Measurement System for Object Detection Based on Multielectrode Capacitive Sensor D. Ćika*, T. Martinović** and H. Džapo** *Research **Faculty
and Engineering Center d.d., Zagreb, Croatia of Electrical Engineering and Computing, Zagreb, Croatia
[email protected]
Abstract - The problem of detecting objects, people and events is encountered in different forms in many application areas. In this paper we investigate the applicability of multielectrode capacitive sensors for monitoring the volume of interest by analyzing the disturbances in electrical field due to the influence of external objects. Capacitive sensors are particularly interesting as a low cost solution with minimum privacy issue concerns. The goal of this research was to characterize the performance of modern capacitanceto-digital converter (CDC) PicoCap PCap02 integrated circuit for detecting various types of objects in a multielectrode system configuration, with human detection as an example application. We present an analysis of an appropriate electrode geometry, propose a custom-designed data acquisition system which complies to the required capacitance measurement range and resolution, discuss sources of measurement errors and methods for minimization of their influence, and elaborate the applicability of the method beyond the simple object detection, by investigating the possibility of object class discrimination from acquired data. Keywords: capacitive sensors, object detection, data acquisition, electronic instrumentation
I.
INTRODUCTION
Object detection is the problem encountered in different forms in many application areas, such as industrial quality control, production process monitoring, security systems, traffic monitoring, inventory tracking etc. Intensive research led to the development of methods and techniques to solve particular practical problems for data acquisition and processing based on information acquired by different types of sensors, including cameras, motion detection sensors (ultrasound, infrared, radars), capacitive and inductive sensors, planar force detection sensors, acoustic sensors etc.[1]. Most advanced methods and achievements have been developed in the area of computer vision, using camera as data acquisition sensor, and employing image processing and machine learning algorithms to solve many practical problems in the field of detection and classification of objects and people. However, those methods are usually computationally demanding, and not suitable for low power embedded systems that are commonly encountered in practice. There are also privacy concerns which might exclude camera sensors in some applications.
Capacitive sensors are interesting due to possibilities for simple and low cost production (both electrode manufacturing process and data acquisition circuitry), and capability for detection of non-magnetic objects, without a need for direct galvanic contact. These characteristics make capacitive sensors an attractive choice also for nonmetallic and non-magnetic object detection and discrimination, and also for applications including detection of people and animals. Capacitive sensors are used in many different applications [2], [3], such as precise displacement measurement (micrometers), proximity detection, pressure and force measurement, liquid level measurement, human-machine interfaces (touch screen displays, keyboards), inertial sensors (accelerometers), environmental conditions monitoring etc. [4] The majority of advances in the area of multielectrode capacitive sensors applications has been achieved for human-machine interfaces, namely touch screen that must precisely determine a finger position in a real-time [5], [6]. Prior work in the area of person detection by means of capacitive sensors proposed using large electrodes in horizontal or vertical plane for detecting persons on the move [7], [8], detecting hand movement in the proximity of the metal processing machinery for increased operator safety [9], or detecting vehicle seat occupancy by electrodes embedded in seats [10], [11], [12]. Previous research also confirmed the applicability of multielectrode capacitive sensors systems for detection and classification of events, human activity, body position and movement [13], [14], [15], by using machine learning approach for sensor data processing. In this article we investigate the possibility of object detection and discrimination by means of a customdesigned multielectrode capacitive sensor, with a signal conditioning part based on a single chip capacitance-todigital converter (CDC). The integrated circuit PCap02 (manufactured by acam-messelectronic gmbh) was chosen due to its exceptional measurement characteristics, comparing with other available CDC solutions. The goal of the research was to assess whether the approach to people and object detection by using lowcost compact CDC-based integrated circuits is a viable solution for practical use cases.
II.
PCAP02 CAPACITANCE-TO-DIGITAL-CONVERTER
A. PCap02 Integrated Circuit Characteristics Typical PCap02 application schematic diagram when I C interface is used is shown in Fig. 1. 2
depends on the capacitor under test, and discharge resistor which can be selected under software control. B. Connecting Sensors to CDC Various configurations are possible when capacitance sensors are used, as shown in Fig. 2 and Fig. 3. Sensor plates can be connected in a single or differential configuration, and each can have external ground reference, or it can be floating. Depending on the chosen configuration, different number of electrodes can be used since the total number of measuring inputs on the PCap02 CDC unit is limited to eight, including the input for the reference capacitance measuring. If the single floating configuration is used, four capacitive sensors can be connected to the single CDC unit. Shielded cables are recommended to compensate the external parasitic capacitances against the ground for longer cables, as shown in Fig. 4. This measure alone may not be effective in some cases, so active shield driving might be necessary.
Fig. 1. PCap02A typical schematics, I2C interface
Detailed characteristics and explanation of principle of operation for this component can be found in [16], [17]. PCap02 has the digital signal processor (DSP) integrated in order to achieve on-chip data postprocessing in addition to the basic converter functionality. The PCAp02 can handle larger offset capacitance on its inputs than some other comparable CDC chips, therefore it is inherently less susceptible to stray capacitance caused by connecting wires. Table 1 shows dependency between the output data rate and capacitive noise and resolution for 10 pF base and 1 pF span. Only part of the table for floating sensor configuration is shown here, more information can be found in [16].
Fig. 2. Options for connecting sensors - single inputs
The PCap02 is based on the discharge time measurement principle, which means that the capacitance is determined from the time needed to discharge the RCnetwork. The time is measured by high resolution timeto-digital converter (TDC), and the ratio of discharge times is directly proportional to the ratio of capacitor under test and the reference capacitor. The discharge time
Fig. 3. Options for connecting sensors - differential inputs
Fig. 4. Connection of shielded cables Table 1. PCap02 RMS noise and resolution
III.
DATA ACQUISITION SYSTEM PROTOTYPE
A. PCap02 Breakout Board Although there is readily available PCap02 evaluation board (PCap02-EVA-Kit [18]), we decided to fabricate our own small breakout board, that is more suitable for cases where portability and small dimensions are needed. The breakout board contains only minimum set of components necessary for the PCap02 operation and communication with the host microcontroller. Breakout board dimensions are 20 x 20 mm. The hand soldered PCap02 ready for test is shown in Fig. 5. The breakout board schematic is shown in Fig. 6. PCap02 pins necessary for functions not implemented on the breakout board are available at header pins distributed along the breakout board edges with the standard 100 mils pin spacing. The power supply is not part of the breakout board, and user should provide the necessary 3.3 V DC voltage to the board. The 1.8 V DC is generated internally in the PCap02, and available at external pins for powering other components if necessary. The PCap02 breakout board power consumption measured on the prototype is 15 µA
Fig. 5. PCap02 breakout board ready for test
with I2C interface used for communication with the host microcontroller. Additional power consumption should be added to the system power requirements, depending on the microcontroller type and clock speed selection, ratio of active versus sleep time operation, and other microcontroller operation specific parameters. The serial bus communication mode with the host microcontroller (MCU) is preselected by soldering the R3 solder bridge (0 Ω resistor) for the SPI, or the R4 solder bridge for the I2C interface. The I2C is more convenient when several boards needs to be connected with a minimum wiring, while the SPI offers higher data transfer rates, which was not a requirement for this project. B. CDC Module and Firmware Support The ARM Cortex M4 core family microcontroller was chosen for implementation of the experimental capacitive sensor data acquisition system, and the STM32F4 Discovery evaluation board is used [19]. Firmware was developed in the ARM Keil© uVision integrated development environment (IDE) [20] by defining and implementing hardware abstraction layer (HAL) C functions for the PCap02 sensor integration and support. In case of the MCU platform change, even to some other architecture, changes in the code would be minimized as there are no hardware dependencies. The I2C communication and other hardware dependent functions are implemented using the STM32F4 DSP and standard peripherals library [21], and some freely available open source wrapper functions on top of this library [22]. During the experiments, communication with the host PC was necessary in order to transfer data and display graphs, and the USB port was used for that purpose as shown in Fig. 7. The complete environment including the bench type laboratory power supply and a person standing between two electrodes while doing the measurement is shown in Fig. 8.
Fig. 6. Schematic diagram for the custom designed PCap02 breakout board
divided into 1800 triangle elements. Electrodes were separated by 80 cm thus simulating the standard door.
Fig. 7. Measurement setup block diagram
The simulations were performed by using the metallic cylinder as very simplified model of the human body with satisfactory approximation results. The cylinder diameter was varied in the range of 30 cm, 35 cm, and 40 cm with 180 cm height, and it was divided into 5108 triangle elements for simulation purposes. By using the Robin Hood Solver scripting language, cylinder (i.e. person) is programmed to pass in the middle of the volume between the plates. Simulation results are shown in Fig. 9.
Fig. 8. The measurement setup including single pair of electrodes Fig. 9. The Robin Hood Solver simulation results
IV.
SIMULATION AND MEASUREMENT
A. Measurement Setup The block diagram of the basic measurement setup already shown in Fig. 7 corresponds to the photograph shown in Fig. 8. For different electrode configurations the measuring setup would only be altered regarding the number of inputs. As long as up to three channels are needed, single PCap02 can be used, otherwise additional breakout board should be used. The multielectrode measurement system can be arranged so that each pair of electrodes captures different area of interest. In the experimental setup described here only one pair of electrodes was used at the time, but the electrode placement and object orientation was varied to observe the resulting capacitance change. B. Electrostatic Simulation The expected experimental results can be estimated by means of electrostatic simulation, with different level of accuracy, depending on the tools available and quality of models that can be used with these simulation tools. In this research the simulations were performed by using The Robin Hood Solver [23] because it is fast, it has low memory requirements, scripting in the Lua programming language is supported, and it is easy to use with licensing policy suitable for academic use. The simulation setup used two different electrode pairs (10 x 10 cm and 20 x 10 cm), and each electrode was
In a zero position, the cylinder is placed exactly between the sensing plates, and the distance is increased to 1 m in 5 cm steps. Two different sets of electrode dimensions were simulated, 20 x 10 cm, and 10 x 10 cm. The resulting capacitance change is between 15 fF and 50 fF, depending on a sensing plate size and cylinder diameter. While even smaller sensing plates could be used, resulting capacitance changes would be barely noticeable due to parasitic capacitance changes representing noise to the measuring system. These are very demanding and challenging requirements for the measurement system. C. Measurement Results The cable connection to the sensor plates is critical, and if the wiring is not done properly it can introduce noise, or result in much smaller measuring span. Measurements were performed by using: 1.
unshielded cable,
2.
shielded cables with grounded shield,
3.
shielded cables with active shield driving.
First experimental setup was similar to those described in [10], [15]. The sensing plates are positioned on the chair backrest and seating area, person was standing, seating, and seating away from the backrest. Plate size is 20 x 30 cm. The results are shown in Fig. 10.
Fig. 10. Sensor electrodes positioned on the chair
The distance of only few centimeters from seating person to the sensors could explain much higher capacitance values than simulated and found in following experiments. The unshielded cables also act as a part of electrode and contribute to the total measured capacitance. The noise visible in the results looks insignificant, but it is in the tenths of picofarad range, which is well above the capacity change predicted by the simulation and found in the following 'doorway' measurement setups where object was more distant from the sensor plates. The second experimental setup was using the shielded cable grounded at the PCB side, but the longer cable still contributes to the input capacitance, and shielded cable of one meter length and more could saturate the inputs of the PCap02.
Fig. 11. Active shield, 20 x 15 cm electrodes, 80 cm distance
Best results were obtained in the third experimental setup, when the cable shield is driven with the same signal used for capacitive sensor excitation. Measurement results are shown in Fig. 11. The higher capacitance values correspond to the person passing through the doorway, and standing in the space between the sensor plates for about five seconds. First two peaks were obtained for 'normal' front facing orientation, and third peak which is a bit smaller in capacitance was measured for sideway walking and standing. This is result of a position where the body is a bit more away from the sensor. The active shield measurement option was realized by using the same signal for the shield drive as the one driving the sensor plates. The TL082 operational amplifier in voltage follower configuration was used for each plate (two amplifiers per measuring channel), as shown in the schematic diagram in Fig. 12.
Fig. 12. Complete capacitive sensing circuit (without MCU board) with active shield driving
With the electrodes disconnected, the noise was around 1 fF peak to peak. With electrodes connected using the overall cable length of 3 meters, the noise was around 20 fF, less than half of approximately 50 fF capacitance change detected when person passed through the area observed. Measuring span is 144 pF for the 18 pF reference capacitance selected (span = 8 x Cref).
[6] [7]
[8] [9]
V.
CONCLUSION
The PCap02 CDC capacitive sensor can be used for object detection and discrimination applications based on the multielectrode capacitive sensors. Cabling is critical, and after active shield drive technique was used, the noise was reduced below the detection threshold using the reasonably sized electrodes, while in the same time cables longer than one meter are used. The firmware support developed can be easily applied to other low power MCU architectures, and portable battery operated device can be designed which would autonomously operate for extended period of time. The sensitivity and speed of the measurement setup is high enough to detect if the person was passing slowly or running, or is it standing facing front or sideways, if supported by appropriate detection and discrimination algorithms.
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ACKNOWLEDGMENT The research leading to these results has received funding from the project "Information and communication technology for generic and energyefficient communication solutions with application in e-//m-health (ICTGEN)" co-financed by the European Union from the European Regional Development Fund. REFERENCES [1]
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