2.1. The Tri-Axial Touch Sensor
Fabricated by Micro Electromechanical Systems (MEMs) technology, the tri-axial touch sensor is designed to output a three-dimensional force profile comprising of normal and shear forces. This sensor encompasses a discrete, tri-axial design by orienting three, silicone cantilevers perpendicular to one another in a novel and robust Printed Circuit Board (PCB) assembly. When the top of the sensor is loaded by touch, each cantilever responds to a direction of load. Because each cantilever is oriented perpendicular to one another, a three-dimensional force profile of touch can be acquired. In addition, the PCB is encapsulated with a polydimethylsiloxane (PDMS) mold that maintains load sensitivity and PCB visibility. Cantilever X is oriented to respond to shear loading across the x direction. Cantilever Y is oriented to respond to shear loading across the y direction. Both cantilevers collectively respond to shear loading when a user applies lateral forces to the top of the sensor. Cantilever Z is oriented to respond to normal loading perpendicular the x and y directions. Cantilever Z responds to normal loading in the z direction when one touches downward on the sensor .
A novel approach to sensing touch, this sensor is designed to such that the magnitude of directional load on each cantilever can be reported as a proportional output voltage. The higher a load is applied to a cantilever, the higher the voltage amplitude. Furthermore, the sensor and hardware interface outputs amplitudes with different polarities based on the direction of load, Hence, quantified touch can be reported with both magnitude and polarity. Each cantilever is dedicated to a channel such that three separate outputs can be connected to a data acquisition (DAQ) system, enabling individual capture of forces, in the x direction, Fx, in the y direction, Fy, and in the z direction, Fz, .
Conceptual illustration of the sensor’s axis and channel states (A) and its visual representation on the cube (B).
2.2. Data Acquisition
A PC laptop is used as a DAQ system and is connected to the sensor via a computer hardware interface comprising of an NI-DAQ USB 6210 from National Instruments, and a custom Wheatstone bridge circuit with amplification.
2.3. Data Visualization
We have classified the sensor’s three channels of force Fx, Fy and Fz as three pairs of directions: Forward-Back, Left-Right, and Up-Down . One direction from each pair is combined such that the total possible combination is eight directions. To represent each combination in virtual space, we subdivided a cube into eight equal, enumerated sections. Each section visually represents one of the eight possible states . For example, Forward-Left-Up is state ‘1’.
We have constructed this cube in software using the National Instruments LabVIEW Graphical Development Environment. This software allows the cube to be manipulated in three-dimensional space. A laptop displays our cube interface, an eight-section wireframe cube. Each section is colored with a unique color to be distinguishable among adjacent sections. When touched, the software provides spatial cues to visually communicate where the sensor was touched. As shown, a texture-map of state 7 is applied to visually communicate that the sensor was loaded in the “Back-Left-Down” direction . To accomplish this in real-time, the software repeatedly samples all three forces Fx
simultaneously, subtracts baseline offsets, and applies a linear transformation of the forces to minimize intra-channel crosstalk. The calibration coefficients, T
, from publication Shenshen Zhao, Yuho Li, et. al. were used to perform this linear transformation [12
Depiction of a force on the sensor and the visualization of the Cube Algorithm. A left-backward-down directional force is highlighted as state 7 of the cube.
Despite subtracting baseline offsets from forces Fx, Fy and Fz, the sensor exhibited some nominal activity due in part by the active components of the custom circuit. To account for this activity, three sets of minimum and maximum thresholds were defined to create a non-visible bounding box that would encapsulate a “not touched” state. Conducting preliminary, touch-loading experiments with the sensor, we defined this bounding box in milivolts. Conceptually located inside and at the center of the cube, this box, U, is defined as Ux-min = 50 mV, Ux-max = 310 mV on the x-axis; Uy-min = −50 mV, Uy-max = 180 mV on the y-axis; and Uz-min = −50 mV, Uz-max = 180 mV on the z-axis. The software samples all three forces Fx, Fy, and Fz simultaneously every 1 kHz to determine if all three forces are outside of this bounding box. If all three forces are outside of this box, the software considers the sensor touched, determines the sensor state and texture maps the associated section of the cube. When this section is no longer considered touched, the texture map is removed and the section’s wireframe is colored grey. At the end of a DAQ session, one can determine which sections remained untouched by observing which sections of the wireframe maintained its original color.
2.4. Data Acquisition - Proof of Concept Analysis
After several practice trials and with the audible aid of a metronome, a participant repeatedly rubbed the top of the tri-axial touch sensor with their fingertip in a shearing forward and back motion at approximately 1.5Hz for about 10 seconds. We captured this motion with the DAQ system.
2.5. Data Analysis – Force Profile Samples
This motion was stored in the DAQ system as a force profile. Using Matlab, we ran a Gaussian smoothing algorithm across this force profile to reduce noise [13
]. We then chose four random samples, subtracted baseline offsets of forces Fx
, applied the linear transformation, and defined each sample as a set of three amplitudes with vector points Vx
. The four samples were tallied as four vectors, each having Euclidean distances that we labeled as vector magnitudes in a table. Furthermore, we classified a direction of touch as a transition between two states. Given all four vectors, directional magnitudes from all pairs of transitions between states were calculated as Euclidean distances with points of origin at starting states of transition.
All common pairs of transitions between states were grouped together. Once grouped, we determined frequency of occurrence and average directional magnitude. We report our results in a table.