Frost accumulation on refrigerator evaporator coils is a significant source of wasted energy. While automatic de-frosting is a standard feature on modern refrigerators, current commercial solutions use heuristics to determine the frequency of heating cycles, leading to a sub-optimal defrosting routine. The majority of previous defrosting research incorporates cameras or microwave technology to better inform defrost algorithms of frost accumulation, however, these methods are both financially and computationally expensive. In this paper, we propose a low-cost frost detection system using ultrasonic resonance of piezoelectric sensors. We addressed the financial and computational cost challenges by using low-cost sensors and basic circuit components to replace software complexity. Our frost detection system was evaluated extensively in a Samsung refrigerator, resulting in a frost detection accuracy of 99.7%. We believe our solution can be further used for downstream refrigerator control cycle optimizations to achieve improved energy efficiency.

Robot end effectors with proximity detection and contact sensing capabilities can reactively position the gripper to align objects and ensure successful grasps. In this paper, we introduce SonicFinger, an acoustic aura based sensing system capable of full-surface pre-touch and contact sensing. A single piezoelectric transducer embedded within a novel 3D printed finger is excited using a monotone to create an acoustic aura encompassing the finger; this enables pre-touch sensing and gripper alignment, while changes in finger-transducer acoustic coupling indicate contact. SonicFinger is low-cost, compact, and easy to manufacture and assemble. Sensing capabilities are evaluated using a set of objects with various physical properties such as optical reflectivity, dielectric constants, mechanical properties, and acoustic absorption. A dataset with over 8,000 proximity and contact events is collected. Our system shows a pre-touch detection true positive rate (TPR) of 92.4% and a true negative rate (TNR) of 95.3%. Contact detection experiments show a TPR of 93.7% and a TNR of 98.7%. Furthermore, pretouch detection information from Sonic Finger is used to adjust the robot grippers pose to align a target object at the center of both fingers.

Contact sensing and localization capabilities that mimic human skin are highly desirable for robots. In this paper, we introduce AcouSkin, an acoustic wave based full surface contact localization system. Acoustic waves produced by piezoelectric transceivers using a monotone are coupled to surfaces turning them into an active sensor. Our system leverages information from four piezoelectric transceivers mounted on the surface of an acrylic sheet and vacuum cleaner robot bumper to localize contacts to 18 unique segments. We first characterize acoustic wave propagation based on signal and material properties and then propose hardware and software methods to realize full surface contact localization. Our results show that AcouSkin can reliably localize contact on a flat acrylic sheet with 18 uniformly spaced locations across a 54cm length with mean absolute error (MAE) of ≤ 1 locations using maximum likelihood estimator (MLE) and multilayer perceptron (MLP) models. On the vacuum cleaner robot bumper AcouSkin shows a zero MAE. Further, the system is also able to localize contacts made using forces as low as 2N (Newtons) and as high as 20N. Overall, AcouSkin provides full surface contact localization while requiring minimal instrumentation with easy deployment on real-world robots.

In this paper, we present AmbiSense, an acoustic field based sensing system that performs proximity detection and bearing estimation for safer physical human-robot interactions. A single low cost piezoelectric transducer is used to setup this novel acoustic sensing modality to create a blindspot-free sound field engulfing a robot arm. Two detection algorithms leveraging spectral information from reflected audio waves of objects entering the acoustic field are proposed to infer object presence and bearing. We also present a new receiver structure which improves signal to noise ratio (SNR). AmbiSense is paired with a collision avoidance inverse kinematic solver for real world deployment on a Kinova Gen3 robot. Validation is performed using ten test objects generating 2000 proximity and bearing estimation events in real world settings, we show that AmbiSense detects proximity with 93.8% sensitivity and 96.6 % specificity. It estimates bearing and maps it to three zones on a robot link with 100% sensitivity and specificity, while using fewer sensors than state of the art methods for similar coverage.

Headphones continue to become more intelligent as new functions (e.g., touch-based gesture control) appear. These functions usually rely on auxiliary sensors (e.g., accelerometer and gyroscope) that are available in smart headphones. However, for those headphones that do not have such sensors, supporting these functions becomes a daunting task. This paper presents HeadFi, a new design paradigm for bringing intelligence to headphones. Instead of adding auxiliary sensors into headphones, HeadFi turns the pair of drivers that are readily available inside all headphones into a versatile sensor to enable new applications spanning across mobile health, user-interface, and context-awareness. HeadFi works as a plug-in peripheral connecting the headphones and the pairing device (e.g., a smartphone). The simplicity (can be as simple as only two resistors) and small form factor of this design lend itself to be embedded into the pairing device as an integrated circuit. We envision HeadFi can serve as a vital supplementary solution to existing smart headphone design by directly transforming large amounts of existing "dumb" headphones into intelligent ones. We prototype HeadFi on PCB and conduct extensive experiments with 53 volunteers using 54 pairs of non-smart headphones under the institutional review board (IRB) protocols. The results show that HeadFi can achieve 97.2%--99.5% accuracy on user identification, 96.8%--99.2% accuracy on heart rate monitoring, and 97.7%--99.3% accuracy on gesture recognition.

Head-mounted devices (HMD) for Augmented Reality (AR) are gaining traction thanks to a growing number of applications in the areas of image guided therapy, computer aided design, cargo packing, manufacturing and digital field service. However, providing an always available, intuitive and user friendly input for these devices remains a challenging problem. This paper explores recognizing dynamic, micro finger gestures using capacitive coupling for interacting with a head-mounted device. Electrodes are attached to fingertips of users gloves and capacitive coupling among all pairs of electrodes is measured quickly to infer the real-time spatial relationship between fingers. The system is able to recognize fine, low-effort finger gestures, such as swiping, sliding, tap, double-tap. We evaluated our prototype with 14 gestures executed by 10 subjects and found a 97% accuracy of gesture recognition.

This paper explores the feasibility of localizing and detecting activities of building occupants using visible light sensing across a mesh of light bulbs. Existing Visible Light activity sensing (VLS) techniques require either light sensors to be deployed on the floor or a person to carry a device. Our approach integrates photosensors with light bulbs and exploits the light reflected off the floor to achieve an entirely device-free and light source based system. This forms a mesh of virtual light barriers across networked lights to track shadows cast by occupants. The design employs a synchronization circuit that implements a time division signaling scheme to differentiate between light sources and a sensitive sensing circuit to detect small changes in weak reflections. Sensor readings are fed into indoor supervised tracking algorithms as well as occupancy and activity recognition classifiers. Our prototype uses modified off-the-shelf LED flood light bulbs and is installed in a typical office conference room. We evaluate the performance of our system in terms of localization, occupancy estimation and activity classification, and find a 0.89m median localization error as well as 93.7% and 93.78% occupancy and activity classification accuracy, respectively.