics and internal sensor values. Our evaluation showed high prediction accuracy with root-mean-square errors of less than 2$^\circ$C. We discuss several insights from our findings and recommendations for user experience, OS design, and developer support for better user-thermal interactions.

We introduce Zaturi, a system enabling parents to create an audio book for their babies by utilizing micro spare time at work. We define micro spare time at work as tiny fragments of time with low cognitive loads that frequently occur at work, such as waiting for an elevator. We show that putting together micro spare time at work helps a working parent (1) build a tangible symbol conveying his/her thoughts to the beloved baby and (2) develop his/her own feelings of parental achievement without compromising regular working hours. Zaturi lets the parent immediately be aware of micro spare time and provides a crafted interface to seamlessly record the book piece by piece, so that the baby can enjoy listening to the book recorded in the parent's own voice. Through an extensive design process, we characterize the notion of micro spare time and build a working prototype of Zaturi. We also report parents' perceptions and family reactions after a two-week deployment.

Smartphone notifications provide application-specific information in real-time, but could distract users from in-person social interactions when delivered at inopportune moments. We explore breakpoint-based notification management, in which the smartphone defers notifications until an opportune moment. With a video survey where participants selected appropriate moments for notifications from a video-recorded social interaction, we identify four breakpoint types: long silence, a user leaving the table, others using smartphones, and a user left alone. We introduce a Social Context-Aware smartphone Notification system, SCAN, that uses build-in sensors to detect social context and identifies breakpoints to defer smartphone notifications until a breakpoint. We conducted a controlled study with ten friend groups who had SCAN installed on their smartphones while dining at a restaurant. Results show that SCAN accurately detects breakpoints (precision=92.0%, recall=82.5%), and reduces notification interruptions by 54.1%. Most participants reported that SCAN helped them to focus better on in-person social interaction and found selected breakpoints appropriate.

Since the advent of datacenter networking, achieving low latency within the network has been a primary goal. Many congestion control schemes have been proposed in recent years to meet the datacenters' unique performance requirement. The nature of congestion feedback largely governs the behavior of congestion control. In datacenter networks, where round trip times are in hundreds of microseconds, accurate feedback is crucial to achieve both high utilization and low queueing delay. Proposals for datacenter congestion control predominantly leverage explicit congestion notification (ECN) or even explicit in-network feedback to minimize the queuing delay. In this paper, we explore latency-based feedback as an alternative and show its advantages over ECN. Against the common belief that such implicit feedback is noisy and inaccurate, we demonstrate that latency-based implicit feedback is accurate enough to signal a single packet's queuing delay in 10 Gb/s networks. Such high accuracy enables us to design a new congestion control algorithm, DX, that performs fine-grained control to adjust the congestion window just enough to achieve very low queuing delay while attaining full utilization. Our extensive evaluation shows that: 1) the latency measurement accurately reflects the one-way queuing delay in single packet level; 2) the latency feedback can be used to perform practical and fine-grained congestion control in high-speed datacenter networks; and 3) DX outperforms DCTCP with 5.33 times smaller median queueing delay at 1 Gb/s and 1.57 times at 10 Gb/s.