Patent Publication Number: US-2023161447-A1

Title: Flexible touch sensing system and method with deformable material

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/549,860, filed Dec. 13, 2021, which is a continuation of U.S. patent application Ser. No. 16/544,891, filed Aug. 19, 2019, which in turn claims priority to U.S. Provisional Patent Application No. 62/719,540 filed Aug. 17, 2018, all of which are hereby incorporated by reference in their entireties. 
    
    
     BACKGROUND 
     Stretchable soft sensors have been explored as promising input methods for adding interactions on both rigid and elastic physical objects, smart textiles, shape-changing surfaces, humanoids, and the human body. With a high flexibility and stretchability of the sensors, a wide scope of natural applications have been suggested. Still, the expensive and multi-step fabrication processes hinder production of inexpensive, customized soft sensors. Moreover, such sensors often cannot maintain localization of a sensing point when the material is deformed during a touch interaction. Therefore, improvements are needed in the field. 
     SUMMARY 
     By jointly emphasizing fabrication, multi-modality and novel computational methods, the present disclosure provides a single-volume soft-matter sensor that provides multimodal sensing and is able to support and restore contact localization upon and after deformation of the sensing material. The presently disclosed sensor and associated methods allow users to fabricate sensors inexpensively, customize interfaces easily, and deploy them instantly for continuous touch input. 
     In certain embodiments, the presently disclosed device utilizes carbon-filled liquid silicone rubber, a non-toxic piezoresistive elastomer material. The major hurdle in employing the carbon-filled silicone as an interaction input is the lack of real time sensing capability. This is mainly due to a rebound elasticity of the material, which causes a slow-recovery of the sensing signals after the material deformations that occur during an input event. In the present disclosure, an adaptive multi-sensing process is implemented using an electrical impedence tomography (EIT) process to achieve real-time contact localization and a learning-based support vector machine (SVM) to achieve deformation awareness. The disclosed system is therefore able to update contact localization in the presence of deformation of the sensing material. 
     By employing the EIT and SVM technique, the presently disclosed system enables a human touch to interface and interact with the sensor via electrodes placed on the material boundary only. In this way, the sensor can be fabricated in a single-volume manner and implemented without invasive wirings or electronics or other elements which have to be fabricated and placed in the interior of the material boundary. No interior elements are required, instead the material itself is used as a sensor. Using the disclosed method provides sensing contact localization and stretching within the sensor material. To this end, users are allowed to perform interactions instantly after deployment without any extra training processes. 
     According to various aspects, a system is provided, comprising a single volume soft sensor capable of sensing real-time continuous contact and stretching. A low-cost and an easy way to fabricate such piezoresistive elastomer-based soft sensors for instant interactions is also provided. An electrical impedance tomography (EIT) technique with SVM learning is employed to estimate changes of resistance distribution on the sensor caused by fingertip contact and determine contact localization even during a material deformation event. The EIT image reconstruction is processed with a difference in resistance measurement (.delta.V) which the difference between an instant measurement reading (V.sub.i) and a homogeneous baseline reading (V.sub.H). A deformation switch value is determined to maintain and restore the contact localization during and after the deformation event. When deformation occurs, the most recent .delta.V before the deformation event occurred is maintained and used during the deformation event. Upon release from the deformation, we updated the homogeneous baseline using.delta.V, where V.sub.H=V.sub.i−.delta.V. Using the presently disclosed method, the contact localization can be maintained during the deformation and restored after the deformation as shown in  FIG.  2 B . 
     This summary is provided to introduce the selection of concepts in a form that is easy to understand the detailed embodiments of the descriptions. The embodiments are then brought together in a final embodiment which described an environment, thereby stressing that each of the embodiments may be viewed in isolation, but also the synergies among them are very significant. This summary is not intended to identify key subject matter or key features or essential features thereof. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other objects, features, and advantages of various examples will become more apparent when taken in conjunction with the following description and drawings wherein identical reference numerals have been used, where possible, to designate identical features that are common to the figures, and wherein: 
         FIG.  1 A  illustrates a change in sensor output values without dynamic manipulation upon a deformation event. 
         FIG.  1 B  illustrates a change in sensor output values with dynamic manipulation upon a deformation event. 
         FIG.  2 A  illustrates a contact localization output for a stretching event without a deformation switch value incorporated. 
         FIG.  2 B  illustrates a contact localization output for a stretching event with a deformation switch value incorporated. 
         FIG.  3 A  illustrates a sensor activating the initial sensing electrode pair. 
         FIG.  3 B  illustrates a sensor activating a subsequent sensing electrode pair. 
         FIG.  4    is a process flowchart illustrating a touch sensing process according to one embodiment. 
         FIG.  5    is a process flowchart illustrating a baseline update process according to one embodiment. 
         FIG.  6    shows a schematic view of a 16-electrode system. 
         FIG.  7    illustrates a high-level diagram showing the components of a sensing system. 
     
    
    
     DETAILED DESCRIPTION 
     The term “drawings” used herein refers to drawings attached herewith and to sketches, drawings, illustrations, photographs, or other visual representations found in this disclosure. The terms “I,” “we,” “our” and the like throughout this disclosure do not refer to any specific individual or group of individuals. 
     Sensing performed by the presently disclosed system is based on an EIT technique with an SVM learning process which estimates the resistance distribution of the conductive material using inverse problem analysis based on measurements from the sensor boundary. The difficulty of providing real-time sensing with carbon-filled silicone rubber is due to the material&#39;s rebound elasticity (&gt;50%), which causes a long settling time (&gt;10 s) and small shifts in baseline values as shown in  FIGS.  1 A and  1 B .  FIG.  1 A  illustrates a change in sensor output values without dynamic manipulation upon a deformation event.  FIG.  1 B  illustrates a change in sensor output values with dynamic manipulation upon a deformation event. 
     The presently disclosed sensing method is based on carbon-filled liquid silicone rubber that changes its resistance distribution upon mechanical deformations. In one example, four-terminal sensing is used to measure resistance since this method reduces the inaccuracy from contact resistances. Unlike matrix tactile sensors where arrays of electrodes are required within the sensing area, the presently disclosed system utilizes sensing electrodes  204  and a capacitive channel  206  coupled to the outer edge of the sensor  202 . Then, a Neighboring Method is used where DC current is fed through two adjacent electrodes  202  and the voltage differential is measured successively throughout the adjacent electrode pairs as shown in  FIGS.  3 A and  3 B .  FIG.  3 A  shows the initial sensing electrode pair and  FIG.  3 B  shows the next successive electrode pair sensing. 
     According to one embodiment, EIT image reconstruction is carried out by comparing the measurements at two different instances. The update method comprises the following steps as shown in  FIG.  4   : 
     Raw sensor readings from sensors  204  are fed into EIT channel  404  and SVM channel  406 . 
     Before feeding the sensor values to the SVM classifier (block  408 ), differential dynamic manipulation (block  410 ) is applied when the sensor settlement enters the quasi-steady state, i.e., dV.sub.avg&lt;dV.sub.avg,threshold when V.sub.avg&lt;V.sub.avg,threshold. 
     The deformation type is classified and assigned a deformation identifier (block  413 ) using SVM with polynomial kernel (block  412 ). 
     If there is “No Deformation,” (block  414 ) the presence of deformation is confirmed in the previous frame (block  416 ). 
     If the deformation event exists in the previous frame, V.sub.H=V.sub.i−.delta.V is set to update the homogeneous baseline (block  418 ). Otherwise, EIT localization (block  420 ) is processed using the baseline process  500  of  FIG.  5    to update.delta.V (block  422 ) with the current Vi to localize a contact coordinate location in the sensor  202  (block  424 ). 
     If any deformation is detected, multiple channels are activated: 1).delta.V from the most recent localization during “No Deformation” is used (block  426 ) and a contact coordinate is outputted and 2) the system determines the level of the corresponding deformation (block  430 ) using SVM regression (block  428 ) with a polynomial kernel, with the regressed values mapped to corresponding deformation (block  432 ). 
       FIG.  5    shows a flowchart  500  which illustrates a baseline update process. First, if a contact is not detected, i.e., the capacitive sensing value cap, is less than a predetermined threshold cap.sub.threshold (stage  502 ), instant measurement readings (Vi) are set as a homogeneous baseline data (V.sub.H) (stage  504 ). If cap.sub.i&gt;=cap.sub.threshold, a movement detection is evaluated (stage  506 ). If div Vavg,i&gt;=div Vavg threshold, the system sets the previous frame&#39;s data (Vi−1) as VH (stage  508 ) and proceeds to perform an image reconstruction using EIT (stage  510 ). If div Vavg,i&lt;div Vavg threshold, the system directly proceeds to stage  410  and performs an image reconstruction using EIT. The system may optionally apply a color filter to the reconstructed image for blob detection and localize a contact coordinate from the center of the blob (stage  512 ) before outputting the contact position (x, y). 
     Throughout this description, some aspects are described in terms that would ordinarily be implemented as software programs. Those skilled in the art will readily recognize that the equivalent of such software can also be constructed in hardware, firmware, or micro-code. Because data-manipulation algorithms and systems are well known, the present description is directed in particular to algorithms and systems forming part of, or cooperating more directly with, systems and methods described herein. Other aspects of such algorithms and systems, and hardware or software for producing and otherwise processing signals or data involved therewith, not specifically shown or described herein, are selected from such systems, algorithms, components, and elements known in the art. Given the systems and methods as described herein, software not specifically shown, suggested, or described herein that is useful for implementation of any aspect is conventional and within the ordinary skill in such arts. 
       FIG.  7    is a high-level diagram showing the components of the exemplary system  1000  for analyzing the EIT location data and performing other analyses described herein, and related components. The system  1000  includes a processor  1086 , a peripheral system  1020 , a user interface system  1030 , and a data storage system  1040 . The peripheral system  1020 , the user interface system  1030  and the data storage system  1040  are communicatively connected to the processor  1086 . Processor  1086  can be communicatively connected to network  1050  (shown in phantom), e.g., the Internet or a leased line, as discussed below. The EIT data may be received using sensor  202  (via electrodes  204 ) and/or displayed using display units (included in user interface system  1030 ) which can each include one or more of systems  1086 ,  1020 ,  1030 ,  1040 , and can each connect to one or more network(s)  1050 . Processor  1086 , and other processing devices described herein, can each include one or more microprocessors, microcontrollers, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), programmable logic devices (PLDs), programmable logic arrays (PLAs), programmable array logic devices (PALs), or digital signal processors (DSPs). 
     Processor  1086  can implement processes of various aspects described herein. Processor  1086  can be or include one or more device(s) for automatically operating on data, e.g., a central processing unit (CPU), microcontroller (MCU), desktop computer, laptop computer, mainframe computer, personal digital assistant, digital camera, cellular phone, smartphone, or any other device for processing data, managing data, or handling data, whether implemented with electrical, magnetic, optical, biological components, or otherwise. Processor  1086  can include Harvard-architecture components, modified-Harvard-architecture components, or Von-Neumann-architecture components. 
     The phrase “communicatively connected” includes any type of connection, wired or wireless, for communicating data between devices or processors. These devices or processors can be located in physical proximity or not. For example, subsystems such as peripheral system  1020 , user interface system  1030 , and data storage system  1040  are shown separately from the data processing system  1086  but can be stored completely or partially within the data processing system  1086 . 
     The peripheral system  1020  can include one or more devices configured to provide digital content records to the processor  1086 . For example, the peripheral system  1020  can include digital still cameras, digital video cameras, cellular phones, or other data processors. The processor  1086 , upon receipt of digital content records from a device in the peripheral system  1020 , can store such digital content records in the data storage system  1040 . 
     The user interface system  1030  can include a mouse, a keyboard, another computer (connected, e.g., via a network or a null-modem cable), or any device or combination of devices from which data is input to the processor  1086 . The user interface system  1030  also can include a display device, a processor-accessible memory, or any device or combination of devices to which data is output by the processor  1086 . The user interface system  1030  and the data storage system  1040  can share a processor-accessible memory. 
     In various aspects, processor  1086  includes or is connected to communication interface  1015  that is coupled via network link  1016  (shown in phantom) to network  1050 . For example, communication interface  1015  can include an integrated services digital network (ISDN) terminal adapter or a modem to communicate data via a telephone line; a network interface to communicate data via a local-area network (LAN), e.g., an Ethernet LAN, or wide-area network (WAN); or a radio to communicate data via a wireless link, e.g., WiFi or GSM. Communication interface  1015  sends and receives electrical, electromagnetic or optical signals that carry digital or analog data streams representing various types of information across network link  1016  to network  1050 . Network link  1016  can be connected to network  1050  via a switch, gateway, hub, router, or other networking device. 
     Processor  1086  can send messages and receive data, including program code, through network  1050 , network link  1016  and communication interface  1015 . For example, a server can store requested code for an application program (e.g., a JAVA applet) on a tangible non-volatile computer-readable storage medium to which it is connected. The server can retrieve the code from the medium and transmit it through network  1050  to communication interface  1015 . The received code can be executed by processor  1086  as it is received, or stored in data storage system  1040  for later execution. 
     Data storage system  1040  can include or be communicatively connected with one or more processor-accessible memories configured to store information. The memories can be, e.g., within a chassis or as parts of a distributed system. The phrase “processor-accessible memory” is intended to include any data storage device to or from which processor  1086  can transfer data (using appropriate components of peripheral system  1020 ), whether volatile or nonvolatile; removable or fixed; electronic, magnetic, optical, chemical, mechanical, or otherwise. Exemplary processor-accessible memories include but are not limited to: registers, floppy disks, hard disks, tapes, bar codes, Compact Discs, DVDs, read-only memories (ROM), erasable programmable read-only memories (EPROM, EEPROM, or Flash), and random-access memories (RAMs). One of the processor-accessible memories in the data storage system  1040  can be a tangible non-transitory computer-readable storage medium, i.e., a non-transitory device or article of manufacture that participates in storing instructions that can be provided to processor  1086  for execution. 
     In an example, data storage system  1040  includes code memory  1041 , e.g., a RAM, and disk  1043 , e.g., a tangible computer-readable rotational storage device such as a hard drive. Computer program instructions are read into code memory  1041  from disk  1043 . Processor  1086  then executes one or more sequences of the computer program instructions loaded into code memory  1041 , as a result performing process steps described herein. In this way, processor  1086  carries out a computer implemented process. For example, steps of methods described herein, blocks of the flowchart illustrations or block diagrams herein, and combinations of those, can be implemented by computer program instructions. Code memory  1041  can also store data, or can store only code. 
     Various aspects described herein may be embodied as systems or methods. Accordingly, various aspects herein may take the form of an entirely hardware aspect, an entirely software aspect (including firmware, resident software, micro-code, etc.), or an aspect combining software and hardware aspects These aspects can all generally be referred to herein as a “service,” “circuit,” “circuitry,” “module,” or “system.” 
     Furthermore, various aspects herein may be embodied as computer program products including computer readable program code stored on a tangible non-transitory computer readable medium. Such a medium can be manufactured as is conventional for such articles, e.g., by pressing a CD-ROM. The program code includes computer program instructions that can be loaded into processor  1086  (and possibly also other processors), to cause functions, acts, or operational steps of various aspects herein to be performed by the processor  1086  (or other processor). Computer program code for carrying out operations for various aspects described herein may be written in any combination of one or more programming language(s), and can be loaded from disk  1043  into code memory  1041  for execution. The program code may execute, e.g., entirely on processor  1086 , partly on processor  1086  and partly on a remote computer connected to network  1050 , or entirely on the remote computer. 
     Those skilled in the art will recognize that numerous modifications can be made to the specific implementations described above. The implementations should not be limited to the particular limitations described. Other implementations may be possible.