High-Performance Interface Electronic System for a 13 ... - IEEE Xplore

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IEEE SENSORS JOURNAL, VOL. 13, NO. 11, NOVEMBER 2013

High-Performance Interface Electronic System for a 13 × 13 Flexible Biomechanical Ground Reaction Sensor Array Achieving a Gait Ground Velocity Resolution of 100 μm/sec Qingbo Guo, Michael A. Suster, Member, IEEE, Rajesh Surapaneni, Carlos H. Mastrangelo, Member, IEEE, and Darrin J. Young, Member, IEEE Abstract— This paper describes a high-performance interface electronics system design for a high-density flexible biomechanical ground reaction sensor array (GRSA). The prototype system can be incorporated into a personal boot heel to measure realtime ground force, shear strain, and sole deformation associated with a human bipedal locomotion, thus providing zero-velocity correction to an inertial measurement unit placed in a close proximity. This approach can greatly reduce inertial error accumulation and improve positioning accuracy. The sensing electronics consist of a front-end multiplexer that can sequentially connect individual sensing nodes from a 13 × 13 GRSA to a capacitanceto-voltage converter followed by a 12-bit algorithmic ADC with a sampling rate of 66.7 k-samples/s. The entire sensor array can be scanned within 10 ms. The GRSA employs capacitive sensing scheme and is fabricated using PDMS deformable dielectric layer. The integrated sensing electronics are fabricated in XFAB 0.35 µm CMOS process and dissipate 3 mW power. The overall system sensing resolution is limited by electrical interferences coupled through long interconnect traces between a GRSA and an electronic sensing module. Dynamic and static pressure testing shows the prototype system functionality achieving a gait ground velocity sensing resolution of 100 µm/s. Index Terms— Interface circuit, capacitance-to-voltage converter, sensor scanning electronics, capacitive sensor, pressure sensor, shear sensor, tactile sensor, flexible sensor, ground reaction sensor array, PDMS, PDMS elastic modulus.

I. I NTRODUCTION

G

PS-DENIED environments call for alternative position tracking solutions. MEMS technology has enabled system integration of miniature low-power inertial measurement units (IMU) based on accelerometers and gyroscopes. However, these devices suffer from an excessive output drift over Manuscript received April 11, 2013; accepted May 26, 2013. Date of publication June 10, 2013; date of current version October 2, 2013. This work was supported in part by the U.S. Defense Advanced Research Project Agency under Contract W31P4Q-08-C-0253. The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. The associate editor coordinating the review of this paper and approving it for publication was Dr. Patrick Ruther. Q. Guo, R. Surapaneni, C. H. Mastrangelo, and D. J. Young are with the Department of Electrical and Computer Engineering, the University of Utah, Salt Lake City, UT 84112 USA (e-mail: [email protected]; [email protected]; [email protected]; darrin.young@ utah.edu). M. A. Suster is with the Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2013.2267613

Pressure Contours

Centroids IMU

Heel strike

Foot flat

GRSA

Midstance

Pressure contour at t Pressure contour at t+Δt

Push off

Toe off

Fig. 1. Proposed personal navigation system employing high-resolution-gaitcorrected IMU.

time, thus inadequate for a long-term accurate position tracking. It was recently demonstrated that a personal navigation system could be developed by employing high-resolution-gaitcorrected IMUs [1]. The system combines a commercial IMU with a flexible error-correcting biomechanical ground reaction sensor array (GRSA). The IMU and GRSA are placed within the heel of a personal boot as depicted in Figure 1, where the IMU can measure inertial information while the biomechanical GRSA independently measures dynamic ground force, shear strain, and sole deformation associated with a ground locomotion gait. In a human bipedal locomotion, the walking mode or gait consists of two separate phases as shown in Figure 1. In the swing phase, the leg is off the ground. This period extends from the instant the toe leaves the ground until the heel strikes. In the stance phase, the foot heel first contacts the ground, then it rolls until the mid-stance is reached, resulting in a pivoting of the leg on the ankle and a corresponding forward motion of the body. Beyond mid-stance, detachment of the foot takes place through a gradual rolling [2]. Extensive research on gait and posture kinetics has been reported [3]–[5]. It is evident that only during a fraction of the mid-stance the velocity of the heel is zero [6]. This critical information can be detected by measuring pressure contours

1530-437X © 2013 IEEE

GUO et al.: HIGH-PERFORMANCE INTERFACE ELECTRONIC SYSTEM FOR A 13 × 13 GRSA

II. H IGH -D ENSITY F LEXIBLE B IOMECHANICAL G ROUND R EACTION S ENSOR A RRAY A high-density flexible biomechanical ground reaction sensor array consisting of 13 × 13 individual sensing nodes is designed for improving IMU positioning accuracy. Capacitive sensing scheme is employed for the array implementation. Figure 2 presents a schematic of the prototype design, where each sensing node can be accessed by the corresponding row and column connections. The sensor array area of 53 mm × 57 mm is selected to match with a typical boot heel size. For navigation applications, in addition to the normal pressure sensing along the vertical axis it is desirable to detect shear force for determining slippage and shoe rotation estimation. Therefore, a combined capacitive pressure and shear stress sensing scheme is incorporated inside each sensing node as depicted in Figure 3 [12].

53mm

57mm

Rows

Fig. 2.

Columns

Schematic of a high-density flexible biomechanical GRSA. 4.28mm

Floating Electrode CS-X+

D1

X

CS-X-

4.16mm

or contours centroid movement captured by a GRSA placed between the heel and insole of a shoe and in turn provides zero-velocity correction to the IMU, thus drastically reducing inertial error accumulation and improving positioning accuracy. Step-corrected IMUs (also known as dead reckoning) integrated with GPS navigation systems have been demonstrated [7]–[10]. However, these systems detect the step impact shock by employing accelerometers placed away from the ground. This approximate detection technique typically results in a large positioning error around 1-2% of the distance traveled. In our proposed architecture, a data-rich high-resolution GRSA is placed in the heel close to ground contact, therefore providing detailed accurate contact information to an IMU located in a close proximity. This extra contact information and the close proximity between the GRSA and IMU are the key to achieve a highly accurate position tracking performance [11]. To verify the concept, a prototype system incorporating a commercial insole-shaped pressure sensor array was implemented with an IMU mounted externally to a boot near its heel. The pressure sensor array consists of 99 sensing elements, where 54 elements were located in the heel portion [1]. A loopclosing walk test over 30 minutes was performed by using the prototype system with the necessary signal conditioning and processing algorithms. The field test demonstrated a position error less than 4 meters [1]. To further improve the positioning accuracy for an extended application time, a GRSA with an increased density is required. More data points available from a high-density array are expected to detect much smaller contact pressure profile change during the stationary contact of the heel, thus improving the accuracy of zerovelocity estimation during the mid-stance phase. Section II of the paper describes a high-density flexible ground reaction sensor array. The interface electronic system design for the sensor array is illustrated in Section III. Section IV shows the overall prototype system integration. Section V presents the characterization of electrical interferences associated with the prototype system and measurement results obtained from static and dynamic pressure and shear testing with conclusions given in Section VI.

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CS-Y+

D2

Sensing Electrode

Driving Electrode

(a)

Y

S1

CS-Y-

S2

(b)

Fig. 3. (a) Floating-electrode-based capacitive sensing architecture and (b) Layout of a sensing node detecting z-axis pressure and x/y axes shear.

Figure 3(a) presents a 3D model of a floating-electrodebased capacitive sensing architecture, consisting of a sensing electrode, a driving electrode and a floating electrode embed in the soft deformable dielectric material such as PDMS. The floating electrode covers the entire sensing electrode and approximately 70% of the driving electrode. The sensing capacitance is formed between the driving and sensing electrodes through the coupling of the floating electrode. Figure 3(b) shows a layout of a sensing node, which employs the sensing architecture described in Figure 3(a) to detect z-axis pressure and x/y-axes shear. The overlapping area between the floating electrode and the driving electrode can change in response to an applied shear. With a mirrored structure placed adjacently, an applied shear along the x-axis (or yaxis) will result in a differential capacitance change, modeled as CS−X+ and CS−X− (or CS−Y+ and CS−Y− ). Parallel sensing fingers are employed to enhance the shear sensitivity. The soft dielectric layer will shrink under an applied vertical pressure. Thus, combining CS−X+ and CS−X− (or CS−Y+ and CS−Y− ) together can result in an overall sensor capacitance increase independent of the shear effect for a well matched sensor design. Figure 4 presents an electrical model of a GRSA sensing node, where D1 and D2 represent the driving terminals and S1 and S2 represent the sensing terminals of a sensing node, matching the schematic shown in Fig. 3(b). The sensor terminals can be dynamically reconfigured by switches to achieve differential (x/y-axes) shear sensing and single-

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Integrated Circuit

13X13 GRSA

CS-X+

CLK Ctrl Prog Data

S1 D1 Sensor Output 26

S2 -

CS-X CS-Y+

MUX

Sensor 26 Drive

D2

ADC

Digital Timing & Control

Digital Output ...00101011...

Drive Ckts

CS-Y-

Fig. 4.

C/V

MUX

Electrical model of a GRSA sensing node.

Fig. 6.

Architecture of interface electronic system with GRSA. C/V Converter ΦRST CI2 Sensor Cs+

Φ1 Vs Φ2

LTrace

Cp+

1

2

Cp

MUX ΦSel ΦX/Y/ΦZ

LTrace

ΦZ

LTrace

ΦSel

ΦX/Y ΦZ

Cref LTrace

Photo of fabricated ground reaction sensor array.

ended (z-axis) normal pressure sensing, as will be described in Section III. The sensor array size of 13 × 13 is chosen to yield a sensor pitch size of approximately 4 mm as a compromise between the array wiring complexity and electronic system dynamic range required for a ground velocity sensing resolution. The prototype design calls for a dynamic range of 66 dB to achieve a velocity resolution around 10 μm/sec over a processing time of 200 msec, which is sufficient for the intended IMU zero-velocity correction. The sensor array was fabricated by using a flexible kapton layer embedding the sensing and driving electrodes, followed by spin coating a 15 μm thick PDMS layer. The top floating electrodes are formed by depositing and patterning a layer of 20 nm Cr/150 nm Au [12]. Each floating electrode overlaps its sensing and driving electrodes with an area of 1.8 mm2 and 1.2 mm2 , respectively. The dielectric layer between floating electrodes and sensing/driving electrodes consists of a 25 μm thick kapton and 15 μm thick PDMS. The kapton is much stiffer than PDMS. It is, therefore, assumed all deformation occurs in the PDMS layer under an applied pressure and shear. Finite element simulation reveals a capacitance of approximately 0.6 pF between a pair of sensing and driving electrodes, 70% of which is contributed by the coupling capacitance through the floating electrode and the remaining is due to fringing capacitance. This results in a nominal sensor capacitance value of approximately 0.6 pF and 1.2 pF for the differential (x/y-axes) and single-ended (z-axis) operations, respectively. Analysis also indicates a maximum capacitance change of 10 and 1 percent for the z-axis and x/y-axes operations, respectively, under a maximum load. Figure 5 shows a photo of a fabricated 13 × 13 flexible ground reaction sensor array along with a close view of an individual sensing node.

CFB

OCMFB

Vout

CpCs-

Fig. 5.

ΦZ CI1

Cp-ref Programmable Ref. Caps.

Fig. 7.

CFB CI1

VICM ICMFB

CI2

ΦZ ΦRST

Front-end interface electronic design architecture.

Impedance measurement of isolated sensing nodes reveals a nominal capacitance value of approximately 0.8 pF for CS−X+ , CS−X− , CS−Y+ and CS−Y− . The additional capacitance is likely contributed by a reduced dielectric layer thickness in the fabrication process and residual parasitic capacitance associated with the measurement setup. III. I NTERFACE E LECTRONICS S YSTEM D ESIGN An interface electronic system is designed for the prototype ground reaction sensor array. Figure 6 presents the interface electronic system design architecture, consisting of a frontend multiplexer that can sequentially connect 169 individual sensing nodes in a 13 × 13 sensor array to a capacitance-tovoltage (C/V) converter followed by a 12-bit ADC, a digital timing & control unit, and a driving circuitry. A 1.6 MHz clock and control programming data are sent to the interface circuit for a desired operation. Each GRSA sensing node can be accessed by the corresponding row and column connections. Figure 7 presents the front-end interface electronic circuit topology, where different switching schemes are used to conduct z-axis pressure and x/y-axes shear measurements. A pair − of differential sensing capacitors, C+ S and CS , from the GRSA electrical model shown in Figure 4, is used to model a sensing node for simplicity. A 10 msec array scanning time is chosen as a trade-off between power dissipation and number of frames needed to estimate a gait ground velocity, thus allocating 60 μsec to complete a sequential z, x, z and y sensing for each sensing node. A selected sensing node can be configured in differential or single-ended mode by closing xy or z switches accordingly to sense its shear stress or normal pressure. In a differential


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VDD

VDD M2 Vout-p

M1

M0 GND

VDD M3 Mc

Vout-n CC

Vbias-1 M4

M17 M13

Vb2

Vin-p

Mc CC

Vcmfb2

M16 M20

M12

M8

M9

M5 Vbias-2 M7

M6

M10

M14 M18

CV

ADC

Digital Timing Programming Control

Vcmfb2

GND

Fig. 8.

Programmable Ref Capacitors Stimulation Buffers

13 Pads for GRSA Drive

Vin-n

Vcmfb1

MUX

M11

M19 M15

13 Pads for GRSA Drive

Vb2

26 Pads for GRSA Outputs

Fig. 9.

Fabricated chip micrograph.

Two-stage amplifier design architecture.

mode, the circuit detects the capacitance difference between − C+ S and CS , which represents the shear stress along x or y axis. − In a single-ended mode, C+ S and CS are combined together and compared with an on-chip programmable reference capacitor, Cref . The capacitance difference thus represents the normal pressure information. The Cref is designed with a 6-bit programmable capability covering a range from approximately 62 fF to 4 pF, which is critical for closely matching to the nominal sensor capacitance value of the array to effectively suppress amplifier output offset voltage. The programmable feature enables the electronic system to be interfaced with a sensor array exhibiting a wide range of nominal sensor capacitance. A 5-bit programmable parasitic reference capacitor, Cp−ref , is also designed on-chip to improve the matching with respect to the sensor parasitic capacitance, which is mainly composed of capacitances from un-selected sensing nodes on a selected sensing column. This technique is important for suppressing potential ground interference amplification caused by the amplifier input parasitic capacitance mismatch, as will be described in section V. A symmetric GRSA layout technique is employed to improve capacitance match under a dynamic locomotion [13], [14]. The GRSA exhibits an inherent complex wiring and is connected to the interface electronic system through long interconnect traces, which introduce inductances modeled as Ltrace shown in Figure 7. These inductances can act as antennas to couple system clock edge transitions as interference signals to various locations, thus limiting system performance. An effective electrical shielding, therefore, becomes a critical part of the overall system integration to minimize interference coupling. The capacitive sensor is interfaced with a switchedcapacitor-based fully differential capacitance-to-voltage converter shown in Figure 7. The C/V converter is designed to alternatively operate between single-ended mode and differential mode to sequentially scan each sensor node in an array. A stimulation voltage, Vs , with an amplitude of 2 V is applied to the sensor, converting the capacitance difference to an output voltage, Vout , through the amplifier integrating capacitor. The switchable integrating capacitor, CI2 , is connected in parallel with CI1 in the single-ended mode. CI1 and CI2 are designed to be 0.7 pF, thus resulting in an integrating capacitor of 1.4 pF for the single-ended mode and 0.7 pF for the differential mode. The C/V converter output voltage for the

single-ended mode and differential mode can be expressed in Equations (1) and (2), respectively, CS −Cref CI1 +CI2 C+ −C− S = VS ∗ S CI1

VOut−SE = VS ∗

(1)

VOut−Diff

(2)

− where CS is the sum of C+ S and CS along the x or y axis. Low frequency noise such as 1/f noise is typically the dominant noise source in interface electronic system design. This noise along with amplifier DC input offset, however, can be effectively suppressed by employing chopper stabilization or correlated-double-sampling (CDS) technique [15]. A CDS stage with a gain of 2.5 is designed for the prototype system due to its compatibility with switched-capacitor circuits design and low power dissipation [16]. A fully differential amplifier consisting of a cascode transconductance stage followed by a Class A/B stage is chosen for the C/V converter design for its high gain, low noise and low power dissipation as shown in Figure 8. The amplifier is designed to achieve an open-loop DC gain of 129 dB and a unity-gain frequency of 11.5 MHz resulting in a closed-loop bandwidth of approximately 300 kHz with a phase margin of 64 degrees, which satisfy the array scanning speed and settling requirements while dissipating 160 μA from a 3 V supply. Switched-capacitor output common-mode feedback technique is applied in each stage to ensure a proper operation. The √ amplifier input-referred noise of 7 nV / H z is designed to achieve a system output noise of 0.5 mVrms , corresponding to a dynamic range of 70 dB with a capacitance sensing resolution of approximately 140 aF [13], [14]. A12-bit analog-to-digital converter (ADC) sampled at 66.7 k-samples per second is designed to digitize the output signal from the front-end CDS C/V converter. The 12-bit dynamic range is chosen to ensure a certain system design safety margin. A ratio independent algorithmic ADC is chosen because it can achieve the required resolution without calling for well-matched capacitors while consuming a small area and power dissipation [16].

IV. P ROTOTYPE S YSTEM I NTEGRATION The electronics were fabricated in XFAB 0.35 μm CMOS process. Figure 9 presents the fabricated chip micrograph


57mm

50mm

4500

13X13 GRSA

53mm

Flexible Interconnect Cable

Custom-Designed ASIC Readout Board at 1.6Mbit/s & 10 ms/Array Scan

Fig. 10. Interface electronics with fabricated flexible biomechanical ground reaction sensor array.

V. P ROTOTYPE S YSTEM M EASUREMENT R ESULTS Due to the long interconnect traces between the ground reaction sensor array and interface electronic system, electrical interferences can become a dominant performance limiting factor. Figure 11 shows measured time-domain waveforms of the stimulation voltage (Vs ) and C/V converter output exhibiting strong interferences, some of which can be rejected as common-mode signals to the first order. The non-commonmode interferences, however, will remain. The two Vs pulses shown in the figure are used to scan a particular sensing node for its normal pressure and shear stress responses. The electrical interference is mainly caused by three contirbutors consisting of (1) drive line interference at node 1, (2) ground interference at node 2, and (3) interference between the amplifier +/− input terminals. Figure 12 presents a simplified electrical interface circuit diagram illustrating the interference sources, which cause output signal uncertainty. An extensive electrical interference characterization was performed [17], revealing a drive line interference of approximately 20 mV, a ground interference around 1-2 mV, and amplifier input interference level of approximately 450 μV and 250 μV for the single-ended mode and differential mode, respectively.

Fig. 11.

Measured waveforms of Vs and C/V converter output.

CI

1 VDrive_Dist

Cs+

3 VAmp_Dist

Cp+

VOut

Vs Cs-

CpVGND_Dist

CI

2 Fig. 12.

Test configurations for characterizing interference at each node.

5

Output Interference (mV)

occupying 3.5 mm × 2.2 mm area. The ASIC uses 26 driving pads and 26 sensing pads to interface with the GRSA. The remaining pads are employed for acquiring a number of system outputs, characterizing various on-chip building blocks as well as system programming and reconfiguration. The ASIC dissipates 1 mA DC current from a 3V supply. The ASIC is then mounted into a socket on a customdesigned PCB and connected to a prototype flexible highdensity ground reaction sensor array as shown in Figure 10. For illustration purpose, the sensor array shielding layers are not included in the figure. In practice, two shielding layers, each consisting of a 2 mm-thick fabric as a dielectric layer and a grounded thin aluminum foil, are employed to sandwich the ground reaction sensor array. The fabric thickness is chosen to minimize parasitic capacitances from the sensor array, critical for maintaining the designed system closed-loop bandwidth and noise performance. The GRSA-ASIC system is further connected with a peripheral interface unit to ensure a proper operation and communication with an external computer for data processing and analysis.

X/Y-Axes Z-Axis

4 3 2 1 0

0 1 Amplifier Input Interference Fig. 13.

2 Line Interference 3 4 Drive with 0.1pF ΔCZ or ΔCX/Y

5 Interference 6 Ground with 2.5pF ΔCP

Interference contribution due to different sources.

Figure 13 summarizes the effect of different interference signals for the single-ended (z-axis) and differential (x/y-axes) modes, where CX/Y represents the sensor capacitance difference in the differential mode, CZ reprents the difference between the sensor capacitance in the single-ended mode and an on-chip reference capacitor, Cref , shown in Figure 7, and CP is the difference between the parasitic capacitances associated with the amplifier input terminals. It is evident that the performance of the single-ended (z-axis) mode is dominantly limited by the amplifier input interference.


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0.44 0.42

Strain

0.4 0.38 0.36 0.34 0.32

0

Fig. 16. Fig. 14. Test setup for characterizing GRSA under applied normal and shear forces.

Vout(mV)

700 650

Measured strain vs. normal pressure.

6 10

5 10

600 4 10 0

550 500 0

Fig. 15.

800

7 10 Young’s Modulus (Pa)

750

200 400 600 Normal Pressure (KPa)

200 400 600 Normal Pressure (KPa)

800

A GRSA sensing node output voltage vs. normal pressure.

In the differential (x/y-axes) mode, all interferences exhibit a comparable effect. After having characterized the electrical interferences from the GRSA-ASIC system, individual sensing nodes from the sensor array were tested under normal pressure and shear stress. Figure 14 depicts the testing setup, where a prototype GRSA is adhered to the surface of a testing apparatus. Two screw gauges are rotated to generate vertical and lateral displacement for a L-shape beam, which directly applies normal and shear forces to a selected sensing node. The applied normal and shear forces can be detected by the built-in vertical and lateral load sensors. Given a well-defined sensing node area, the corresponding normal pressure and shear stress can be obtained. Assuming an individual carrying equipment weighs approximately 100 kg with a heel area of 30 cm2 , the maximum normal pressure applied on a GRSA inserted into a boot during a normal walking condition is thus around 320 KPa. It is expected that the normal pressure can be doubled during jumping, climbing and running. Therefore, the prototype GRSA is tested under a pressure up to 800 KPa. Figure 15 presents a measured output voltage versus applied normal pressure from a typical sensing node in a GRSA. The output voltage increases with the pressure initially and then reaches saturation. Based on the output voltage change, the PDMS layer thickness shrinkage can be calculated, thus

Fig. 17.

200 300 400 600 Normal Pressure (KPa)

800

PDMS Young’s modulus vs. normal pressure.

obtaining the relationship between strain and pressure as plotted in Figure 16. The plot suggests a highly nonlinear elastic modulus of the hyper-elastic PDMS material as a function of applied pressure shown in Figure 17. It is evident that the Young’s modulus of PDMS increases quickly with applied pressure in its compression mode. The Young’s modulus is approximately 20 KPa at a low pressure level of 8 KPa and can reach about 1.9 MPa when the pressure reaches 800 KPa. During a mid-stance phase under a normal walking condition, the normal pressure on the GRSA is estimated around 320 KPa, corresponding to a Young’s modulus of 760 KPa, which is in agreement with previous published data [18], [19]. However, in order to detect a small pressure contour change or contour centroid movement to capture zero-velocity information associated with a human bipedal locomotion, it is necessary to sense small pressure variation centered around the nominal pressure bias point of 320 KPa, which calls for calculating the slope (or small-signal sensitivity) of the measured strain versus pressure plot shown in Figure 16. Figure 18 presents the resulting sensitivity plot, revealing that the PDMS small-signal elastic modulus is approximately 9 MPa around a normal pressure bias point of 320 KPa. This large elastic modulus will produce a much reduced sensor capacitance change compared to the estimation obtained by using the elastic modulus value of 760 KPa derived from the conventional approach. The reduced sensor capacitance change will call for an enhanced electronic system dynamic range; hence an increased power dissipation. A real-time intelligent calibration technique to offset the nominal pressure bias effect


Fig. 18.

9 10

0.5

8 10

0.4

Shear Strain

Small-Signal Young’s Modulus (Pa)

4502

7 10 6 10

0.3 0.2 0.1

5 10 4 10

0 0

0

200 300 400 600 Normal Pressure (KPa)

20

40 60 80 Shear Stress (KPa)

100

120

800 Fig. 20.

PDMS small-signal Young’s modulus vs. normal pressure.

Measured shear strain vs. shear stress.

400

Shear Modulus (KPa)

350 300 250 200 150 100 0

Fig. 21. Fig. 19.

20

40 60 80 Shear Stress (KPa)

100

120

PDMS shear modulus vs. shear stress.

Output voltage profile from a sensing node under shear force.

can be considered to maintain the system performance requirements without increasing power dissipation. It is also expected that PDMS material composition can be adjusted to increase to device sensitivity around the nominal pressure bias point. The shear test was performed using the same setup depicted in Figure 14. The GRSA is to be inserted in a heel, which requires a smooth surface, thus precluding the use of bumps or similar structures to apply shear stress. The shear stress is, therefore, applied through the frictional force between the sensor array surface and the tip of the L-shape beam. A shear stress is typically around 40% of the normal pressure exerted on a heel. Therefore, the maximum shear stress is limited to 130 KPa for the testing. An increased shear stress could cause slippage between the array surface and the L-shape beam tip. Figure 19 shows a measured x/y-axes output voltage profile from a particular sensing node under an applied shear force of 11.6 N, corresponding to a shear stress of approximately 110 KPa. The lateral axes of the plot represent the sensing nodes location in a 13 x 13 ground reaction sensor array. It should be noted that any initial as-fabricated offset capacitance value between CS−X+ and CS−X− and between CS−Y+ and CS−Y− will manifest itself as an output voltage in the measured shear response due to the normal pressure effect. To eliminate this undesirable effect, the voltage was first measured under an applied normal pressure as a reference signal and then subtracted from the measured shear response. Therefore, a well-matched sensor design and fabrication are critical for avoiding this calibration step as well as minimizing interference from shear stress to normal pressure output

Screw Gauge Load Sensor

Soft Ball

GRSA

Fig. 22. Test setup for emulating GRSA pressure profile under normal walking condition.

response. Figure 20 plots the measured relationship between shear stress and strain, exhibiting a nearly linear characteristic. The resulting shear modulus versus shear stress is plotted in Figure 21, revealing an average shear modulus of 240 KPa over the testing range. The GRSA shear sensitivity, defined as (C/Cs )/shear stress, is approximately 0.08 MPa−1 . The shear sensitivity can be further improved by minimizing the GRSA interdigitated electrodes width. This, however, demands a precision photolithography alignment in the fabrication process. To emulate a standing heel pressure profile under a walking condition, a normal force is applied onto a soft ball positioned over a ground reaction sensor array as depicted in Figure 22. An increased normal force will deform the soft ball, result-


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Centroid Position(mm)

29.7

29.65

~60µm 29.6

29.55 0

Fig. 23. GRSA output voltage profile under different normal force applied on the soft ball. Time: 0.5 s

Time: 1.28 s

Time: 3.5 s

Time: 4.27 s

Time: 5.3 s

Time: 6.1 s

Fig. 24.

Constructed GRSA pressure contours.

ing in an enlarged stress and contact area over the GRSA. Figure 23 presents the measured GRSA z-axis output voltage profiles under different applied normal forces at 1N, 5N, 30N and 70N, respectively. The lateral axes of the plot represent the sensing nodes location in a 13 × 26 ground reaction sensor array. It can be seen that the GRSA output voltage level and contact area increase with the applied force. The output voltage saturates as the PDMS enters into saturation region at large force level, confirming the measurement results demonstrated in Figure 15. A dynamic test by manually rolling a test ball over a GRSA surface was also conducted. The pressure contours and contours centroid position were constructed from the GRSA z-axis output signals and are displayed as a function of time shown in Figure 24. The plots indicate the movement of pressure contours and their corresponding centroid positions following the movement of the test ball. It is necessary to determine the accuracy of a GRSA pressure contour centroid in order to calculate the achievable gait ground velocity sensing resolution. The test setup depicted in Figure 22 was employed for this characterization, where a constant static normal pressure was applied to a soft ball

Fig. 25.

5

10 Time (s)

15

20

GRSA pressure contour centroid position variation over time.

positioned over a GRSA. The resulting GRSA z-axis output voltage signals were used to construct a pressure contour centroid determined by the following expressions, nx ny (Vx y ∗ l x ) 1 1 (3) X centroid = Vx y ny nx (Vx y ∗ l y ) 1 1 Ycentroid = (4) Vx y where x and y represent the coordinates of a GRSA, Vx y is the output voltage from a sensing node located at x and y location, l x and l y represent the distance of a particular point from origin along the x and y axes, respectively, n x is the number of columns, and n y is the number of rows in a GRSA. The expressions indicate that interference signals will limit the pressure contour centroid accuracy. Figure 25 plots the corresponding centroid position variation over a 20-second time frame. It should be noted that each data point in the plot presents an average value of 20 frames, corresponding to a time interval of 200 msec, which is adequate for estimating zero-velocity during a mid-stance of a human bipedal locomotion. The centroid position variation is determined to be 20 μmRMS , thus corresponding a gait ground velocity sensing resolution of 100 μm/sec. The demonstrated performance is limited by the electrical interference from the overall sensing system and is adequate for conducting IMU zero-velocity correction. VI. C ONCLUSION A high-performance interface electronic system has been designed and demonstrated with a flexible high-density ground reaction sensor array. The electrical interference coupling caused by the long interconnect traces in the prototype design limits the system sensing resolution. It is found that the PDMS dielectric layer employed in the prototype GRSA is biased in saturation region under a nominal pressure bias point around 320 KPa, equivalent to a pressure exerted on a heel during a normal walking condition. Therefore, it is critical to reduce

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the system interference and noise in order to sense small pressure variation around the bias point for achieving the accuracy requirement of zero-velocity estimation. A real-time intelligent calibration technique to offset the nominal pressure bias effect can be considered to satisfy the system performance requirements without excessively increasing power dissipation. It is also expected that PDMS material composition can be adjusted to increase to device sensitivity around the nominal pressure bias point. A well-matched sensor design and fabrication between CS−X+ and CS−X− and between CS−Y+ and CS−Y− is crucial for minimizing cross interference between the shear and normal pressure responses. A static pressure testing through a soft ball positioned over a prototype GRSA indicates a pressure contour centroid position uncertainty of 20 μmRMS averaged over 200 msec, thus corresponding a gait ground velocity sensing resolution of 100 μm/sec, adequate for conducting IMU zero-velocity correction. R EFERENCES [1] O. Bebek, M. A. Suster, S. Rajgopal, M. J. Fu, X. Huang, M. C. Cavusoglu, D. J. Young, M. Mehregany, A. J. van den Bogert, and C. H. Mastrangelo, “Personal navigation via high-resolution-gait-corrected inertial measurement units,” IEEE Trans. Instrum. Meas., vol. 59, no. 11, pp. 3018–3027, Nov. 2010. [2] M. Trew and T. Everett, Human movement: An Introductory Text, 4th ed. The Netherlands: Elsevier Health Sci., 2001. [3] D. T.-P. Fong, Y.-Y. Chan, Y. Hong, P. S.-H. Yung, K.-Y. Fung, and K. M. Chan, “Estimating the complete ground reaction forces with pressure insoles in walking,” J. Biomech., vol. 41, no. 11, pp. 2597–2601, Aug. 2008. [4] H. B. Kitaoka, X. M. Crevoisier, D. Hansen, B. Katajarvi, K. Harbst, and K. R. Kaufman, “Foot and ankle kinematics and ground reaction forces during ambulation,” Foot Ankle Int., vol. 27, no. 10, pp. 808–813, Oct. 2006. [5] W. Tao, T. Liu, R. Zheng, and H. Feng, “Gait analysis using wearable sensors,” IEEE Sensors J., vol. 12, no. 2, pp. 2255–2283, Dec. 2012. [6] H. Lanshammar and L. Strandberg, “Horizontal floor reaction forces and heel movements during the initial stance phase,” in Proc. 8th Int. Congr. Biomech., vol. B. Nagoya, Japan, Jul. 1983, pp. 1123–1128. [7] J. Kim, J.-G. Lee, G.-I. Jee, and T. K. Sung, “Compensation of gyroscope errors and GPS/DR integration,” in Proc. IEEE Position Location Navigat. Symp., Apr. 1996, pp. 464–470. [8] W.-W. Kao, “Integration of GPS and dead reckoning navigation systems,” in Proc. IEEE Vehicle Navigat. Inf. Syst. Conf., vol. 2. Oct. 1991, pp. 635–643. [9] E. S. Sazonov, T. Bunpus, S. Zeigler, and S. Marocco, “Classification of plantar pressure and heel acceleration patterns using neural networks,” in Proc. IEEE Neural Netw. Conf., vol. 5. Aug. 2005, pp. 3007–3010. [10] C. Randell, C. Djiallis, and H. Muller, “Personal position measurement using dead reckoning,” in Proc. IEEE Int. Symp. Wearable Comput., Oct. 2005, pp. 166–173. [11] J. Cheung, M. Zhang, A. Leung, and Y. Fan, “Three-dimensional finite element analysis of the foot during standing - a material sensitivity study,” J. Biomech., vol. 38, no. 5, pp. 1045–1054, 2005. [12] R. Surapaneni, Y. Xie, Q. Guo, D. J. Young, and C. H. Mastrangelo, “A high-resolution flexible tactile imager systembased on floating comb electrodes,” in Proc. IEEE Sensors Conf., Oct. 2012, pp. 208–211. [13] M. A. Suster, C. H. Mastrangelo, and D. J. Young, “Low-interference sensing electronics for high-resolution error-correcting biomechanical ground reaction sensor cluster,” in Proc. IEEE Sensors Conf., Jan. 2010, pp. 1020–1023. [14] M. A. Suster, C. H. Mastrangelo, and D. J. Young, “Electronic detection system design for high-resolution error-correcting biomechanical ground reaction sensor cluster,” in Proc. 8th Int. Conf. Netw. Sens. Syst., Jun. 2011, pp. 1–4. [15] C. C. Enz and G. C. Temes, “Circuit techniques for reducing the effects of op-amp imperfections: Autozeroing, correlated double sampling, and chopper stabilization,” Proc. IEEE, vol. 84, no. 11, pp. 1584–1614, Nov. 1996.

[16] P. Cong, N. Chaimanonart, W. H. Ko, and D. J. Young, “A wireless and batteryless 10-bit implantable blood pressure sensing microsystem with adaptive RF powering for realtime genetically engineered mice monitoring,” IEEE J. Solid-State Circuits, vol. 44, no. 12, pp. 3631–3644, Dec. 2009. [17] Q. Guo, M. A. Suster, R. Surapaneni, C. Mastrangelo, and D. J. Young, “Characterization of electrical interferences for ground reaction sensor cluster,” in Proc. IEEE Sensors Conf., Oct. 2012, pp. 596–599. [18] J. C. Lotters, W. Olthuis, P. H. Veltink, and P. Bergveld, “The mechanical properties of the rubber elastic polymer polydimethylsiloxane for sensor applications,” J. Micromech. Microeng., vol. 7, no. 3, pp. 145–147, 1997. [19] M. Liu, J. Sun, Y. Sun, C. Bock, and Q. Chen, “Thicknessdependent mechanical properties of polydimethylsiloxane membranes,” J. Micromech. Microeng., vol. 19, no. 3, p. 035028, 2009.

Qingbo Guo received the B.S. degree from the Department of Microelectronics, Xi’dian University, Xi’an, China, in 2011. He is currently pursuing the Ph.D. degree in electrical engineering with the University of Utah, Salt Lake City, UT, USA. Professor Darrin J. Young is his research advisor. His current research interests include analog/mixedsignal integrated-circuit and microelectromechanical systems for applications include pressure scanning, wireless sensing, biomedical implantation, and industrial applications.

Michael A. Suster (M’07) received the B.S., M.S., and Ph.D. degrees in electrical engineering from Case Western Reserve University, Cleveland, OH, USA, in 2002, 2006, and 2011, respectively. He was a Post-Doctoral Researcher with the Electrical and Computer Engineering Department, University of Utah, Salt Lake City, UT, USA, from 2010 to 2011. Since 2011, he has been a Research Associate with the Electrical Engineering and Computer Science Department, Case Western Reserve University. His current research interests include analog and mixedsignal circuit and system design for applications that include broadband dielectric spectroscopy, wireless sensing, biomedical implants, and advanced industrial applications. He is a member of the the IEEE Solid-State Circuits Society.

Rajesh Surapaneni received the B.S. and M.S. degrees in mechanical engineering from Jawaharlal Nehru Technological University, Hyderabad, India, and University of Utah, Salt Lake City, UT, USA, in 2006 and 2010, respectively. He is currently pursuing the Ph.D. degree with the Electrical and Computer Engineering Department, University of Utah. His doctoral thesis focused on the development of a highly reliable flexible tactile sensor array that can detect three axial forces acting at a point of contact. His current research interests include micro/nano fabrication in cleanroom technology and design and development of microelectromechanical systems.


Carlos H. Mastrangelo (S’84–M’90) was born in Buenos Aires, Argentina, in 1960. He received the B.S., M.S., and Ph.D. degrees in electrical engineering and computer science from the University of California, Berkeley, CA, USA, in 1985, 1988, and 1991, respectively. His graduate work concentrated on the applications of microbridges in microsensor technology. From 1991 to 1992, he was with the Scientific Research Laboratory, Ford Motor Company, Dearborn, MI, USA, developing microsensors for automotive applications. From 1993 to 2002, he was an Associate Professor of electrical engineering and computer science with the Center for Integrated Microsystems, University of Michigan, Ann Arbor, MI, USA. From 2000 to 2005, he was a Vice President of engineering with Corning-Intellisense, Wilmington, MA, USA, and a Director of the Biochemical Technologies Research Group, Corning, NY, USA. From 2005 to 2008, he was an Associate Professor of electrical engineering and computer science at Case Western Reserve University, Cleveland, OH, USA. He is currently a USTAR Professor of electrical engineering and bioengineering with the University of Utah, Salt Lake City, UT, USA. His current research interests include microelectromechanical system applications and technology, microfluidic systems, and integration, design, and modeling of MEMS fabrication processes. He is on the editorial boards of Sensors and Actuators and the IEEE/ASME Journal of Microelectromechanical Systems, and he has participated in technical and organizing committees of numerous SPIE and IEEE conferences in the MEMS area.

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Darrin J. Young (S’93–M’99) received the B.S., M.S., and Ph.D. degrees from the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, USA, in 1991, 1993, and 1999, respectively. In 1999, he joined the Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA, as an Assistant Professor. In 2009, he joined the Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA, as a Utah Science and Technology Research Initiative Associate Professor. His current research interests include micro-electro-mechanical systems design, fabrication, and integrated analog circuits design for wireless sensing, biomedical implant, communication, and general industrial applications. He has published many technical papers in journals and conferences and served as a Technical Program Committee Member and a Session Chair for a number of international conferences. He was an Associate Editor of the IEEE J OURNAL OF S OLID S TATE C IRCUITS from 2006 to 2011 and currently serves as the Chair of the microelectromechanical systems committee under the IEEE Electron Devices Society.