Data Mining Practical Machine Learning Tools and Techniques

IEEE Transactions on Pattern Analysis and Machine Intelligence

Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

This paper presents WiSee, a novel gesture recognition system that leverages wireless signals (e.g., Wi-Fi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require line-of-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources. Further, it achieves this goal without requiring instrumentation of the human body with sensing devices. We implement a proof-of-concept prototype of WiSee using USRP-N210s and evaluate it in both an office environment and a two- bedroom apartment. Our results show that WiSee can identify and classify a set of nine gestures with an average accuracy of 94%.

/pdf/whole-home-gesture-recognition-using-wireless-signals-4tosup7gjr.pdf

Whole-home gesture recognition using wireless signals

Thin-film electronics in its myriad forms has underpinned much of the technological innovation in the fields of displays, sensors, and energy conversion over the past four decades. This technology also forms the basis of flexible electronics. Here we review the current status of flexible electronics and attempt to predict the future promise of these pervading technologies in healthcare, environmental monitoring, displays and human-machine interactivity, energy conversion, management and storage, and communication and wireless networks.

Flexible Electronics: The Next Ubiquitous Platform

A sensing system includes a sensor assembly that is communicably connected to a computer system, such as a server or a cloud computing system. The sensor assembly includes a plurality of sensors that sense a variety of different physical phenomena. The sensor assembly featurizes the raw sensor data and transmits the featurized data to the computer system. Through machine learning, the computer system then trains a classifier to serve as a virtual sensor for an event that is correlated to the data from one or more sensor streams within the featurized sensor data. The virtual sensor can then subscribe to the relevant sensor feeds from the sensor assembly and monitor for subsequent occurrences of the event. Higher order virtual sensors can receive the outputs from lower order virtual sensors to infer nonbinary details about the environment in which the sensor assemblies are located.

Virtual sensor system

Although network bandwidth has increased dramatically, high-resolution images often take several seconds to load, and considerably longer on mobile devices over wireless connections. Progressive image loading techniques allow for some visual content to be displayed prior to the whole file being downloaded. In this note, we present an empirical evaluation of popular progressive image loading methods, and derive one novel technique from our findings. Results suggest a spiral variation of bilinear interlacing can yield an improvement in content recognition time.

/pdf/evaluation-of-progressive-image-loading-schemes-4pp75venwl.pdf

Evaluation of progressive image loading schemes

Touch sensitive devices, methods and computer readable recording mediums are provided that allow for improved classification of objects against a touch sensitive surface of a touch sensitive device based upon analysis of subdivisions of data representing contact with the touch sensitive surface during a period of time.

Method and apparatus for classifying contacts with a touch sensitive device

Future homes and offices will feature increasingly dense ecosystems of IoT devices, such as smart lighting, speakers, and domestic appliances. Voice input is a natural candidate for interacting with out-of-reach and often small devices that lack full-sized physical interfaces. However, at present, voice agents generally require wake-words and device names in order to specify the target of a spoken command (e.g., 'Hey Alexa, kitchen lights to full bright-ness'). In this research, we explore whether speech alone can be used as a directional communication channel, in much the same way visual gaze specifies a focus. Instead of a device's microphones simply receiving and processing spoken commands, we suggest they also infer the Direction of Voice (DoV). Our approach innately enables voice commands with addressability (i.e., devices know if a command was directed at them) in a natural and rapid manner. We quantify the accuracy of our implementation across users, rooms, spoken phrases, and other key factors that affect performance and usability. Taken together, we believe our DoV approach demonstrates feasibility and the promise of making distributed voice interactions much more intuitive and fluid.

https://dl.acm.org/doi/pdf/10.1145/3379337.3415588

Direction-of-Voice (DoV) Estimation for Intuitive Speech Interaction with Smart Devices Ecosystems

Today’s consumer virtual reality (VR) systems offer immersive graphics and audio, but haptic feedback is rudimentary – delivered through controllers with vibration feedback or is non-existent (i.e., the hands operating freely in the air). In this paper, we explore an alternative, highly mobile and controller-free approach to haptics, where VR applications utilize the user’s own body to provide physical feedback. To achieve this, we warp (retarget) the locations of a user’s hands such that one hand serves as a physical surface or prop for the other hand. For example, a hand holding a virtual nail can serve as a physical backstop for a hand that is virtually hammering, providing a sense of impact in an air-borne and uninstrumented experience. To illustrate this rich design space, we implemented twelve interactive demos across three haptic categories. We conclude with a user study from which we draw design recommendations.

Chris Harrison

Papers

Virtual sensor system

Evaluation of progressive image loading schemes

Method and apparatus for classifying contacts with a touch sensitive device

Direction-of-Voice (DoV) Estimation for Intuitive Speech Interaction with Smart Devices Ecosystems

Retargeted Self-Haptics for Increased Immersion in VR without Instrumentation