Brain–computer interfaces (BCIs) are neural prosthetics that enable closed-loop electrophysiology procedures. These devices are currently used in fundamental neurophysiology research, and they are moving toward clinical viability for neural rehabilitation. State-of-the-art BCI experiments have often been performed using tethered (wired) setups in controlled laboratory settings. Wired tethers simplify power and data interfaces but restrict the duration and types of experiments that are possible, particularly for the study of sensorimotor pathways in freely behaving animals. To eliminate tethers, there is significant ongoing research to develop fully wireless BCIs having wireless uplink of broadband neural recordings and wireless recharging for long-duration deployment, but significant challenges persist. BCIs must deliver complex functionality while complying with tightly coupled constraints in size, weight, power, noise, and biocompatibility. In this article, we provide an overview of recent progress in wireless BCIs and a detailed presentation of two emerging technologies that are advancing the state of the art: ultralow-power wireless backscatter communication and adaptive inductive resonant (AIR) wireless power transfer (WPT).

For more than two decades, capacitive sensing has played a prominent role in human-computer interaction research. Capacitive sensing has become ubiquitous on mobile, wearable, and stationary devices - enabling fundamentally new interaction techniques on, above, and around them. The research community has also enabled human position estimation and whole-body gestural interaction in instrumented environments. However, the broad field of capacitive sensing research has become fragmented by different approaches and terminology used across the various domains. This paper strives to unify the field by advocating consistent terminology and proposing a new taxonomy to classify capacitive sensing approaches. Our extensive survey provides an analysis and review of past research and identifies challenges for future work. We aim to create a common understanding within the field of human-computer interaction, for researchers and practitioners alike, and to stimulate and facilitate future research in capacitive sensing.

Synthetic aperture radar (SAR) images are representations of the microwave or millimeter-wave reflectivity of the observed scenes. SAR image reconstruction is an inverse problem, which can be solved via an approximation, e.g. matched filter (MF), or the explicit inverse using a large amount of measurement data. However, the approximation limits the resolution while the explicit inverse is computationally complex and mostly ill-conditioned. This paper proposes a partitioned inverse (PI) approach based on the Moore-Penrose pseudo inverse using truncated singular value decomposition for regularization, which is robust to noise. It is shown that PI has an improved resolution of 24% over MF even at 0 dB SNR and is three orders of magnitude faster than the explicit inverse. A measurement based proof of concept experiment using a laboratory K-Band (15-26.5 GHz) ultra-wideband SAR system is shown to validate the proposed approach.

To maintain sufficient signal-to-noise ratio (SNR) for image reconstruction and image interpretation, conventional synthetic aperture radar (SAR) systems must trade off resolution and scene size. This paper proposes a new SAR mode of operation, which improves resolution while maintaining good SNR and a large scene size. It leverages the unique properties of dynamic metasurface antennas (MSAs) to subsample a large virtual beamwidth utilizing multiple small distinct antenna beams. Due to this parallelization in scene sampling, the constraints on the azimuth sampling rate can be relaxed while maintaining an aliasing-free cross range. Due to the versatile properties of MSAs and their cost effective manufacturing process, this paper proposes SAR systems, which can obtain high resolution images over a wide scene size with lower cost and complexity than competing approaches. Point-spread functions and proof-of-concept SAR simulations are shown to verify this approach. In addition, laboratory experiments using a commercial prototype MSA are presented, which show an improvement of 62% in cross-range resolution of the proposed approach, compared with the cross-range resolution of stripmap mode SAR with the same aperture.

Optical depth cameras, including both time-of-flight and structured-light sensors, have led to dramatic improvements in robot sensing and perception. We propose the use of millimeter-wave (mmW) radar as an important complement to optical sensors. While the millimeter wavelengths of radar sensors do not support as high resolution as the nanometer wavelength of optical sensors, the ability of mmW signals to penetrate smoky and foggy environments as well as see through many opaque objects makes them a compelling sensor for navigation as well as manipulation in challenging environments. We present a series of 2D and 3D mmW images made with a hand-held antenna grasped by a PR2 robot. The radar image sensor uses a mechanical “painting” motion to acquire multiple views of the target object over the 15 - 26.5 GHz K-band. A GPU-based reconstruction algorithm synthesizes 2D and 3D images of the target object. We demonstrate a ground range resolution of 13.6 mm and a cross-range resolution of 7.1 mm for objects up to 0.5 m away from the robot. We further demonstrate imaging objects through fog, as well as through opaque paper.

In this paper, we present a significant improvement over past work on non-contact end-user deployable sensor for real time whole home power consumption. The technique allows users to place a single device consisting of magnetic pickups on the outside of a power or breaker panel to infer whole home power consumption without the need for professional installation of current transformers (CTs). The new approach does not require precise placement on the breaker panel, a key requirement in previous approaches. This is enabled through a self-calibration technique using a neural network that dynamically learns the transfer function despite the placement of the sensor and the construction of the breaker panel itself. We also demonstrate the ability to actually infer true power using this technique, unlike past solutions that have only been able to capture apparent power. We have evaluated our technique in six homes and one industrial building, including one seven-day deployment. Our results show we can estimate true power consumption with an average accuracy of 95.0% during naturalistic energy use in the home.

This paper concerns the use of wideband RF backscatter from semi-passive RF tags for energy-efficient wireless telemetry. Using LFM (Linear Frequency Modulated) signals from a radar base station, we present a method for joint ranging and communications with a distributed set of sensor nodes. Backscatter signaling from each node results in modulation on a sub-carrier frequency determined by the node distance from the radar. Upper bounds on communications rate per coverage area are presented, as well as clutter filtering to suppress ground components. Experimental results from a brass-board microwave system in an indoor environment are presented and discussed.

We address the challenge of finding and navigating to an object with an attached ultra-high frequency radio-frequency identification (UHF RFID) tag. With current off-the-shelf technology, one can affix inexpensive self-adhesive UHF RFID tags to hundreds of objects, thereby enabling a robot to sense the RF signal strength it receives from each uniquely identified object. The received signal strength indicator (RSSI) associated with a tagged object varies widely and depends on many factors, including the object's pose, material properties and surroundings. This complexity creates challenges for methods that attempt to explicitly estimate the object's pose. We present an alternative approach that formulates finding and navigating to a tagged object as an optimization problem where the robot must find a pose of a directional antenna that maximizes the RSSI associated with the target tag. We then present three autonomous robot behaviors that together perform this optimization by combining global and local search. The first behavior uses sparse sampling of RSSI across the entire environment to move the robot to a location near the tag; the second samples RSSI over orientation to point the robot toward the tag; and the third samples RSSI from two antennas pointing in different directions to enable the robot to approach the tag. We justify our formulation using the radar equation and associated literature. We also demonstrate that it has good performance in practice via tests with a PR2 robot from Willow Garage in a house with a variety of tagged household objects.

Current smartphone inputs are limited to physical buttons, touchscreens, cameras or built-in sensors. These approaches either require a dedicated surface or line-of-sight for interaction. We introduce SideSwipe, a novel system that enables in-air gestures both above and around a mobile device. Our system leverages the actual GSM signal to detect hand gestures around the device. We developed an algorithm to convert the discrete and bursty GSM pulses to a continuous wave that can be used for gesture recognition. Specifically, when a user waves their hand near the phone, the hand movement disturbs the signal propagation between the phone's transmitter and added receiving antennas. Our system captures this variation and uses it for gesture recognition. To evaluate our system, we conduct a study with 10 participants and present robust gesture recognition with an average accuracy of 87.2% across 14 hand gestures.

We present a unique multi-antenna RFID reader (a sensor) embedded in a robot's manipulator that is designed to operate with ordinary UHF RFID tags in a short-range, near-field electromagnetic regime. Using specially designed near-field antennas enables our sensor to obtain spatial information from tags at ranges of less than 1 meter. In this work, we characterize the near-field sensor's ability to detect tagged objects in the robots manipulator, present robot behaviors to determine the identity of a grasped object, and investigate how additional RF signal properties can be used for “pre-touch” capabilities such as servoing to grasp an object. The future combination of long-range (far-field) and short-range (near-field) UHF RFID sensing has the potential to enable roboticists to jump-start applications by obviating or supplementing false-positive-prone visual object recognition. These techniques may be especially useful in the healthcare and service sectors, where mis-identification of an object (for example, a medication bottle) could have catastrophic consequences.

Wearable sensor systems have been used in the ubiquitous computing community and elsewhere for applications such as activity and gesture recognition, health and wellness monitoring, and elder care. Although the power consumption of accelerometers has already been highly optimized, this work introduces a novel sensing approach which lowers the power requirement for motion sensing by orders of magnitude. We present an ultra-low-power method for passively sensing body motion using static electric fields by measuring the voltage at any single location on the body. We present the feasibility of using this sensing approach to infer the amount and type of body motion anywhere on the body and demonstrate an ultra-low-power motion detector used to wake up more power-hungry sensors. The sensing hardware consumes only 3.3 μW, and wake-up detection is done using an additional 3.3 μW (6.6 μW total).

In this paper, we describe LightWave, a sensing approach that turns ordinary compact fluorescent light (CFL) bulbs into sensors of human proximity. Unmodified CFL bulbs are shown to be sensitive proximity transducers when they are illuminated. This approach utilizes predictable variations in electromagnetic noise resulting from the change in impedance due to the proximity of a human body to the bulb. The electromagnetic noise can be sensed from any point along a home's electrical wiring. This allows users to perform gestures near any CFL lighting fixture, even when multiple lamps are operational. Gestures can be sensed using a single interface device plugged into any electrical outlet. We experimentally show that we can reliably detect hover gestures (waving a hand close to a lamp), touches on lampshades, and touches on the glass part of the bulb itself. Additionally, we show that touches anywhere along the body of a metal lamp can be detected. These basic detectable signals can then be combined to form complex gesture sequences for a variety of applications. We also show that CFLs can function as more general-purpose sensors for distributed human motion detection and ambient temperature sensing.

We present an empirical study of the long-term practicality of using human motion to generate operating power for body-mounted consumer electronics and health sensors. We have collected a large continuous acceleration data set from eight experimental subjects going about their normal daily routine for three days each. Each subject is instrumented with a data collection apparatus that simultaneously logs 3-axis, 80 Hz acceleration data from six body locations. We use this data set to optimize a first-principles physical model of the commonly used velocity damped resonant generator (VDRG) by selecting physical parameters such as resonant frequency and damping coefficient to maximize the harvested power. Our results show that with reasonable assumptions on size, mass, placement, and efficiency of VDRG harvesters, most body-mounted wireless sensors and even some consumer electronics devices can be powered continuously and indefinitely from everyday motion. We have optimized the power harvesters for each individual and for each body location. In addition, we present the potential of designing a damping- and frequency-tunable power harvester that could mitigate the power reduction of a generator generalized for "average” subjects. We present the full details on the collection of the acceleration data sets, the development of the VDRG model, and a numerical simulator, and discuss some of the future challenges that remain in this promising field of research.

We present the design, development, and evaluation of an end-user installable, whole house power consumption sensing system capable of gathering accurate real-time power use that does not require installing a current transformer around the electrical feeds in a home. Rather, our sensor system offers contactless operation by simply placing it on the outside of the breaker panel in a home. Although there are a number of existing commercial systems for gathering energy use in a home, almost none can easily and safely be installed by a homeowner (especially for homes in the U.S.). Our approach leverages advances in magnetoresistive materials and circuit design to allow contactless operation by reliably sensing the magnetic field induced by the 60 Hz current and a closed loop circuit allows us to precisely infer the power consumption in real-time. The contribution of this work is an enabling technology for researchers in the fields of Ubiquitous Computing and Human-Computer Interaction wanting to conduct practical large-scale deployments of end-user-deployable energy monitoring applications. We discuss the technical details, the iterative design, and end-user evaluations of our sensing approach.

This paper presents ElectriSense, a new solution for automatically detecting and classifying the use of electronic devices in a home from a single point of sensing. ElectriSense relies on the fact that most modern consumer electronics and fluorescent lighting employ switch mode power supplies (SMPS) to achieve high efficiency. These power supplies continuously generate high frequency electromagnetic interference (EMI) during operation that propagates throughout a home's power wiring. We show both analytically and by in-home experimentation that EMI signals are stable and predictable based on the device's switching frequency characteristics. Unlike past transient noise-based solutions, this new approach provides the ability for EMI signatures to be applicable across homes while still being able to differentiate between similar devices in a home. We have evaluated our solution in seven homes, including one six-month deployment. Our results show that ElectriSense can identify and classify the usage of individual devices with a mean accuracy of 93.82%.

Fingerprinting techniques for indoor localization have been widely explored. A particular approach by Patel et al. suggested leveraging of the residential powerline as the signaling mechanism for a domestic location capability. In this paper, we critically examine that initial work, called powerline positioning (PLP). We find the proposed technique lacking in temporal stability, requiring frequent and undesired recalibration in some environments. We also determine that there is no a priori method to determine a pair of signaling frequencies that will reliably work in any space. We propose a wideband approach to PLP (WPLP) that injects up to 44 different frequencies into the powerline. We show that this WPLP approach improves upon overall positioning accuracy, demonstrates greatly improved temporal stability and has the added advantage of working in commercial indoor spaces.

We present an empirical study of the long-term practicality of using human motion to generate operating power for body-mounted consumer electronics and health sensors. We have collected a large continuous acceleration dataset from eight experimental subjects going about their normal daily routine for 3 days each. Each subject is instrumented with a data collection apparatus that simultaneously logs 3-axis, 80Hz acceleration data from six body locations. We use this dataset to optimize a first-principles physical model of the commonly used velocity damped resonant generator (VDRG) by selecting physical parameters such as resonant frequency and damping coefficient to maximize harvested power. Our results show that with reasonable assumptions on size, mass, placement, and efficiency of VDRG harvesters, most body-mounted wireless sensors and even some consumer electronics devices, may be powered continuously and indefinitely from everyday motion.

We introduce an inexpensive, low complexity power surface system capable of simultaneously providing wireless power and bidirectional communication from a surface to multiple mobile robots. This system enables continuous operation of a swarm-sized population of battery-less robots. Our first prototype consists of a 60 cm times 60 cm power surface that provides power and bidirectional communication to an initial evaluation group of five test robots, each one consuming 200 mW. Unlike typical non-resonant inductive (transformer) coupling, power transmission in this system is achieved through magnetic flux coupling between a high Q L-C resonator placed beneath the operating surface and a non-resonant pickup coil on each robot. We explore the design of the pickup coil and conditioning circuitry, and we characterize the position-dependent power density of a static load representative of a small autonomous robot operating on the surface. We demonstrate a continuous power density averaging 4.1 mW/cm2for a static load, and develop much greater peak power for dynamic loads via capacitor storage and power conditioning circuitry. We also demonstrate simultaneous broadcast communication between the surface and all robots via amplitude modulation of the magnetic field, and communication between individual robots and the surface via load modulation.

We present a novel antenna and system architecture for mobile manipulation based on passive RFID technology operating in the 850 MHz - 950 MHz ultra-high-frequency (UHF) spectrum. This system exploits the electromagnetic properties of UHF radio signals to present a mobile robot with both wide-angle dasiaperipheral visionpsila, sensing multiple tagged objects in the area in front of the robot, and focused, high-acuity dasiacentral visionpsila, sensing only tagged objects close to the end effector of the manipulator. These disparate tasks are performed using the same UHF RFID tag, coupled in two different electromagnetic modes. Wide-angle sensing is performed with an antenna designed for far-field electromagnetic wave propagation, while focused sensing is performed with a specially designed antenna mounted on the end effector that optimizes near-field magnetic coupling. We refer to this RFID system as dasiafoveatedpsila, by analogy with the anatomy of the human eye. We report a series of experiments on an untethered autonomous mobile manipulator in a 2.5D environment that demonstrate the features of this architecture using two novel behaviors, one in which data from the far-field antenna is used to determine if a specific tagged object is present in the robotpsilas working area and to navigate to that object, and a second using data from the near-field antenna to grasp a specified object from a collection of visually identical objects. The same UHF RFID tag is used to facilitate both the navigation and grasping tasks.

We present a novel particle filter implementation for estimating the pose of tags in the environment with respect to an RFID-equipped robot. This particle filter combines signals from a specially designed RFID antenna system with odometry and an RFID signal propagation model. Our model includes antenna characteristics, direct-path RF propagation, and multipath RF propagation. We first describe a novel 6-antenna RFID sensor system that provides the robot with a 360-degree view of the tags in its environment. We then present the results of real-world evaluation where RFID-inferred tag position is compared with ground truth data from a laser range-finder. In our experiments the system is shown to estimate the pose of UHF RFID tags in a real-world environment without requiring a priori training or map-building. The system exhibits 6.1 deg mean bearing error and 0.69 m mean range error over robot to tag distances of over 4 m in an environment with significant multipath. The RFID system provides the ability to uniquely identify specific tagged locations and objects, and to discriminate among multiple tagged objects in the field at the same time, which are important capabilities that a laser range-finder does not provide. We expect that this new type of multiple-antenna RFID system, including particle filters that incorporate RF signal propagation models, will prove to be a valuable sensor for mobile robots operating in semi-structured environments where RFID tags are present.