Automatic, camera-free lifelogging offers new opportunities for memory rehabilitation, personal informatics, and assistive technologies. However, most existing approaches limit daily activities to isolated event labels, offering little context and lacking the narrative coherence essential for effective lifelogging. Recent advances in audio–language models combine foundation audio processing with language-based reasoning, enabling open-ended sound understanding. We introduce EchoScriptor, an end-to-end system that transforms raw in-home audio into context-aware natural-language descriptions, generating coherent narrative lifelogs of activities and acoustic contexts. In moment-level evaluation, EchoScriptor achieved 94.15% activity recognition and 89.25% background recognition accuracy, and at the summary level, achieved an F1 score of 0.92, outperforming the classifier+LLM baseline. In our user study with 20 participants across 10 household activity videos, EchoScriptor summaries were consistently rated highly, approaching the perceived utility of human-written ones. By advancing from event detection to narrative understanding, EchoScriptor establishes a significant step toward automated, unobtrusive, context-aware lifelogging technologies.

This study introduces AutoCLC, an AI-powered system designed to assess and provide feedback on Closed-Loop Communication (CLC) in professional learning environments. CLC, where a sender’s Call-Out statement is acknowledged by the receiver’s Check-Back statement, is a critical safety protocol in high-reliability domains, including emergency medicine resuscitation teams. Existing methods for evaluating CLC lack quantifiable metrics and depend heavily on human observation. AutoCLC addresses these limitations by leveraging natural language processing and large language models to analyze audio recordings from Advanced Cardiovascular Life Support (ACLS) simulation training. The system identifies CLC instances, measures their frequency and rate per minute, and categorizes communications as effective, incomplete, or missed. Technical evaluations demonstrate that AutoCLC achieves 78.9% precision for identifying Call-Outs and 74.3% for Check-Backs, with a performance gap of only 5% compared to human annotations. A user study involving 11 cardiac arrest instructors across three training sites supported the need for automated CLC assessment. Instructors found AutoCLC reports valuable for quantifying CLC frequency and quality, as well as for providing actionable, example-based feedback. Participants rated AutoCLC highly, with a System Usability Scale score of 76.4%, reflecting above-average usability. This work represents a significant step toward developing scalable, data-driven feedback systems that enhance individual skills and team performance in high-reliability settings.

As the number of smart devices increases in our lives, the data they collect to perform valuable tasks, such as voice assistant requests, comes at the cost of user privacy. To mitigate their privacy impact, emerging usable privacy-aware sensing (UPAS) research has relied on cross-disciplinary approaches that extend past the core focus of broader academic research communities, such as Security & Privacy or Human-Computer Interaction. These works incorporate privacy design principles, whereby systems include safeguards by combining usable privacy (UP) with privacy-aware sensing (PAS) design to protect users' privacy. To better understand this emerging area of research, we conducted a mixed qualitative and quantitative Systematization of Knowledge (SoK). With a thorough review of pertinent literature, resulting in 114 selected works (reduced from 10,122 across 12 venues), we found that, despite the similarity of these works, many are dispersed across multiple communities, utilize community-specific jargon and keywords, and minimally overlap in design and evaluation approaches, potentially hindering cross-pollination across communities and thereby slowing the growth of this emerging research area. Thus, these factors helped reveal a research gap in this space. We use these findings to present four research themes and provide community and design recommendations to encourage cross-disciplinary UPAS research.

Fiducial marking is indispensable in mobile robots, including their pose calibration, contextual perception, and navigation. However, existing fiducial markers rely solely on vision-based perception which suffers such limitations as occlusion, energy overhead, and privacy leakage.

The proliferation of mobile devices in consumer electronics, IoT, and healthcare sectors has sparked considerable interest in wireless localization. While antenna array systems have demonstrated promise for wireless localization, they often entail high costs, intricate system designs, lengthy integration periods, and specialized packet formats. This study employs a 16-element L-shaped antenna array paired with a 2-channel 2MHz receiver, utilizing affordable and readily available components to localize incoming packets. The proposed approach calculates the 2D Angle of Arrival (AoA) of incoming signals using a custom Phase Difference Matching (PDM) algorithm. Additionally, a non-parallel wave depth estimator infers depth information of the signal source by learning phase difference trends. The result shows the system achieves median AoA error of 2.53 degrees horizontally and 1.88 degrees vertically, with an average depth estimation error of 1.07 m. This approach demonstrates the potential for 3D wireless localization of commonly available RF devices, through an N-element 2D phased array paired with a cost-effective commodity receiver.

Internet-connected cameras support many useful home monitoring and health applications. However, these same cameras indiscriminately capture sensitive and Personally Identifiable Information (PII), limiting their acceptance in certain settings, such as the home. Prior works removed Region of Interest (ROI) to secure images and improve privacy. However, the methods that rely solely on RGB information to find persons are susceptible to environmental and lighting conditions, causing them to fail and leak PII. From our deployment study, nearly half of the images containing persons had a PII leakage when using RGB-only methods. Furthermore, ROI removal is often performed off-device, requiring the server performing these operations to be trustworthy. This work presents the PrivacyLens system, where with the addition of thermal sensing, our system has a significantly enhanced ability to find persons in RGB images and video and efficiently remove them on the device before any data is stored or transmitted, all while staying under typical IoT power constraints. From our aforementioned deployment study in an office-building atrium, family home, and outdoor park environment, the PrivacyLens prototype effectively removes PII with a sanitization rate of 99.1%. Additionally, PrivacyLens can use its embedded GPU to generate on-device features for downstream CV/ML tasks, as shown in three illustrative applications, further reducing the collection and storage of PII.

Enabling computing systems to understand user interactions with everyday surfaces and objects can drive a wide range of applications. However, existing vibration-based sensors (e.g., accelerometers) lack the sensitivity to detect light touch gestures or the bandwidth to recognize activity containing high-frequency components. Conversely, microphones are highly susceptible to environmental noise, degrading performance. Each time an object impacts a surface, Surface Acoustic Waves (SAWs) are generated that propagate along the air-to-surface boundary. This work repurposes a Voice PickUp Unit (VPU) to capture SAWs on surfaces (including smooth surfaces, odd geometries, and fabrics) over long distances and in noisy environments. Our custom-designed signal acquisition, processing, and machine learning pipeline demonstrates utility in both interactive and activity recognition applications, such as classifying trackpad-style gestures on a desk and recognizing 16 cooking-related activities, all with >97% accuracy. Ultimately, SAWs offer a unique signal that can enable robust recognition of user touch and on-surface events.

Road surface markings, like symbols and line markings, are vital traffic infrastructures for driving safety and efficiency. However, real-world conditions can impair the utility of existing road markings. For example, adverse weather conditions such as snow and rain can quickly obliterate visibility.

Touchscreen devices, designed with an assumed range of user abilities and interaction patterns, often present challenges for individuals with diverse abilities to operate independently. Prior efforts to improve accessibility through tools or algorithms necessitated alterations to touchscreen hardware or software, making them inapplicable for the large number of existing legacy devices. In this paper, we introduce BrushLens, a hardware interaction proxy that performs physical interactions on behalf of users while allowing them to continue utilizing accessible interfaces, such as screenreaders and assistive touch on smartphones, for interface exploration and command input. BrushLens maintains an interface model for accurate target localization and utilizes exchangeable actuators for physical actuation across a variety of device types, effectively reducing user workload and minimizing the risk of mistouch. Our evaluations reveal that BrushLens lowers the mistouch rate and empowers visually and motor impaired users to interact with otherwise inaccessible physical touchscreens more effectively.

Emerging ultra-low-power tiny scale computing devices run on harvested energy, are intermittently powered, have limited computational capability, and perform sensing and actuation functions under the control of a dedicated firmware operating without the supervisory control of an operating system. Wirelessly updating or patching firmware of such devices is inevitable. We consider the challenging problem of simultaneous and secure firmware updates or patching for a typical class of such devicesComputational Radio Frequency Identification (CRFID) devices. We propose Wisecr, the first secure and simultaneous wireless code dissemination mechanism to multiple devices that prevents malicious code injection attacks and intellectual property (IP) theft, whilst enabling remote attestation of code installation. Importantly, Wisecr is engineered to comply with existing ISO compliant communication protocol standards employed by CRFID devices and systems. We comprehensively evaluate Wisecr's overhead, demonstrate its implementation over standards compliant protocols, analyze its security, implement an end-to-end realization with popular CRFID devices and open-source the complete software package on GitHub.

Magnetic sensing is emerging as an enabling technology for various engaging applications. Representative use cases include high-accuracy posture tracking, human-machine interaction, and haptic sensing. This technology uses multiple MEMS magnetometers to capture the changing magnetic field at a close distance. However, magnetometers are susceptible to real-world disturbances, such as hard- and soft-iron effects. As a result, users need to perform a cumbersome and lengthy calibration process frequently, severely limiting the usability of magnetic tracking.

Pervasive and interactive displays promise to present our digital content seamlessly throughout our environment. However, traditional display technologies do not scale to room-wide applications due to high per-unit-area costs and the need for constant wired power and data infrastructure. This research proposes the use of photochromic paint as a display medium. Applying the paint to any surface or object creates ultra-low-cost displays, which can change color when exposed to specific wavelengths of light. We develop new paint formulations that enable wide area application of photochromic material. Along with a specially modified wide-area laser projector and depth camera that can draw custom images and create on-demand, room-wide user interfaces on photochromic enabled surfaces. System parameters such as light intensity, material activation time, and user readability are examined to optimize the display. Results show that images and user interfaces can last up to 16 minutes and can be updated indefinitely. Finally, usage scenarios such as displaying static and dynamic images, ephemeral notifications, and the creation of on-demand interfaces, such as light switches and music controllers, are demonstrated and explored. Ultimately, the UbiChromics system demonstrates the possibility of extending digital content to all painted surfaces.

Augmented Reality (AR) has the potential to leverage environmental information to better facilitate distributed collaboration, however, such applications are difficult to develop. We present XSpace, a toolkit for creating spatially-aware AR applications for distributed collaboration. Based on a review of existing applications and developer tools, we design XSpace to support three methods for creating shared virtual spaces, each emphasizing a different aspect: shared objects, user perspectives, and environmental meshes. XSpace implements these methods in a developer toolkit, and also provides a set of complimentary visual authoring tools to allow developers to preview a variety of configurations for a shared virtual space. We present five example applications to illustrate that XSpace can support the development of a rich set of collaborative AR experiences that are difficult to produce with current solutions. Through XSpace, we discuss implications for future application design, including user space customization and privacy and safety concerns when sharing users' environments.

Sound presents an invaluable signal source that enables computing systems to perform daily activity recognition. However, microphones are optimized for human speech and hearing ranges: capturing private content, such as speech, while omitting useful, inaudible information that can aid in acoustic recognition tasks. We simulated acoustic recognition tasks using sounds from 127 everyday household/workplace objects, finding that inaudible frequencies can act as a substitute for privacy-sensitive frequencies. To take advantage of these inaudible frequencies, we designed a Raspberry Pi-based device that captures inaudible acoustic frequencies with settings that can remove speech or all audible frequencies entirely. We conducted a perception study, where participants “eavesdropped’’ on PrivacyMic’s filtered audio and found that none of our participants could transcribe speech. Finally, PrivacyMic’s real-world activity recognition performance is comparable to our simulated results, with over 95% classification accuracy across all environments, suggesting immediate viability in performing privacy-preserving daily activity recognition.

Accurate tracking of the hands and fingers allows users to employ natural gestures in various interactive applications. Hand tracking also supports health applications, such as monitoring face-touching, a common vector for infectious disease. However, for both types of applications, the utility of hand tracking is often limited by the impracticality of bulky tethered systems (e.g., instrumented gloves) or inherent limitations (e.g., Line of Sight or privacy concerns with vision-based systems). These limitations have severely restricted the adoption of hand tracking in real-world applications. We present MagX, a fully untethered on-body hand tracking system utilizing passive magnets and a novel magnetic sensing platform. Since passive magnets require no maintenance, they can be worn on the hands indefinitely, and only the sensor board needs recharging, akin to a smartwatch.

Accurate tracking of the hands and fingers allows users to employ natural gestures in various interactive applications, e.g., controller-free interaction in augmented reality. Hand tracking also supports health applications, such as monitoring face-touching, a common vector for infectious disease. However, for both types of applications, the utility of hand tracking is often limited by the impracticality of bulky tethered systems (e.g., instrumented gloves) or inherent limitations (e.g., Line of Sight or privacy concerns with vision-based systems). These limitations have severely restricted the adoption of hand tracking in real-world applications. We demonstrate MagX, a fully untethered on-body hand tracking system utilizing passive magnets and a novel magnetic sensing platform. Since passive magnets require no maintenance, they can be worn on the hands indefinitely, and only the sensor board needs recharging, akin to a smartwatch.

When digital applications aim to blend virtual and real worlds, understanding the actual physical actions of users becomes an important task; the precise timing of these tangible interaction events is needed, along with the identity, and possibly location and history, of all involved actors/objects. With multiple actors or objects, it is difficult to identify who touches which object and when. Instrumenting objects for Body Channel Communication (BCC) allows message exchange around the human body between instrumented objects and the user themselves. In this paper we show how BCC can be utilized to perform under real-time conditions so that we can directly notice touch events (and the identity of actors). TangibleID is a framework that unifies tangible interaction capture for objects and users based on wearable BCC. TangibleID provides identification and communication with tagged objects/users in less than 120 ms and supports a variety of tangible interactions, without the need to restrict user (hand) movements or to maintain line-of-sight connection to cameras. When an AR application is combined with TangibleID, a new tangible mixed reality experience is achieved, as demonstrated in the “Haunted Castle” showcase. The paper presents an end-to-end technical evaluation including trade-offs regarding robustness and speed of touch recognition, outlines the breadth of interaction modalities, and reports on an initial user assessment.

The simplicity of deployment and perpetual operation of energy harvesting devices provides a compelling proposition for a new class of edge devices for the Internet of Things. In particular, Computational Radio Frequency Identification (CRFID) devices are an emerging class of battery free, computational, sensing enhanced devices that harvest all of their energy for operation. Despite wireless connectivity and powering, secure wireless firmware updates remains an open challenge for CRFID devices due to: intermittent powering, limited computational capabilities, and the absence of a supervisory operating system. We present,for the first time, asecurewireless code dissemination (SecuCode) mechanism for CRFIDs by entangling adevice intrinsic hardware security primitive—Static Random Access Memory Physical Unclonable Function (SRAM PUF)—to a firmware update protocol. The design of SecuCode: i) overcomes the resource-constrained and intermittently powered nature of the CRFID devices; ii) is fully compatible with existing communication protocols employed by CRFID devices—in particular, ISO-18000-6C protocol; and ii) is built upon a standard and industry compliant firmware compilation and update method realized by extending a recent framework for firmware updates provided by Texas Instruments. We build an end-to-end SecuCode implementation and conduct extensive experiments to demonstrate standards compliance, evaluate performance and security.

We present Surface++, which leverages our previous work SATURN, a self-powered flexible acoustic sensor, and ZEUSSS, a passive wireless sound communication technique using analog backscatter, to create a scalable and self-sustainable wireless sound sensing surface. Our new prototype allows for large area acoustic sensing using modular fabrication techniques with the promise of being fully printable. A single small Surface++ patch can be used to extend voice and gesture input for everyday surfaces, while our more sensitive Surface++ modular array allows for large-area context sensing and localization.

Falls have serious consequences and are prevalent in acute hospitals and nursing homes caring for older people. Most falls occur in bedrooms and near the bed. Technological interventions to mitigate the risk of falling aim to automatically monitor bed-exit events and subsequently alert healthcare personnel to provide timely supervisions. We observe that frequency-domain information related to patient activities exist predominantly in very low frequencies. Therefore, we recognise the potential to employ a low resolution acceleration sensing modality in contrast to powering and sensing with a conventional MEMS (Micro Electro Mechanical System) accelerometer. Consequently, we investigate a batteryless sensing modality with low cost wirelessly powered Radio Frequency Identification (RFID) technology with the potential for convenient integration into clothing, such as hospital gowns. We design and build a passive accelerometer-based RFID sensor embodiment-ID-Sensor-for our study. The sensor design allows deriving ultra low resolution acceleration data from the rate of change of unique RFID tag identifiers in accordance with the movement of a patient's upper body. We investigate two convolutional neural network architectures for learning from raw RFID-only data streams and compare performance with a traditional shallow classifier with engineered features. We evaluate performance with 23 hospitalized older patients. We demonstrate, for the first time and to the best of knowledge, that: i) the low resolution acceleration data embedded in the RF powered ID-Sensor data stream can provide a practicable method for activity recognition; and ii) highly discriminative features can be efficiently learned from the raw RFID-only data stream using a fully convolutional network architecture.

Immersive experiences seek to engage the full sensory system in ways that words, pictures, or touch alone cannot. With respect to the haptic system, however, physical feedback has been provided primarily with handheld tactile experiences or vibration-based designs, largely ignoring both pressure receptors and the full upper-body area as conduits for expressing meaning that is consistent with sight and sound. We extend the potential for immersion along these dimensions with the Force Jacket, a novel array of pneumatically-actuated airbags and force sensors that provide precisely directed force and high frequency vibrations to the upper body. We describe the pneumatic hardware and force control algorithms, user studies to verify perception of airbag location and pressure magnitude, and subsequent studies to define full-torso, pressure and vibration-based feel effects such as punch, hug, and snake moving across the body. We also discuss the use of those effects in prototype virtual reality applications.

Human environments are typified by walls, homes, offices, schools, museums, hospitals and pretty much every indoor context one can imagine has walls. In many cases, they make up a majority of readily accessible indoor surface area, and yet they are static their primary function is to be a wall, separating spaces and hiding infrastructure. We present Wall++, a low-cost sensing approach that allows walls to become a smart infrastructure. Instead of merely separating spaces, walls can now enhance rooms with sensing and interactivity. Our wall treatment and sensing hardware can track users' touch and gestures, as well as estimate body pose if they are close. By capturing airborne electromagnetic noise, we can also detect what appliances are active and where they are located. Through a series of evaluations, we demonstrate Wall++ can enable robust room-scale interactive and context-aware applications.

Novel interactions that capacitively couple electromagnetic (EM) fields between devices and the human body are gaining more attention in the human-computer interaction community. One class of these techniques is Body Channel Communication (BCC), a method that overlays physical touch with digital information. Despite the number of published capacitive sensing and communication prototypes, there exists no guideline on how to design such hardware or what are the application limitations and possibilities. Specifically, wearable (groundless) BCC has been proven in the past to be extremely challenging to implement. Additionally, the exact behavior of the human body as an EM-field medium is still not fully understood today. Consequently, the application domain of BCC technology could not be fully explored. This paper addresses this problem. Based on a recently published general purpose wearable BCC system, we first present a thorough evaluation of the impact of various technical parameter choices and an exhaustive channel characterization of the human body as a host for BCC. Second, we discuss the implications of these results for the application design space and present guidelines for future wearable BCC systems and their applications. Third, we point out an important observation of the measurements, namely that BCC can employ the whole body as user interface (and not just hands or feet). We sketch several applications with these novel interaction modalities.

Communication costs dominate the energy consumption, and ultimately limit the utility, of low power devices and sensor nodes. Backscatter communication based on deliberate and ambient sources has the potential to radically alter this paradigm by offering two to three orders of magnitude better communication efficiency (in terms of nJ/Bit) then conventional radio architectures. Initial work on ambient backscatter shows promising results but has focused on narrow band operation in well controlled laboratory settings. The goal of this work is to enable the ubiquitous deployment of ultra-low power nodes that communicate via ambient backscatter to wired Universal Backscatter Readers, in real-world environments. This is accomplished through ultra-wideband backscatter techniques that leverage the breath of commercial broadcast signals in the 80 MHz to 900 MHz range from FM radios, digital TVs, and cellular networks. Additionally the use of powered Universal Backscatter Readers allows a network of ultra-low power nodes to operate on ambient carriers as low as -80 dBm, which is typical for indoor home and office environments. For the first time we demonstrate the simultaneous use of 17 ambient signal sources to achieve node-to-reader communication distances of 50 meters, with data rates up to 1 kbps.

Energy-autonomous computing devices have the potential to extend the reach of computing to a scale beyond either wired or battery-powered systems. However, these devices pose a unique set of challenges to application developers who lack both hardware and software support tools. Energy harvesting devices experience power intermittence which causes the system to reset and power-cycle unpredictably, tens to hundreds of times per second. This can result in code execution errors that are not possible in continuously-powered systems and cannot be diagnosed with conventional debugging tools such as JTAG and/or oscilloscopes. We propose the Energy-interference-free Debugger, a hardware and software platform for monitoring and debugging intermittent systems without adversely effecting their energy state. The Energy-interference-free Debugger re-creates a familiar debugging environment for intermittent software and augments it with debugging primitives for effective diagnosis of intermittence bugs. Our evaluation of the Energy-interference-free Debugger quantifies its energy-interference-freedom and shows its value in a set of debugging tasks in complex test programs and several real applications, including RFID code and a machine-learning-based activity recognition system.

We describe techniques that allow inexpensive, ultra-thin, battery-free Radio Frequency Identification (RFID) tags to be turned into simple paper input devices. We use sensing and signal processing techniques that determine how a tag is being manipulated by the user via an RFID reader and show how tags may be enhanced with a simple set of conductive traces that can be printed on paper, stencil-traced, or even hand-drawn. These traces modify the behavior of contiguous tags to serve as input devices. Our techniques provide the capability to use off-the-shelf RFID tags to sense touch, cover, overlap of tags by conductive or dielectric (insulating) materials, and tag movement trajectories. Paper prototypes can be made functional in seconds. Due to the rapid deployability and low cost of the tags used, we can create a new class of interactive paper devices that are drawn on demand for simple tasks. These capabilities allow new interactive possibilities for pop-up books and other papercraft objects.

Technologies that allow autonomous robots and computer systems to quickly recognize and interact with individuals in a group setting has the potential to enable a wide range of personalized experiences. However, existing solutions fail to both identify and locate individuals with enough speed to enable seamless interactions in very dynamic environments that require fast, implicit, non-intrusive, and ubiquitous recognition of users. In this work, we present a hybrid computer vision and RFID system that uses a novel reverse synthetic aperture technique to recover the relative motion paths of an RFID tags worn by people and correlate that to physical motion paths of individuals as measured with a 3D depth camera. Results show that our real-time system is capable of simultaneously recognizing and correctly assigning IDs to individuals within 4 seconds with 96.6% accuracy and groups of five people in 7 seconds with 95% accuracy. In order to test the effectiveness of this approach in realistic scenarios, groups of five participants play an interactive quiz game with an autonomous robot, resulting in an ID assignment accuracy of 93.3%.

RFID tags can be used to add inexpensive, wireless, batteryless sensing to objects. However, quickly and accurately estimating the state of an RFID tag is difficult. In this work, we show how to achieve low-latency manipulation and movement sensing with off-the-shelf RFID tags and readers. Our approach couples a probabilistic filtering layer with a monte-carlo-sampling-based interaction layer, preserving uncertainty in tag reads until they can be resolved in the context of interactions. This allows designers' code to reason about inputs at a high level. We demonstrate the effectiveness of our approach with a number of interactive objects, along with a library of components that can be combined to make new designs.

Energy-harvesting computers eschew tethered power and batteries by harvesting energy from their environment. The devices gather energy into a storage element until they have enough energy to power a computing device. Once powered, the device functions until its energy is depleted, when it browns out and gathers more energy. Software on such computing devices executes intermittently, as power is available. An intermittent program execution may be interrupted by a power failure at any point and with each interruption, the volatile state of the device (e.g., register file, RAM) is erased, and its non-volatile state (e.g., FRAM) is retained. Recent work [3] defined and characterized the intermittent execution model, in which a program's execution spans periods of execution perforated by power failures. Our position is that designers of system and toolchain support for energy-harvesting devices should treat energy-interference-freedom and intermittence as first-class design concerns in future systems, methodologies, and techniques. From this position, we discuss the design of an energy-interference-free platform for monitoring and manipulating the energy and device state of an energy-harvesting device. We see our platform as an essential step toward a toolchain for energy-harvesting devices that supports debugging, testing, and analysis of realistic, intermittent executions.

In order to enable unobtrusive human object interaction detection, we propose a minimalistic approach to instrumenting everyday objects with passive (i.e. battery-free) UHF RFID tags. By measuring the changes in the physical layer of the communication channel between the RFID tag and reader (such as RSSI, RF phase, and read rate) we are able to classify, in real time, tag/object motion events along with two types of touch events. Through a user study, we demonstrate that our real-time classification engine is able to simultaneously track 20 objects and identify four movement classes with 93% accuracy. To demonstrate how robust this general-purpose interaction mechanism is, we investigate three usage scenarios 1) interactive storytelling with toys 2) inference of daily activities in the home 3) identification of customer browsing habits in a retail setting.

RFID sensor networks perpetually stream sensor data without batteries. Cameras are power hungry but provide richer information than conventional sensor network nodes. Battery-free, RF-powered camera sensor nodes combine many of the attractive features of RFID sensor networks with those of cameras. However, prior battery-free cameras have no notion of 3D location, which is desirable for creating large scale networks of battery free cameras.

Most everyday electrical and electromechanical objects emit small amounts of electromagnetic (EM) noise during regular operation. When a user makes physical contact with such an object, this EM signal propagates through the user, owing to the conductivity of the human body. By modifying a small, low-cost, software-defined radio, we can detect and classify these signals in real-time, enabling robust on-touch object detection. Unlike prior work, our approach requires no instrumentation of objects or the environment; our sensor is self-contained and can be worn unobtrusively on the body. We call our technique EM-Sense and built a proof-of-concept smartwatch implementation. Our studies show that discrimination between dozens of objects is feasible, independent of wearer, time and local environment.

Paper displays have a number of attractive properties, in particular the ability to present visual information perpetually with no power source. However, they are not digitally updatable or re-usable. Bistable display materials, such as e-paper, promise to enable displays with the best properties of both paper and electronic displays. However, rewriting a pixelated bistable display requires substantial energy, both for communication and for setting the pixel states.

Effective means of delivering wireless power to volumes of spaces will enable users the freedom and mobility to seamlessly power and recharge their devices in an unencumbered fashion. This has particular importance for consumer electronic, medical, and industrial applications, where usage models focus on unstructured and dynamic environments. However, existing wireless power technology falls short of this vision. Inductive charging solutions are limited to near-contact distances and require a docking station or precise placement for effective operation. Far-field wireless power techniques allow much greater range, but require complicated tracking systems to maintain a line-of-sight connection for high-efficiency power delivery to mobile applications. Recent work using magnetically coupled resonators (MCRs) for wireless power delivery has shown a promising intersection between range (on the order of a meter), efficiency (over 80%), and delivered power (up to tens of watts). However, unpredictable loads rapidly change system operating points, and changes in position disrupt system efficiency, which affects the ultimate usability of these systems. Dynamic adaptation to these changes in operating conditions and power transfer range is a critical capability in developing a fully functional and versatile wireless power solution. This paper provides an overview of methods used to adapt to variations in range, orientation, and load using both wideband and fixed-frequency techniques.

Wireless data communication technology has eliminated wired connections for data transfer to portable devices. Wireless power technology offers the possibility of eliminating the remaining wired connection: the power cord. For ventricular assist devices (VADs), wireless power technology will eliminate the complications and infections caused by the percutaneous wired power connection. Integrating wireless power technology into VADs will enable VAD implants to become a more viable option for heart failure patients (of which there are 80 000 in the United States each year) than heart transplants. Previous transcutaneous energy transfer systems (TETS) have attempted to wirelessly power VADs ; however, TETS-based technologies are limited in range to a few millimeters, do not tolerate angular misalignment, and suffer from poor efficiency. The free-range resonant electrical delivery (FREE-D) wireless power system aims to use magnetically coupled resonators to efficiently transfer power across a distance to a VAD implanted in the human body, and to provide robustness to geometric changes. Multiple resonator configurations are implemented to improve the range and efficiency of wireless power transmission to both a commercially available axial pump and a VentrAssist centrifugal pump [3]. An adaptive frequency tuning method allows for maximum power transfer efficiency for nearly any angular orientation over a range of separation distances. Additionally, laboratory results show the continuous operation of both pumps using the FREE-D system with a wireless power transfer efficiency upwards of 90%.

The next generation internet will be the internet of things (and not just of computing devices like PCs, PDAs); this is presumed to be enabled by integrating simple computing plus communications capabilities into common objects of everyday use. Radio-frequency identification (RFID) is a compelling technology for creation of such pervasive sensor networks due to its potential for ubiquitous, low-cost/low-maintenance use. However, the current drivers for RFID deployment emphasize supply chain management using passive tags, implying that RFID sensor nets require advances beyond the components and system designs aimed at supply chain applications. This work provides a glimpse of how this may be achieved.

We demonstrate a simple RFID sensor network comprised of an Intel WISP and a commodity UHF RFID reader. WISPs are devices that gather their operating energy from RFID reader transmissions, in the manner of passive RFID tags, and further include sensors, e.g., accelerometers, and provide a very small-scale computing platform. We believe that the small form factor and lack of battery makes the WISP an attractive alternative to motes for many of the original smart dust applications that require very small or long-lived sensors. The Intel WISP that we demonstrate has an ultra-low-power microcontroller, 32K of program space, 8K of flash, and accelerometer and temperature sensors. It harvests power from and communicates sensor data to standard (EPC Class 1 Gen 2) UHF RFID readers with a range of roughly 10 feet. This combination of RFID technology and sensor networks raises many research challenges, such as how to function with intermittent power and how to modify RFID protocols to support sensor queries.