Securely pairing wearables with ano-ther device is the key to many promising applications. This paper presents Touch-And-Guard ( TAG ), a system that uses hand touch as an intuitive manner to establish a secure connection between a wristband wearable and the touched device. It generates secret bits from hand resonant properties, which are obtained using accelerometers and vibration motors. The extracted secret bits are used by both sides to authenticate each other and then communicate confidentially. The ubiquity of accelerometers and motors presents an immediate market for our system. We demonstrate the feasibility of our system using an experimental prototype and conduct experiments involving 12 participants with 1,440 trials. The results indicate that we can generate secret bits at a rate of 7.15 bit s, which is 44 percent faster than conventional text input PIN authentication.
Vibration properties of objects have been exploited to enable different applications. @cite_12 develop a touch sensing technique that recognizes a rich context of touch postures based on the resonant changes when users change their touch postures and positions. SoQr @cite_23 estimates the amount of content inside a container based on the vibration responses to acoustic excitations. @cite_26 study the vibration properties of different persons, and design a wristband wearable to recognize household people based on their vibration properties. This paper is inspired by these studies, and takes it one step further in that we exploit the resonant properties of two objects (a hand and its touched device) in physical contact to facilitate secure pairing.
@cite_26 @cite_23 @cite_12
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In this paper, we present a novel acoustic touch sensing technique called Touch & Activate. It recognizes a rich context of touches including grasp on existing objects by attaching only a vibration speaker and a piezo-electric microphone paired as a sensor. It provides easy hardware configuration for prototyping interactive objects that have touch input capability. We conducted a controlled experiment to measure the accuracy and trade-off between the accuracy and number of training rounds for our technique. From its results, per-user recognition accuracies with five touch gestures for a plastic toy as a simple example and six hand postures for the posture recognition as a complex example were 99.6 and 86.3 , respectively. Walk up user recognition accuracies for the two applications were 97.8 and 71.2 , respectively. Since the results of our experiment showed a promising accuracy for the recognition of touch gestures and hand postures, Touch & Activate should be feasible for prototype interactive objects that have touch input capability. User identification is an essential problem for security protection and data privacy preservation of wearable devices. With proper user identification, wearable devices can adopt personalized settings for different users, automatically label the corresponding data to protect user privacy, and help prevent illegal user spoofing attacks. Current user identification solutions proposed for wearable devices either rely on dedicated devices with high cost or require user intervention which is not convenient. In this work, we leverage the bio-vibrometry to enable a novel user identification solution for wearable devices in small-scale scenarios, e.g., household scenario. Unlike existing user identification solutions, our system only uses the low-cost sensors that are already available for most wearable devices. The key idea is that, when human body is exposed to a vibration excitation, the vibration response reflects the physical characteristics of user, i.e., the mass, stiffness and damping. Meanwhile, due to users' biological diversity, such physical characteristics of different users are quite distinctive. Therefore, we can leverage the discrepancy in users' vibration responses as an identifier. Based on this idea, we propose VibID, which only uses a low-cost vibration motor and accelerometer to generate an unobtrusive vibration to users' arms and capture the corresponding responses. By examining the vibration patterns at different frequencies, VibID builds an ensemble machine learning model to recognize who is using the device. Extensive experiments are conducted on human subjects to demonstrate that our system is reliable in small- scale scenarios and robust to various confounding factors, e.g., arm position, muscle state, user mobility and wearing location. We also show that, in an uncontrolled scenario of 8 users, our system can still ensure a identification accuracy above 91 . According to the duplex theory of tactile texture perception, detection of cutaneous vibrations produced when the exploring finger moves across a surface contributes importantly to the perception of fine textures. If this is true, a vibrating surface should feel different from a stationary one. To test this prediction, experiments were conducted in which subjects examined two identical surfaces, one of which was surreptitiously made to vibrate, and judged which of the two was smoother. In experiment 1, the vibrating surface was less and less often judged smoother as the amplitude of (150 Hz) vibration increased. The effect was comparable in subjects who realized the surface was vibrating and those who did not. Experiment 2 showed that different frequencies (150–400 Hz) were equally effective in eliciting the effect when equated in sensation level (dB SL). The results suggest that vibrotaction contributes to texture perception, and that, at least within the Pacinian channel, it does so by means of an inten... We describe a novel method for symbolic location discovery of simple objects. The method requires no infrastructure and relies on simple sensors routinely used in sensor nodes and smart objects (acceleration, sound). It uses vibration and short, narrow frequency 'beeps' to sample the response of the environment to mechanical stimuli. The method works for specific locations such as 'on the couch', 'in the desk drawer' as well as for location classes such as 'closed wood compartment' or 'open iron surface'. In the latter case, it is capable of generalizing the classification to locations the object has not seen during training. We present the results of an experimental study with a total of over 1200 measurements from 35 specific locations (taken from 3 different rooms) and 12 abstract location classes. It includes such similar locations as the inner and outer pocket of a jacket and a table and shelf made of the same wood. Nonetheless on locations from a single room (16 in the largest one) we achieve a recognition rate of up to 96 . It goes down to 81 if all 35 locations are taken together, however the correct location is in the 3 top picks of the system 94 of the times.