The present invention relates to classifying touch events, and more particularly, to a method and system for classifying vibro-acoustic signals to identify a finger part used to touch an interactive element displayed on a touch screen.
Many technologies exist that have the ability to digitize different types of input. There are two main touch sensing approaches: active and passive. The key downside of active approaches is that an explicit object must be used (e.g., a special pen), which is implemented with electronics (and potentially batteries). For example, pens augmented with infrared light emitters on their tips can be used on the commercially available Microsoft Surface. There have also been efforts to move beyond pens, including, e.g., infrared-light-emitting brushes for painting applications. Current systems generally do not attempt to discriminate among different pens (just perhaps pen from finger input). Variably-modulated infrared light enables identification, but requires specialized hardware. Additionally, ultrasonics can be used for input localization, and can provide pen ID as well. Capacitive coupling allows users or objects to be localized and identified, though this typically requires grounding plates or a physical connection to function.
Sensing based on electromagnetic resonance, another method, operates between active and passive. Although the tools and tangibles need not be powered, they contain a resonant coil that is excited by proximate EM fields, generated by the specialized tablet they operate on. It is also possible to support object identification through a combination of RFID and vision processing, which offers greater scalability.
Fiducial markers are a truly passive approach. They provide the ID of an object through a uniquely patterned tag—often in a sticker form factor. This method has been shown to be very capable—the only major drawback is the size of the marker, which in general, prevents placement on small surfaces like the tip of a pen. Fiducial markers can also work in a capacitive-sensing manner, allowing tags to be embedded in an object. Additionally, the shape of an object can be captured optically and used for classification (e.g., mice and keyboards).
In general, the aforementioned techniques require instrumentation of the object providing input, which is problematic for fingers (i.e., people do not like to wear things on their fingers and hands). Researchers have also looked at wrist-mounted acoustic sensors that can classify finger-on-finger actions, such as pinching or flicking Finger taps can also be localized on the body through acoustic fingerprinting.
However, the latter systems require sensors to be placed on the user. Without instrumentation, some areas of the finger can be determined through computer vision (e.g., pad vs. tip). Using accelerometers or acoustics, soft and hard taps can be discriminated.
Contemporary interactive surfaces generally treat finger touches as a single class of input (a partial exception to this are finger-print scanning systems, which can capture a high-resolution fingerprint image to infer the 3D “posture” of a finger; also, area of contact via optical or capacitive sensing can be considered an extra input dimension). However, this is a gross simplification—fingers are diverse appendages, both in their motor capabilities and their anatomical composition. Supporting additional dimensions of finger input have largely been ignored because instrumenting the user with active or passive components is invasive.
At present, in order for a finger to perform different operations at a single point in space on a touch screen, it must be overloaded. Most common is a tap-and-hold period, or chording of the fingers (e.g., two-finger-tap for right click). This can trigger a transient contextual menu, which allows a user to select one of several actions. An alternative to finger overloading is breaking function out into one or more buttons. For example, there is a scheme in which one button is for minimizing a window and another button is for closing it. However, this is problematic for mobile devices with limited screen real estate.
Currently, touch screens do not distinguish between different types of finger contacts. For example, touching with the tip of the finger and touching with the knuckle are treated the same. As such, all finger touches have the same interactive outcome.
In view of the above, the present invention provides a method and system for activating different interactive functions based on a classification of acoustic signals. In accordance with an aspect of the present invention, there is provided a system for classifying touch events, the system comprises a touch screen configured to display an interactive element; one or more acoustic sensors coupled to the touch screen; a touch event detector configured to monitor the one or more acoustic sensors and to save acoustic signals sensed by the one or more acoustic sensors, wherein the touch event detector is further configured to detect touch events in which the interactive element is touched by a first or a second finger part of a user, and wherein the touch events result in generating the acoustic signals; and an acoustic classifier configured to classify the acoustic signals.
The above and other objects and features of the present invention will become apparent from the following description of an exemplary embodiment given in conjunction with the accompanying drawings, in which:
The advantages and features of exemplary embodiments and methods of accomplishing these will be clearly understood from the following embodiments taken in conjunction with the accompanying drawings. However, the exemplary embodiments are not limited and may be implemented in various forms. It should be noted that the exemplary embodiments are provided to make a full disclosure and also to allow those skilled in the art to understand the full range of the exemplary embodiments. Therefore, the exemplary embodiments are to be defined only by the scope of the appended claims.
In brief, the concept of the present invention may be described as follow. A user touches or contacts a touch screen with one of the finger parts, e.g., a finger tip. Such a touch event triggers a primary or conventional functionality, such as opening a file, dragging a file, launching an application, selecting a song, pressing a button, and the like. When a user touches the touch screen with another one of the finger parts, e.g., a knuckle or fingernail, an auxiliary function is launched on the same touch screen.
Although one or more exemplary embodiments explain and illustrate that a finger tip causes to activate a primary function and a knuckle or fingernail causes to activate an auxiliary function, the embodiments do not limit thereto. It should be noted that different combination of these finger parts or the use of other finger parts can also activate the primary and auxiliary functions.
First,
As shown in
Referring to
In
In
As illustrated in
The computing system of the embodiment may have an operating system (OS), and can run various types of services or applications, known as apps. The computing system may also be equipped with a telecommunication capability that can allow connections to communication network. Such a computing system may include, but not limited to, a table top computer (e.g., Surface Computing), laptop computer, desktop computer, mobile computer, mobile internet device, mobile phone, smart-phone, PDA (Personal Digital Assistant), game console, portable media player, and the like.
Referring to
The touch screen 100 is an electronic visual display and serves also an input/output device supplementing or substituted for a keyboard, a mouse, and/or other types of devices. The touch screen 100 displays one or more interactive elements such as graphical representation for services or applications designed to perform a specific function on the computing system. Touching the interactive elements with the finger parts of a user, beyond the conventional tip of the finger, causes the OS 130 to activate the application or service related to the interactive elements. As shown in
The fingertip includes the fleshy mass on the palmar aspect of the extremity of the finger, as well as the finger sides up to the distal interphalangeal articulation. It also includes the very tip of the finger (i.e., the distal tip of the distal phalanx). However, the fingernail may not be included in an embodiment as part of fingertip definition, as this is an anatomically distinct feature and region.
The fingernail encompasses all parts of the keratin (or artificial nail material), horn-like envelope covering the dorsal aspect of the terminal phalanges of fingers. The knuckle includes the immediate areas surrounding the boney joints of human fingers, including joints on the thumb, and both major and minor knuckles. Specifically, the boney regions are within a 1 cm radius surrounding the metacarpophalangeal joints and interphalangeal articulations.
When an object strikes a certain material, vibro-acoustic waves propagate outward through the material or along the surface of the material. Typically, interactive surfaces use rigid materials, such as plastic or glass, which both quickly distribute and faithfully preserve the signal. As such, when respective finger parts touch or contact the surface of the touch screen 100, vibro-acoustic responses are produced. The vibro-acoustic characteristics of the respective finger parts are unique, mirroring their unique anatomical compositions. For example,
When a user wants to use an application or a service on the computing system, the user may touch an interactive element for the application or service with a finger part such as a fingertip, knuckle, or fingernail. Such a touch event results in the generation of a unique vibro-acoustic signal from the surface of the touch screen 100.
Referring back to
The OS 130 runs the computing system so that the primary function or the auxiliary function can be activated in line with the classification of the vibro-acoustic signals.
The vibro-acoustic classifier 120 includes a segmentation unit 122 to segment the vibro-acoustic signal into a digital representation; a conversion unit 124 to convert the digitized vibro-acoustic signal into an electrical signal; a feature extraction unit 126 derive a series of features from the electrical signal; and a classification unit 128 to classify the finger part using the features to distinguish what finger part was used for the touch event.
The segmentation unit 122 samples the vibro-acoustic signal, for example, at a sampling rate of 96 kHz, using a sliding window of 4096 samples of the vibro-acoustic signal. The conversion unit 124 then performs, for example, a Fourier Transform on sampled time-dependent vibro-acoustic signal to produce an electrical signal having frequency domain representation. For example, the Fourier Transform of this window may produce 2048 bands of frequency power.
The vibro-acoustic classifier 120 may further down-sample this data into additional vectors (i.e., buckets of ten), providing a different aliasing. In addition, additional time-domain features may be calculated from the vibro-acoustic signal, such as the average absolute amplitude, total absolute amplitude, standard deviation of the absolute amplitude, the center of mass for both the segmented input signal and the Fourier Transformed signal, and zero crossings.
The feature extraction unit 126 may also calculate a series of features from the frequency domain representation of the vibro-acoustic signals, such as the fundamental frequency of the impact waveform.
The classification unit 128 classifies the vibro-acoustic signal using the features to distinguish what finger part was used to generate the touch event, so that the computing system to selectively activate a primary function or an auxiliary function related to the interactive element depending on the classified vibro-acoustic signals.
To aid classification, the user can provide supplemental training samples to the vibro-acoustic classifier 120.
In one exemplary embodiment, the classification unit 128 may be implemented with a support vector machine (SVM) for feature classification. The SVM is a supervised learning model with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.
While the present invention has been shown and described with respect to the exemplary embodiments, the present invention is not limited thereto. It will be understood by those skilled in the art that various changes and modifications may be made without departing from the scope of the present invention as defined in the following claims.
This application is a continuation of application Ser. No. 14/612,089 filed on Feb. 2, 2015 having the title “METHOD AND APPARATUS FOR CLASSIFYING FINGER TOUCH EVENTS ON A TOUCHSCREEN”, which is a continuation of application Ser. No. 13/849,698 filed on Mar. 25, 2013 having the title “METHOD AND SYSTEM FOR ACTIVATING DIFFERENT INTERACTIVE FUNCTIONS USING DIFFERENT TYPES OF FINGER CONTACTS.”
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Number | Date | Country | |
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20180095595 A1 | Apr 2018 | US |
Number | Date | Country | |
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Parent | 14612089 | Feb 2015 | US |
Child | 15832080 | US |