Mostafa Ammar

Commercial smart rings have demonstrated their utility in health applications such as sleep monitoring and fitness tracking. However, given the small size of a ring form-factor, the applicable scenarios of smart rings are still under-explored. This paper presents SigningRing, which proposes a novel functionality for the smart ring: employing the inertial sensor embedded in the smart ring for secure and fast user authentication. To access an account, the user wearing the ring will move their finger in air, tracing a signature-like pattern, as if signing their name. In our evaluation with 18 volunteers, SigningRing achieves 97.4% in balanced accuracy, with 99.8% true negative rate and 95.1% true positive rate.

Recording 3D movements of a user's hand, robotic arms, or an object, even in a small confined space, has several applications in AR/VR, robotics, movement science, and 3D modeling and rendering. Existing camera-based tracking systems, though extremely accurate, are quite expensive and suffer from issues of occlusion and face difficulties when operating in extremely dark or extremely bright environments. We contend that trading-off a bit of accuracy while reducing costs and enabling more flexible operating environment might be worth exploring. This paper presents UTrack3D, a table-top setup that tracks the movements of an object in 3D space using embedded low-cost ultra-wideband (UWB) radios. The core idea is to continuously track the changes in phase as captured from UWB signal's channel impulse response (CIR) derived from the UWB messages received at a set of dual-antenna UWB receivers. Each of our custom dual-antenna receivers captures the UWB signal from two corners of a cuboid allowing us to perform relative phase measurements. The main challenges in the solution are caused by a location-dependant large variation in the signal amplitudes and corruption of the CIR due to multipath. UTrack3D tackles these challenges via a signal processing pipeline fusing a forward localization process which tracks the object's location using UWB CIR phase, and a posterior location check process, which validates the estimated location. UTrack3D is implemented on commercial-off-the-shelf (COTS) UWB chips, and provides a 90^th percentile accuracy of 9 mm in a table-top 3D region (1.5m × 0.8m × 0.8m). We evaluate the effects of additional UWB receivers, effect of different movement speeds, and effect of small-scale signal blocking using different materials. We expect UTrack3D to allow researchers a rich new environment for further advancing UWB-based 3D tracking.

Video conferencing applications (VCAs) have become an indispensable tool for business, educational, and personal communications. There is, therefore, considerable interest in understanding and measuring the Quality of Experience (QoE) delivered by VCAs to their users. Video quality, one QoE measure, has received considerable attention in the literature. In this paper, we are concerned with another important aspect of VCA QoE, namely interactivity. We define this informally as the ability of a VCA to facilitate satisfying interaction among its users. Interactivity is primarily impacted by the media transmission latency among users which is, in turn, a function of network and application processing delays. Our goal in this work is to address two challenges in investigating interactivity-related QoE in VCAs. First, we propose a suite of meaningful quantifiable interactivity metrics, such as the proportion of silence time and rate of overlapping speech, that correlate well with conversational impairments and, hence, QoE perceptions. Second, we investigate scalable approaches for measuring these metrics. We develop a validated model for user behavior that enables realistic simulation of interactivity in VCA sessions. We also briefly consider an approach to measure interactivity metrics from packet traces. Through a set of experimental results, we demonstrate how our evaluation methodology provides a way for researchers, VCA service providers and network operators to perform large-scale investigations of how latency can interfere with user interactivity and impact VCA QoE.

We study user’s valuations of smartphone sensing resources and the factors mediating them through a systematic auction study with 108 bids from N =18 participants, two resource use conditions [fixed battery (FB) and variable battery (VB)] and three sensors (camera, microphone, and GPS) with differing energy and privacy costs. We use a second-price sealed-bid reverse auction as this allows us to elicit the participants’ truthful perceived value for sharing resources. We show that most users would be willing to share even highly-privacy intrusive sensors if they are sufficiently compensated. At the FB level, participants placed much lower value for sharing GPS (€13) than camera (€30) or microphone (€32.5). The values people place on sharing access to resources generally reflect four considerations: 1) the perceived value of the sensor type; 2) the value of the data captured by the sensor; 3) the impact of sharing on the device; and 4) personal variations related to sharing motives, personal tendencies, and the broader sharing context. We address the practical impact of our results by presenting two case studies (collaborative sensing and collaborative AI). Finally, we derive design implications for sharing sensing resources on personal devices.

Visual body signals are designated body poses that deliver an application-specific message. Such signals are widely used for fast message communication in sports (signaling by umpires and referees), transportation (naval officers and aircraft marshallers), and construction (signaling by riggers and crane operators), to list a few examples. Automatic interpretation of such signals can help maintaining safer operations in these industries, help in record-keeping for auditing or accident investigation purposes, and function as a score-keeper in sports. When automation of these signals is desired, it is traditionally performed from a viewer's perspective by running computer vision algorithms on camera feeds. However, computer vision based approaches suffer from performance deterioration in scenarios such as lighting variations, occlusions, etc., might face resolution limitations, and can be challenging to install. Our work, ViSig, breaks with tradition by instead deploying on-body sensors for signal interpretation. Our key innovation is the fusion of ultra-wideband (UWB) sensors for capturing on-body distance measurements, inertial sensors (IMU) for capturing orientation of a few body segments, and photodiodes for finger signal recognition, enabling a robust interpretation of signals. By deploying only a small number of sensors, we show that body signals can be interpreted unambiguously in many different settings, including in games of Cricket, Baseball, and Football, and in operational safety use-cases such as crane operations and flag semaphores for maritime navigation, with > 90% accuracy. Overall, we have seen substantial promise in this approach and expect a large body of future follow-on work to start using UWB and IMU fused modalities for the more general human pose estimation problems.

High-precision tracking of a pen-like instrument's movements is desirable in a wide range of fields spanning education, robotics, and art, to name a few. The key challenge in doing so stems from the impracticality of embedding electronics in the tip of such instruments (a pen, marker, scalpel, etc.) as well as the difficulties in instrumenting the surface that it works on. In this paper, we present ITrackU, a movement digitization system that does not require modifications to the surface or the tracked instrument's tip. ITrackU fuses locations obtained using ultra-wideband radios (UWB), with an inertial and magnetic unit (IMU) and a pressure sensor, yielding multidimensional improvements in accuracy, range, cost, and robustness, over existing works. ITrackU embeds a micro-transmitter at the base of a pen which creates a trackable beacon, that is localized from the corners of a writing surface. Fused with inertial motion sensor and a pressure sensor, ITrackU enables accurate tracking. Our prototype of ITrackU covers a large 2.5m × 2m area, while obtaining around 2.9mm median error. We demonstrate the accuracy of our system by drawing numerous shapes and characters on a whiteboard, and compare them against a touchscreen and a camera-based ground-truthing system. Finally, the produced stream of digitized data is minuscule in volume, when compared with a video of the whiteboard, which saves both network bandwidth and storage space.

Understanding end-user video Quality of Experience (QoE) is important for Internet Service Providers (ISPs). Existing work presents mechanisms that use network measurement data to estimate video QoE. Most of these mechanisms assume access to packet-level traces, the most-detailed data available from the network. However, collecting packet-level traces can be challenging at a network-wide scale. Therefore, we ask:"Is it feasible to estimate video QoE with lightweight, readily-available, but coarse-grained network data?" We specifically consider data in the form of Transport Layer Security (TLS) transactions that can be collected using a standard proxy and present a machine learning-based methodology to estimate QoE. Our evaluation with three popular streaming services shows that the estimation accuracy using TLS transactions is high (up to 72%) with up to 85% recall in detecting low QoE (low video quality or high re-buffering) instances. Compared to packet traces, the estimation accuracy (recall) is 7% (9%) lower but has up to 60 times lower computation overhead.

Storage has become a constrained resource on smartphones. Gaming is a popular activity on mobile devices and the explosive growth in the number of games coupled with their growing size contributes to the storage crunch. Even where storage is plentiful, it takes a long time to download and install a heavy app before it can be launched. This paper presents AppStreamer, a novel technique for reducing the storage requirements or startup delay of mobile games, and heavy mobile apps in general. AppStreamer is based on the intuition that most apps do not need the entirety of its files (images, audio and video clips, etc.) at any one time. AppStreamer can, therefore, keep only a small part of the files on the device, akin to a "cache", and download the remainder from a cloud storage server or a nearby edge server when it predicts that the app will need them in the near future. AppStreamer continuously predicts file blocks for the near future as the user uses the app, and fetches them from the storage server before the user sees a stall due to missing resources. We implement AppStreamer at the Android file system layer. This ensures that the apps require no source code or modification, and the approach generalizes across apps. We evaluate AppStreamer using two popular games: Dead Effect 2, a 3D first-person shooter, and Fire Emblem Heroes, a 2D turn-based strategy role-playing game. Through a user study, 75% and 87% of the users respectively find that AppStreamer provides the same quality of user experience as the baseline where all files are stored on the device. AppStreamer cuts down the storage requirement by 87% for Dead Effect 2 and 86% for Fire Emblem Heroes.