sensors Article Seat Occupancy Detection Based on a Low-Power Microcontroller and a Single FSR † Ernesto Sifuentes 1 , Rafael Gonzalez-Landaeta 1, * , Juan Cota-Ruiz 1 and Ferran Reverter 2 1 Department of Computer and Electrical Engineering, Universidad Autónoma de Ciudad Juárez (UACJ), Ciudad Juárez 32310, Mexico; esifuent@uacj.mx (E.S.); jcota@uacj.mx (J.C.-R.) 2 Department of Electronic Engineering, Universitat Politècnica de Catalunya (UPC)–BarcelonaTech, Castelldefels, 08860 Barcelona, Spain; ferran.reverter@upc.edu * Correspondence: rafael.gonzalez@uacj.mx; Tel.: +52-656-688-4841 † This paper is an extended version of the conference paper: Sifuentes, E.; González-Landaeta, R.; Cota-Ruiz, J.; Reverter, F. Microcontroller-Based Seat Occupancy Detection and Classification. In Proceedings of the EUROSENSORS 2018, Graz, Austria, 9–12 September 2018.  Received: 26 December 2018; Accepted: 5 February 2019; Published: 8 February 2019  Abstract: This paper proposes a microcontroller-based measurement system to detect and confirm the presence of a subject in a chair. The system relies on a single Force Sensing Resistor (FSR), which is arranged in the seat of the chair, that undergoes a sudden resistance change when a subject/object is seated/placed over the chair. In order to distinguish between a subject and an inanimate object, the system also monitors small-signal variations of the FSR resistance caused by respiration. These resistance variations are then directly measured by a low-cost general-purpose microcontroller unit (MCU) without using either an analogue processing stage or an analogue-to-digital converter. Two versions of such a MCU-based circuit are presented: one to prove the concept of the measurement, and another with a smart wake-up (generated by the sudden resistance change) intended to reduce the energy consumption. The feasibility of the proposed measurement system is experimentally demonstrated with subjects of different weight sitting at different postures, and also with objects of different weight. The MCU-based circuit with a smart wake-up shows a standby current consumption of 800 nA, and requires an energy of 125 µJ to carry out the measurement after the wake-up. Keywords: autonomous sensor; force sensing resistor; microcontroller; resistive sensor; seat occupancy detection; sensor interface electronics 1. Introduction Seat-occupancy monitoring systems generally use mechanical sensors that detect weight, pressure, force or acceleration over the seat. These mechanical sensors can be resistive [1], capacitive [2] or inductive [3], but the former are the most common. Different types of resistive mechanical sensors can be employed, for instance: metallic strain gauges, semiconductor strain gauges and force sensing resistors (FSR), the latter being the cheapest and the most easily integrated into the chair structure. Mechanical sensors, however, have difficulties in distinguishing between subjects and objects seated/placed over the seat. This is usually solved by comparing the set of data to known patterns [4] or by combining the information of different types of sensor. For example, in [5], the information of the resistive mechanical sensor is combined with that provided by two additional sensors: a thermal sensor and a capacitive proximity sensor. The combination of proximity sensors, instead of mechanical sensors, have also been reported for seat-occupancy monitoring systems, as suggested in [6] by combining inductive and capacitive proximity sensors. Sensors 2019, 19, 699; doi:10.3390/s19030699 www.mdpi.com/journal/sensors Sensors 2019, 19, 699 2 of 11 Resistive mechanical sensors, integrated in wheelchairs and conventional chairs, also play a significant role in health telemonitoring applications. However, several sensors are generally required to carry out the measurement of interest. For instance, the sitting posture has been proposed to be detected by employing: four load cells arranged in the seat of a chair in [7] and twelve FSRs distributed in both the seat and the backrest of a wheelchair in [8]. The respiratory signal has also been monitored using resistive mechanical sensors, for example: four FSRs attached to the backrest of a conventional chair in [9], and nine FSRs distributed in both the seat and the backrest of a wheelchair in [10]. This paper, which continues and expands the work presented in [11], proposes a novel measurement method for seat occupancy detection and classification using a single FSR. The additional information usually required for the subject-object classification is extracted from the FSR itself, which is exploited to monitor the respiratory signal similarly to the applications described in the previous paragraph. It is expected that the occupancy causes a large-signal variation of the FSR, whereas the respiration generates a small-signal variation enabling the confirmation of the presence of a subject rather than an object. As for the read-out electronic circuit, it is proposed to read both large- and small-signal variations of the FSR applying the concept of direct interface circuit (DIC) [12], where the sensor is directly connected to a low-cost microcontroller unit (MCU) without using intermediate analogue electronics either an analogue-to-digital converter (ADC). These MCU-based circuits have been extensively analyzed and proved for resistive [13–15], capacitive [16–18] and inductive [19,20] sensors with different topologies, but not for resistive sensors undergoing both large- and small-signal variations, as we have in the application of interest here. DICs have also been suggested for sensors providing a quasi-static analogue output voltage [21,22] and as a versatile interface circuit for the measurement of different types of sensor [23]. According to the last two paragraphs, the main contribution of this paper is the proposal of a MCU-based circuit to detect and confirm the presence of a subject seated over a chair using a single FSR. The same sensor is employed to wake-up the MCU in the presence of a subject/object, and then to monitor the respiratory signal to confirm the presence of a subject. Moreover, this is carried out without using any intermediate analogue electronics, thus resulting in a low-power, low-cost design solution. Consequently, the proposed method is attractive for autonomous sensor applications that require the detection and confirmation of people sitting in chairs, such as intelligent airbag deployment systems and aircraft boarding systems. The paper is organized as follows. Section 2 describes the operating principle of the seat occupancy detection and classification technique. Section 3 explains how the FSR is measured through the DIC and proposes an improved version with a smart wake-up so as to reduce its energy consumption. Section 4 describes the setup and the measurement method. Section 5 shows and discusses the experimental results. Finally, Section 6 draws the main conclusions. 2. Operating Principle The proposed method for the seat occupancy detection and classification relies on a single FSR attached at the center of the seat. A FSR is a sensor whose resistance (Rx ) decreases with increasing the force applied to it; in the application of interest, the force will be exerted by the subject/object seated/placed over the chair. This is a very low-cost, thin-size resistive mechanical sensor that can be easily integrated into the chair structure; however, it has limitations in terms of accuracy, linearity, interchangeability and time drifts. The repeatability due to time drifts can be quite critical when the FSR is subjected to a static loading for a long time interval (say, some hours), but it is quite acceptable (at least for FSRs from Interlink Electronics) when the FSR is subjected to a dynamic loading for a short time interval (say, a few seconds) [24]. The previous limitations are not expected to be critical here since the aim is to monitor resistance variations due to respiration in a few seconds, and not to determine the exact value of the subject/object weight nor the respiratory rate. Sensors 2019, 19, 699 3 of 11 The operating principle of the proposed measurement method is shown in Figure 1, where Sensors 2019, 19, x 3 of 11 three scenarios are possible: (1) The seat is vacant: Rx remains constant with respect to Rx,0 , which is the nominal resistance nominal resistanceunder under zero-force zero-forceconditions. conditions. (2)(2) A A subject subjectisisseated seated over over thethe chair: chair: Rx suddenly Rx suddenly decreases decreases (e.g.,(e.g., fromfrom Rx,0Rtox,0 to Rx,1 Rx,1 ) )due dueto tothe the subject’s subject’s weight. weight.Then, Then,asas thethe subject stays subject there, stays his/her there, his/her respiration respiration causes causes small-signalvariations small-signal variations of of RRxx (∆R (∆Rx xininFigure Figure 1a)1a) that thatenables us to enables usconfirm the the to confirm presence presence of a subject. of a subject. (3) (3) AnAn inanimateobject inanimate objectisisplaced placed over overthe thechair: chair:RxRundergoes a sudden decrease x undergoes a sudden decrease due to the object’s weight, but remains constant at Rx,2, as shown in Figure 1b. Therefore, according due to the object’s weight, but remains constant at Rx,2 , as shown in Figure 1b. Therefore, according to to Figure 1, if the measurement system is able to detect such small-signal variations caused by Figure 1, if the measurement system is able to detect such small-signal variations caused by respiration, respiration, the subject-object classification can be carried out using a single resistive mechanical the subject-object sensor. classification can be carried out using a single resistive mechanical sensor. Figure Figure 1. FSR 1. FSR variations variations whenover when overthe the chair chair we wehave: have:(a)(a) a subject; (b) (b) a subject; an object. an object. 3. Read-out 3. Read-out Interface Interface Circuit Circuit The variations The FSR FSR variations shownshown in Figure in Figure 1 are1 proposed are proposed to betomeasured be measured by aby a low-cost low-cost general- general-purpose MCU applying the DIC concept [12]. The MCU only needs to have an embedded digitaldigital purpose MCU applying the DIC concept [12]. The MCU only needs to have an embedded timer and timer and a few input/output (I/O) digital port pins; no analogue (e.g., comparator) or mixed (e.g., a few input/output (I/O) digital port pins; no analogue (e.g., comparator) or mixed (e.g., ADC) ADC) embedded peripherals are required. Next, two proposals for the MCU-based circuit are embedded peripherals are required. Next, two proposals for the MCU-based circuit are presented. presented. The first one is a basic topology that will enable us to prove the measurement method The first one is ainbasic introduced topology Section thatthe 2, whereas will enable second oneus is to anprove the measurement energy-efficient method topology with introduced a smart wake- in Section up.2, whereas the second one is an energy-efficient topology with a smart wake-up. 3.1. Basic Read-out 3.1. Basic Circuit Read-out Circuit The basic topology The basic topology of of thethe DIC DICtotoread readthe the FSR FSR is is shown shownininFigure Figure 2a.2a. First First of all, of all, notenote that that the the FSR has a resistor (R ) in parallel so as to linearize its response and also to FSR has a resistor (Rp ) in parallel so as to linearize its response and also to avoid long measurements p avoid long measurements in zero-force in zero-force conditions. conditions. The The operatingprinciple operating principle of of this thiscircuit circuitisisasas follows. follows.Initially, Pin Pin Initially, 1 provides 1 provides a digital a digital ‘1’ and ‘1’ and Pin Pin2 is2in is high-impedance in high-impedance(HZ) (HZ) state state and, and,consequently, consequently, thethe well-known well-known capacitor capacitor C is quickly charged to the supply voltage (VDD). Next, Pin 1 is in HZ and Pin 2 provides a digital ‘0’ C is quickly charged to the supply voltage (V DD ). Next, Pin 1 is in HZ and Pin 2 provides a digital so that C is discharged towards ground through the equivalent resistance (Req = Rp‖Rx). In the ‘0’ so that C is discharged towards ground through the equivalent resistance (Req = Rp kRx ). In the meantime, an embedded timer (which uses a high-frequency clock signal as a reference) measures meantime, the timeaninterval embedded required timer (which to do uses a high-frequency the discharge. When the exponential clockdischarging signal as avoltage reference) measures crosses the the time threshold voltage (VT) of the digital buffer embedded into Pin 1, the timer is stopped, as shown in the interval required to do the discharge. When the exponential discharging voltage crosses threshold Figurevoltage 2b. The (V T ) of the resulting digital digital buffer number embedded stored in the timerinto isPin 1, the timer proportional to is thestopped, dischargingas shown time in Figure(Td2b. ) andThe alsoresulting to Req, since digital Td = Rnumber eq·C·ln(VDDstored in this /VT) [12]; the is timer validisprovided proportional that Reqtois the much discharging higher than time the parasitic (Td ) and also to Routput eq , since Td = Reqof·Cthe resistance ·ln(V I/ODD pins /V T involved ) [12]; in this the is measurement. valid provided For that aR given eq Td and is much higher assuming than the thatoutput parasitic C, VDD and VT are known, resistance of the R eq can I/O be estimated pins involved as in the measurement. For a given Td and assuming that C, V DD and V T are known, Req can beTdestimated as Req = (1) C ln (VDD VT ) Td Req = (1) CIncircuit Unlike the circuits proposed in [12,13], the (VDD /Vin Figure T) 2a does not require any reference resistor because the information to be monitored is on the change, not in the absolute value. Unlike the circuits proposed in [12,13], the circuit in Figure 2a does not require any reference resistor because the information to be monitored is on the change, not in the absolute value. Sensors Sensors 2019, 19,2019, 699 19, x 4 of 411 of 11 . (a) (b) Figure Figure 2. 2. (a)applied (a) DIC DIC applied to measure to measure the the FSRFSR so as so as to to detectthe detect theoccupancy occupancy of of aaseat; seat;(b) (b)waveform waveformof of the voltage across C during the measurement of the voltage across C during the measurement of the FSR. the FSR. 3.2. Read-out 3.2. Read-out CircuitCircuit with awith a Smart Smart Wake-up Wake-up The mainThelimitation main limitation of theofcircuit the circuit shown shown in Figure in Figure 2a2a isisthat thatthe thepresence presence of of aasubject/object subject/object overover the chair is detected by polling, thus involving unnecessary energy consumption especially when the the chair is detected by polling, thus involving unnecessary energy consumption especially when the seat is vacant. This can be improved using the DIC-based topology shown in Figure 3, which has a seat is vacant. This can be improved using the DIC-based topology shown in Figure 3, which has smart wake-up generated by the FSR itself when a sudden decrease of resistance occurs. In other a smartwords: wake-up the generated MCU is in by the FSR a deep sleepitself modewhen a sudden by default, but decrease of resistance when a sudden decreaseoccurs. In other of resistance words: happens, the MCUthe is MCU in a deep sleep mode by default, but when a sudden decrease of resistance wakes up and measures the FSR in the same way indicated in Section 3.1. This kind happens, the MCU wakeswake-up of smart up and inmeasures MCU-based the circuits FSR in was the same way indicated also proposed inbut in [5,25], Section 3.1. This employing kindfor a sensor of the smart wake-up in MCU-based wake-up and anothercircuits for thewas also proposed measurement in [5,25], of interest. Here, but employing the FSR a sensor is employed forpurposes. for both the wake-up and anotherThe foroperating principle of the measurement ofthe circuit shown interest. Here, the in Figure FSR is3 employed is the following. Initially, for both the MCU is in purposes. Sensors 2019, 19, x 5 of 11 a deep sleep mode and the circuit is configured as a voltage divider using a series resistance (Rs). The pins are configured as: Pin 3 provides a digital ‘1’, Pin 2 is set as external interruption with falling edge attention, Pin 1 provides a digital Pin‘0’, 3 and Pin 4 is in HZ. Note that the current consumption of this voltage divider is very low since the FSR offersRas very high resistance in zero-force conditions. When a subject/object is seated/placed Pin 2 over theVint FSR, Rits eq resistance suddenly decreases and, consequently, the output (Vint) of the voltage divider also decreases, thus generating an external Microcontroller interruption attended by Pin 2. In such a case, the MCU wakes FSR up and then measures the FSR as described in Section 3.1. In comparison Timer with the circuit shown in Figure 2a, there are two differences: (1) Pin 3 is set in HZ and Pin 4 provides Pin 1 a digital ‘0’ during the charge-discharge process shown in VC Figure 2b, and (2) Rp is not required here since the sensor resistance is quite low when a subject/object is seated/placed over the chair and, hence, the discharging C time is not expected to be so long. Therefore, in such a topology, we havePin Req4 = Rx during the discharge process. Br ea thin g Once the MCU is awake, the measurement of TUSB d is repeated n times with a sampling frequency RX/TX of fs during an acquisition time of Tacq (= n/fs) that should be long enough to appropriately monitor the respiratory signal. The main energy consumption occurs when charging C to VDD and when measuring Td with a timer Figure Figure 3. DIC running 3. DIC applied applied at totohigh frequency measure measure the FSR the FSRso as ato with with reduce asmart smart the effects of quantization. wake-up. wake-up. Accordingly, the energy required to carry out the complete measurement can be approximated as An algorithm The operating to be executed principle by the shown of the circuit MCU forinthe subject-object Figure 3 is the classification is proposed following. Initially, theinMCU   V   is in aFigure deep4.sleep Whenmode the MCUandwakes up the circuit E from ≈ Vis DD C the VDDdeep sleep + I timer configured Req mode, asln DD it measures  fsTacq a voltage divider Td using up to natimes seriesand the resistance (2) result is stored in a 16-bit variable Ni. From  VT   the set of measurements, the maximum (Nmax) and (Rs ). The pins are configured as: Pin 3 provides a digital ‘1’, Pin 2 is set as external interruption minimum (Nmin) values are found; a low-pass filter processing of the data could be required before with falling whereedge Itimer isattention, the currentPin 1 provides consumption a digital of the timer in ‘0’, and of charge Pin 4 is in HZ. measuring Note Td. This that the is valid current provided determine Nmax and Nmin so as to avoid the effects of aberrant measurements. Afterwards, the consumption differenceof(ΔN) that the this voltage current betweendivider consumption Nmax and of is very the MCU Nmin is low since theIfFSR while controlling calculated. suchoffers both a very high the charging a difference resistance stage is higher and than in the zero-force sampling a given frequency conditions. When is a negligible. Note that subject/object is these two tasks can seated/placed over easily the be FSR, controlled its by a timer resistance runningdecreases suddenly at low threshold level, the presence of a subject is confirmed. As explained later in Section 5.2, the circuit in frequency (e.g., and, consequently, the32output kHz) [26] (V intthat ) offsgenerally involves a currentdecreases, consumption smaller than 1 µA. The Figure 3 is expected to operate at =the voltage 2 Sa/s divider during Tacq =also 10 s and, therefore, thus generating 20 samples ofan Tdexternal (of consumption related to the data processing is not considered in Equation (2). interruption attended 16 bits) will be stored.by This Pin involves 2. In such a case, 40 bytes the MCU of RAM, whichwakes up andinthen are available most measures MCUs. the FSR as described in Section 3.1. In comparison with the circuit shown in Figure 2a, there are two differences: (1) Pin 3 is set in HZ and Pin 4 provides a digital ‘0’ during the charge-discharge process shown in Figure 2b, and (2) Rp is not required here since the sensor resistance is quite low when a subject/object Sensors 2019, 19, 699 5 of 11 is seated/placed over the chair and, hence, the discharging time is not expected to be so long. Therefore, in such 2019, 19, x we have Req = Rx during the discharge process. a topology, Sensors 5 of 11 Once the MCU is awake, the measurement of Td is repeated n times with a sampling frequency of f s during an acquisition time of Tacq (= n/f Pin s3) that should be long enough to appropriately monitor the respiratory signal. The main energy consumption Rs occurs when charging C to V DD and when measuring Td with a timer running at high Pin 2 frequencyVint so as Reqto reduce the effects of quantization. Accordingly, the energy required to carry out the complete measurement can be approximated as Microcontroller Timer   FSR VDD E ≈ VDD C VDD + Itimer Req ln f s Tacq (2) Pin 1 VC VT where Itimer is the current consumption of the timer in charge C of measuring Td . This is valid provided Pin 4 that the current consumption of the MCU while controlling both the charging stage and the sampling Br ea thin g frequency is negligible. Note that theseRX/TX two tasks can USB easily be controlled by a timer running at low frequency (e.g., 32 kHz) [26] that generally involves a current consumption smaller than 1 µA. The consumption related to the data processing is not considered in Equation (2). An algorithm to beFigure 3. DIC applied to measure the FSR with a smart wake-up. executed by the MCU for the subject-object classification is proposed in Figure 4. AnWhen the MCU wakes algorithm to be executed byup fromthe theMCU deep for sleep mode, it measures the subject-object Td up classification to n times is proposed in and the result stored in a 16-bit variable N Figure 4. When the MCU wakes up from ithe deep sleep mode, it measures Td up to n times and the max ) is . From the set of measurements, the maximum (N and minimum (Nminin) values result is stored a 16-bit are found; variable Ni. aFrom low-pass the setfilter processing ofthe of measurements, themaximum data could be) required (Nmax and beforeminimum determine N)max (Nmin values Nmin andare so aaslow-pass found; to avoidfilterthe processing effects of of aberrant the datameasurements. could be requiredAfterwards, before determine(∆N) the difference Nmax between and Nmin N somaxas and to avoid Nmin the effects of aberrant is calculated. If such ameasurements. Afterwards, difference is higher than the a given difference (ΔN) between N threshold level, the presence of a subject is confirmed. As explained later in Section 5.2,a the max and N min is calculated. If such a difference is higher than givencircuit threshold in Figure level, the presence 3 is expected to operateof aat subject f s = 2isSa/s confirmed. during AsTexplained = 10 slater and,intherefore, Section 5.2,20 thesamples circuit inof T acq d Figure 3 is expected to operate at fs = 2 Sa/s during Tacq = 10 s and, therefore, 20 samples of Td (of (of 16 bits) will be stored. This involves 40 bytes of RAM, which are available in most MCUs. 16 bits) will be stored. This involves 40 bytes of RAM, which are available in most MCUs. Figure Figure 4. Flowchart 4. Flowchart of of thethe proposedalgorithm proposed algorithm for forthe thesubject-object subject-objectclassification. classification. Sensors 2019, 19, 699 6 of 11 Sensors 2019, 19, x 6 of 11 4. Materials 4. Materials and and Method Method 4.1. General 4.1. General A commercial A commercial FSR FSR (FSR (FSR406 406from fromInterlink InterlinkElectronics) Electronics)was wasarranged arranged inin thethe center center of of thethe seat seat of of a conventional chair. This FSR has R > 10 MΩ, an active area of 4 × 4 cm 2 and a rise time x,0 a conventional chair. This FSR has Rx,0 > 10 MΩ, an active area of 4 × 4 cm and a rise time lower than 2 3lower than sensor µs. The 3 µs. The wassensor then was then connected connected to a DICtoimplemented a DIC implemented by a low-cost, by a low-cost, low-powerlow-power MCU (MSP430F123 from Texas Instruments, Dallas, TX, USA) operating at 8 MHz and powered at MCU (MSP430F123 from Texas Instruments, Dallas, TX, USA) operating at 8 MHz and powered at V VDDDD==3.33.3V.V.ThisThisMCUMCU hashas several several Low-Power Low-Power ModesModes (LPM)(LPM) to control to control which which on-chipon-chip circuitry, circuitry, such such as theascentral the central processing processing unit (CPU) unit (CPU) and peripherals, and peripherals, is active. is active. It alsoIthasalso256 hasbytes 256 bytes of RAM.of RAM. An embedded 16-bit timer (running at 8 MHz and operating An embedded 16-bit timer (running at 8 MHz and operating in LPM3) measured in LPM3) measured T d as in Figure Td as 2b.in Several samples Figure 2b. Several samplesof T d were taken at a sampling frequency of Td were taken at a sampling frequency of f s , which was controlled of fs, which was controlledby anotherby embedded another timer. The embedded frequency timer. of the respiratory The frequency signal is expected of the respiratory signal is to be very to expected lowbe(lower very low than(lower 1 Hz) and, hence, f s ≥ 2 Sa/s following the Nyquist criterion. Each than 1 Hz) and, hence, fs ≥ 2 Sa/s following the Nyquist criterion. Each sample sample of T d was transmitted in real of Td was transmitted time to a personal computer and, then, converted to the corresponding in real time to a personal computer and, then, converted to the corresponding value of R applying Equation eq value of Req applying (1) through a(1) control program implemented TM in LabVIEW in.LabVIEWTM. Equation through a control program implemented The proposed measurement The proposed measurement system was system was experimentally experimentally testedtested with with three three healthy healthy subjects subjects of of different weight and age (S1: 45 kg/14 years; S2: 64 kg/39 years; S3: different weight and age (S1: 45 kg/14 years; S2: 64 kg/39 years; S3: 91 kg/41 years), and also with91 kg/41 years), and also with three objects three objects of of different different weight weight (O1:(O1: 55 kg; kg; O2: 10 kg; O2: 10 kg; O3: O3: 30 30 kg). kg). The The volunteers volunteers were were asked asked toto position position themselves over the chair and breathe freely, but to keep quiet during the themselves over the chair and breathe freely, but to keep quiet during the measurement so as to avoid measurement so as to avoid movement artifacts. movement artifacts. FourFour different differentsitting sittingpostures posturesofofthethesubject subjectwerewerealso alsotested, tested,asasshown shownininFigure Figure 5. The idea of placing the FSR at the backrest was not considered because 5. The idea of placing the FSR at the backrest was not considered because the signal detected would the signal detected would be insignificant be insignificant in certain in certain sitting postures, sitting such postures, as P3 such as in P3Figure in Figure5. 5. The measurements The measurements of of current current consumption consumption were were carried carried out out by by aa digital digital electrometer electrometer (Keithley (Keithley 6514) following the procedures indicated 6514) following the procedures indicated in [26]. in [26]. P1 P2 P3 P4 Figure Figure 5. Different Different sitting postures of the subject to test the proposed measurement system. 4.2. Basic 4.2. Basic Read-out Read-out Circuit Circuit The circuit The circuit inin Figure Figure 2a2a was was tested using RRpp = tested using Ω, and 3570 Ω, = 3570 and different different values of C values of C (470 (470 nF, nF, 11µF and µF and 2.2 µF) 2.2 and ffss (2, µF) and (2, 20 20 and and 60 60 Sa/s). Sa/s).The Thevalues valuesof ofCCwere werehigh high enough enough toto have have aa good good resolution resolution inin the the measurement of T and, hence, of measurement of Tddand, hence, of Req [27]. R eq [27]. The selected value of R p (together The selected value of Rp (together with the maximum with the maximum value value of C) generated T ≤ 8 ms, thus avoiding the overflow of the of C) generated Td ≤ 8 ms, thus avoiding the overflow of the timer in zero-force d timer in zero-force conditions. conditions. The function of Pin 1 and Pin 2 in Figure 2a was implemented by pins P1.1 and P1.2, The function of Pin 1 and Pin 2 in Figure 2a was implemented by pins P1.1 and P1.2, respectively, respectively, which are general-purpose I/O digital pins. The former has a Schmitt-Trigger buffer with V which are general-purpose I/O digital pins. The former has a Schmitt-Trigger buffer with VTT == 1.2 1.2 V V and is and is associated associated withwith aa capture capture module module that that automatically captures the automatically captures the value value ofof the the timer timer when when thethe external signal crosses external signal crosses VTT. V . 4.3. Read-out Circuit with a Smart-Wake-up 4.3. Read-out Circuit with a Smart-Wake-up In the circuit shown in Figure 3, the MCU operated by default in LPM4, where the CPU and all In the circuit shown in Figure 3, the MCU operated by default in LPM4, where the CPU and all clocks are disabled and only the external interruption generated by Pin 2 is enabled; the wake-up time clocks are disabled and only the external interruption generated by Pin 2 is enabled; the wake-up from LPM4 is 6 µs, which is fast enough for the application considered herein. Moreover, the circuit time from LPM4 is 6 µs, which is fast enough for the application considered herein. Moreover, the circuit employed Rs = 1 MΩ, which is more than ten times smaller than the FSR resistance in zero- 5. Experimental Results and Discussion 5.1. Basic Read-out Circuit The experimental results of the circuit in Figure 2a for different subjects are shown in Figure 6a. Sensors 2019, 19, 699 7 of 11 When a subject sat down (at t ≈ 10 s), a large-signal variation (between 600 and 2400 Ω) was observed, and this depended on the subject’s weight. For the heaviest subject (S3), the resistance dropped to an average employed valueRs of= 1about MΩ,1250 whichΩ. While is more seated, than tensmall-signal resistance times smaller thanvariations of aroundin the FSR resistance ±125 Ω with zero-force aconditions, respiratory thusrate between a12digital generating (for S3) and ’1’ at the24 (for of input S1) breaths Pin 2 whenper theminute, seat waswhich vacant.correspond The circuit towasa frequency between 0.2 and 0.4 Hz, were monitored. This well-defined optimized in terms of energy consumption using the minimum values of C (470 nF) and f s (2 Sa/s) respiratory signal clearly confirms from those theindicated presenceinofSection a subject 4.2,over the chair without as suggested by Equationincorporating (2). any other sensor. The The results function when of pinsplacing 1, 2, 3different objects 3over and 4 in Figure was the chair are represented implemented by pins P1.1,inP1.2, Figure P1.36b, andwhich P1.4, only respectively. In addition to the features indicated in the second paragraph of Section 4.2, P1.2 It shows a large-signal variation due to the object’s weight but not the small-signal variations. is was worth mentioning that in Figure 6b the large-signal variation is similar configured as an external interruption to automatically wake the MCU from LPM4 when the FSR and even higher than that in Figure underwent6a although a sudden thechange. weight is lower. This is because the objects under test had small dimensions and, consequently, their weight caused a “point” force on the FSR that was higher than the 5. Experimental corresponding Results and component Discussion force generated by the subject. of distributed Figures 7–9 show the small-signal resistance variations monitored for different values of fs, C 5.1. sitting and Basic Read-out posture,Circuit respectively. According to Figure 7, a sampling frequency of 2 Sa/s seems to be enough Thetoexperimental recover the frequency results of theof the respiratory circuit in Figuresignal, 2a forwith the corresponding different benefits subjects are shown in terms in Figure 6a. of energy When consumption. a subject sat down (at Fromt ≈ Figure 10 s), a 8, a low-value large-signal capacitor variation (470 nF) (between 600 seems and 2400 Ω) was valid to detect the observed, resistance variationsoninvolved and this depended in the the subject’s measurement, weight. thus reducing For the heaviest the energy subject (S3), consumption the resistance droppedoftothe an circuit average even valuemore.of about 1250 Ω. According to Figure While 9, the amplitude seated, small-signalof the small-signal resistance resistance variations variation of around ±125was Ω quite similar for the four sitting postures under test. Posture P3 generated with a respiratory rate between 12 (for S3) and 24 (for S1) breaths per minute, which correspond to the highest large-signal variation a frequencyat the instant0.2 between at which and 0.4 theHz, seatwere was monitored. occupied, whereas posture P4 provided This well-defined respiratorya more unstable signal clearly signal confirmsprobably because the presence ofof the crossed a subject over leg. the chair without incorporating any other sensor. 4000 4000 3500 3500 S1 3000 3000 O1 2500 2500 Req (Ω) Req (Ω) S2 2000 2000 O2 1500 1500 1000 S3 1000 500 500 O3 0 0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 t (s) t (s) (a) (b) Figure 6. Experimental resistance variations monitored by the DIC in Figure 2a when over the chair Figure we had:6.(a) Experimental resistance subjects of different variations weight monitored and age by the in the sitting DIC in posture P2;Figure 2a when (b) objects over theweight. of different chair we had: (a) subjects of different weight and age in the The test was carried out at f s = 60 Sa/s with C = 2.2 µF.sitting posture P2; (b) objects of different weight. The test was carried out at fs = 60 Sa/s with C = 2.2 µF. The results when placing different objects over the chair are represented in Figure 6b, which only shows a large-signal variation due to the object’s weight but not the small-signal variations. It is worth mentioning that in Figure 6b the large-signal variation is similar and even higher than that in Figure 6a although the weight is lower. This is because the objects under test had small dimensions and, consequently, their weight caused a “point” force on the FSR that was higher than the corresponding component of distributed force generated by the subject. Figures 7–9 show the small-signal resistance variations monitored for different values of f s , C and sitting posture, respectively. According to Figure 7, a sampling frequency of 2 Sa/s seems to be enough to recover the frequency of the respiratory signal, with the corresponding benefits in terms of energy consumption. From Figure 8, a low-value capacitor (470 nF) seems valid to detect the resistance variations involved in the measurement, thus reducing the energy consumption of the circuit even more. According to Figure 9, the amplitude of the small-signal resistance variation was quite similar for the four sitting postures under test. Posture P3 generated the highest large-signal variation at the Sensors 2019, 19, 699 8 of 11 Sensors 2019, 19, x 8 of 11 instant at which Sensors 2019, 19, x the seat was occupied, whereas posture P4 provided a more unstable signal probably 8 of 11 because of the Sensors 2019, 19, xcrossed leg. 8 of 11 1650 1650 1600 1650 1600 2Sa/s 1550 1600 2Sa/s 1550 2Sa/s 1500 1550 eq (Ω) 1500 1450 R(Ω) 1500 1450 ReqR(Ω) eq 1400 1450 1400 1350 1400 1350 1300 1350 1300 20Sa/s 1250 1300 20Sa/s 1250 0 5 20Sa/s 10 15 20 25 30 1250 0 5 t 15 (s) 10 20 25 30 0 5 t 15 (s) 10 20 25 30 t (s)by the DIC in Figure 2a for different sampling Figure 7. Small-signal resistance variations monitored Figure 7. Small-signal frequencies. resistance The test was variations carriedvariations monitored with subject S3 in thebyby the DIC sitting in Figure posture 2a for P2, with different C =different sampling 1 µF. sampling Figure Figure7.7.Small-signal Small-signalresistance resistance monitored the theDIC in inFigure 2a 2afor frequencies. The test was carried variations monitored with subject S3 in thebysitting DIC postureFigure P2, with for C =different 1 µF. sampling frequencies. frequencies.The Thetest testwas wascarried carriedwith withsubject subjectS3 S3ininthe thesitting sittingposture postureP2, withCC==11µF. P2,with µF. 1700 1700 1700 1650 1uF 1650 1uF 1650 1600 1uF 1600 eq (Ω) 1600 1550 R(Ω) 1550 ReqR(Ω) eq 1550 1500 1500 1500 1450 1450 470nF 1450 470nF 1400 1400 0 5 470nF10 15 20 25 30 1400 0 5 t 15 (s) 10 20 25 30 0 5 t 15 (s) 10 20 25 30 t (s)by the DIC in Figure 2a for different values of Figure 8. Small-signal resistance variations monitored Figure Figure8.8.Small-signal Small-signal resistance resistance variations monitored by thetheDIC in inFigure 2a 2afor different values of the capacitor. The test was carriedvariations monitored with subject S2 in thebysitting DIC Figure posture P2, withfor fs =different 20 Sa/s. values of the capacitor. Figure 8. The test Small-signal was carried resistance with subject variations S2 in monitoredthe sitting by the posture DIC in P2, with Figure 2a f for = 20 Sa/s. different the capacitor. The test was carried with subject S2 in the sitting posture P2, with sfs = 20 Sa/s. values of the capacitor. The test was carried with subject S2 in the sitting posture P2, with fs = 20 Sa/s. 1800 1800 1800 P4 1700 1700 P4 1600 1700 P4 1600 1500 1600 P2 eq (Ω) 1500 P2 R(Ω) 1400 1500 P1 P2 ReqR(Ω) eq 1400 P1 1300 1400 P1 1300 1200 1300 P3 1200 P3 1100 1200 P3 1100 0 5 10 15 20 25 30 1100 0 5 t 15 (s) 10 20 25 30 0 5 t 15 (s) 10 20 25 30 Figure9.9. Small-signal Figure Small-signal resistance resistance variations t (s) by variations monitored monitored by the the DIC DIC in in Figure Figure2a 2afor fordifferent differentsitting sitting Figure 9.(see postures postures Small-signal (seeFigure Figure5). resistance 5).The Thetest variations testwas wascarried monitored carriedwith withsubject by subjectS2 the atf sfsDIC S2at 20in == 20 Figure Sa/s Sa/s 2a for and with and with C =different C µF. sitting =11µF. Figure 9. (see postures Small-signal Figure 5).resistance variations The test was monitored carried with subjectby S2the at fsDIC= 20in Figure Sa/s 2a for and with C different = 1 µF. sitting postures (see Figure 5). The test was carried with subject S2 at fs = 20 Sa/s and with C = 1 µF. 5.2. Read-out Circuit with a Smart Wake-up 5.2. Read-out Circuit with a Smart Wake-up 5.2. Read-out Circuit with The experimental a Smart results of Wake-up the circuit in Figure 3 for different subjects and objects are shown in FigureThe experimental 10a,b, results respectively, of the that assuming circuit t = in 0 isFigure 3 for at the instant different subjects which the and objects subject/object are shown in is seated/placed FigureThe experimental 10a,b, results respectively, of the assuming circuit in Figure that t =Although 3 for 0 is the instant different at which subjects and objects are shown in over the chair, thus waking up the MCU. the values of Cthe subject/object and fs were low issoseated/placed as to reduce Figure 10a,b, respectively, assuming that t = 0 is the instant at which the subject/object over the chair, thus waking up the MCU. Although the values of C and fs were low so as to reduceis seated/placed over the chair, thus waking up the MCU. Although the values of C and fs were low so as to reduce Sensors 2019, 19, 699 9 of 11 5.2. Read-out Circuit with a Smart Wake-up The experimental results of the circuit in Figure 3 for different subjects and objects are shown in Sensors 2019, 19, x 9 of 11 Figure 10a,b, Sensors 2019, 19, respectively, x assuming that t = 0 is the instant at which the subject/object is seated/placed 9 of 11 over the chair, the energy thus wakingthe consumption, upcircuit the MCU. Although was able the values to recover of C and of the frequency f s the were low so as signal respiratory to reduce and the the energy the circuit was able to recover the frequency to detect the resistance variations caused by respiration, as clearly shown in Figure 10a. If anand consumption, the circuit was able to recover the frequency ofofthethe respiratory respiratory signal signal andto object to detect detect thethe resistance resistance variations variations caused caused by by respiration, respiration, as as clearly clearly shown shownin was placed over the chair instead of a subject, the resulting Req was almost constant over time after in Figure Figure 10a. 10a. If an If an object object was was the placed placed overover wake-up, the theaschair chair shown instead instead in of a of Figure a subject, subject, 10b. the resulting the resulting Accordingly, theReq Req was was almost subject-objectalmost constant constant overover classification time is time also after after the feasible the wake-up, wake-up, as as shown shown in in Figure Figure 10b. 10b. Accordingly, Accordingly, the the subject-object subject-object classification classification with the circuit in Figure 3. An appropriate threshold level (see Figure 4) for the classification could is is also also feasiblefeasible with with the theinstance, be, circuit for circuit in50Figure in Figure 3. appropriate Ω,3.which An An appropriate corresponds tothreshold threshold ΔN = 188 levellevel (see(see counts Figure Figure under 4) for 4) for the the the operating classification classification conditions couldcould indicatedbe, be, for for instance, instance, before. Ω,Ω, 50 From50Figure which which corresponds corresponds 10a, the acquisition toto∆NΔN=should time =188 188counts counts be around under under the 10the operating operating s in order to haveconditions enough indicated indicated samples before. (even for very low respiratory rates) that enable the detection of the respiratory signal. The samples before. From From Figure Figure 10a, 10a, the the acquisition acquisition time time should should be be around around 10 10 s s in in order order to to have have enough enough samples effects of (even (even for for very the sitting very low lowrespiratory posture respiratory when using rates) the that rates) thatenable circuit enable the in Figurethedetection detection ofof the 3 are represented therespiratory respiratory in Figure signal. signal.TheThe 11, which effects ofa effects shows the of sitting the posture sitting performance quitewhen posture when similarusing using to thethe that circuit circuitinin represented Figure Figure3in before 3are are represented Figurerepresented 9. ininFigure Figure11, 11,which which showsshows aa performance performance quite quite similar to that represented before in Figure 9. 3000 4000 3000 4000 3500 S1 O1 2500 3500 S1 3000 O1 2500 3000 2000 2500 S2 2500 Req (Ω) Req (Ω) 2000 S2 2000 O2 Req (Ω) Req (Ω) 2000 O2 1500 1500 1500 1500 1000 1000 S3 1000 O3 1000 S3 500 O3 500 500 0 500 0 5 10 15 20 25 30 0 0 5 10 15 20 25 30 0 5 10 15 t (s) 20 25 30 0 5 10 15 t (s) 20 25 30 t (s) t (s) (a) (b) (a) (b) Figure 10. Experimental resistance variations monitored by the DIC in Figure 3 when over the chair Figure 10. Figure Experimental resistance 10. Experimental resistance variations variationsmonitored monitoredby bythe theDIC DIC in in Figure 33 when when over over the the chair chair we had: (a) subjects of different weight and age in the sitting posture P2;Figure (b) objects of different weight. we we had: had: (a) subjects of different weight and age in the sitting posture P2; (b) objects of different weight. The test(a)was subjects of out carried different at fs =weight and age 2 Sa/s with C = in the 470 sitting posture P2; (b) objects of different weight. nF. The test The test was was carried carried out at ffss==22Sa/s out at withC C= = Sa/swith 470 470 nF. nF. 1700 1700 P4 P4 1600 1600 1500 1500 P2 1400 P2 Req (Ω) 1400 Req (Ω) 1300 1300 P1 1200 P1 1200 1100 P3 1100 P3 1000 1000 0 5 10 15 20 25 30 0 5 10 15 t (s) 20 25 30 t (s) Figure 11.Small-signal Figure11. Small-signal resistance resistance variations variations monitored monitored byby the the DIC DICininFigure Figure33for fordifferent differentsitting sitting Figure 11. postures Small-signal (see Figure 5). resistance The test variations was carried monitored with subject by S2 the at f s DIC = 2 in Sa/sFigure and 3 for with C different = nF. sitting 470 nF. postures (see Figure 5). The test was carried with subject S2 at fs = 2 Sa/s and with C = 470 postures (see Figure 5). The test was carried with subject S2 at fs = 2 Sa/s and with C = 470 nF. The current consumption of the circuit shown in Figure 3 in LPM4 (i.e., when waiting for an The current consumption of the circuit shown in Figure 3 in LPM4 (i.e., when waiting for an interruption The generated current by a subject/object) consumption of the circuitwas shown800 in nA,Figure including 3 in the LPM4current (i.e., of whenthe voltage divider interruption generated by a subject/object) was 800 nA, including the current of the waiting for an voltage divider interruption formed by generated Rs and FSR. by On a subject/object) wasthe the other hand, 800current nA, including the current consumption of the of embedded the voltage timer divider (at formed by R s and FSR. On the other hand, the current consumption of the embedded timer (at 8 MHz and in LPM3) while measuring Td was 500 µA. According to Equation (2) and assuming 8VMHz and in LPM3) while measuring Td was 500 µA. According to Equation (2) and assuming DD = 3.3 V, VT = 1.2 V, C = 470 nF, Itimer = 500 µA, Req = 1600 Ω, fs = 2 Sa/s, and Tacq = 10 s, the energy DD = 3.3 V, VT = 1.2 V, C = 470 nF, Itimer = 500 µA, Req = 1600 Ω, fs = 2 Sa/s, and Tacq = 10 s, the energy Vrequired is 125 µJ. Therefore, in case of using a lithium battery of 3.6 V − 1Ah, the circuit in Figure 3 required is 125autonomy would have µJ. Therefore, in case to check moreof than using100 a lithium millionbattery times ifofthe V − 1Ah, thewas 3.6 interruption circuit in Figure generated by3a Sensors 2019, 19, 699 10 of 11 formed by Rs and FSR. On the other hand, the current consumption of the embedded timer (at 8 MHz and in LPM3) while measuring Td was 500 µA. According to Equation (2) and assuming V DD = 3.3 V, V T = 1.2 V, C = 470 nF, Itimer = 500 µA, Req = 1600 Ω, f s = 2 Sa/s, and Tacq = 10 s, the energy required is 125 µJ. Therefore, in case of using a lithium battery of 3.6 V − 1 Ah, the circuit in Figure 3 would have autonomy to check more than 100 million times if the interruption was generated by a subject or an object. Taking into account that the circuit in Figure 3 was clearly able to detect the resistance variations caused by respiration, the discharging-time measurement could also be carried out at a lower operating frequency (e.g., 1 MHz instead of 8 MHz), thus reducing even more the energy required since Itimer would be smaller. However, the previous estimation of the autonomy would be shorter if the system had a transceiver circuit whose energy consumption can be quite significant. 6. Conclusions This work has proved that detecting and confirming the presence of a subject in a chair is feasible using a single FSR directly connected to a general-purpose MCU. The proposed system first detects the subject by monitoring his/her weight and then confirms his/her presence by monitoring the respiration. The proposed MCU-based circuit has also been improved in terms of energy consumption by incorporating a smart wake-up generated by the FSR itself. In such a way, the MCU is by default in a deep sleep mode. We believe the proposed system is suitable for applications related to autonomous sensors where it is important to detect and confirm the presence of people sitting in chairs, such as intelligent airbag deployment systems and aircraft boarding systems. Author Contributions: R.G.-L. and E.S. proposed the main idea and, together with J.C.-R., built the measurement setup, performed the experiments and analyzed the data. F.R. interpreted the results and wrote the manuscript. All the authors approved the final version of the manuscript. Acknowledgments: Rafael González-Landaeta, Ernesto Sifuentes and Juan Cota-Ruiz were funded by PRODEP and UACJ, México. The work of Ferran Reverter was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under project TEC2016-76991-P. Conflicts of Interest: The authors declare no conflict of interest. References 1. Oestreicher, R.; Homann, M.; Lichtinger, H.; Morell, S.; Reich, D. Method and System for Determining Weight and Position of a Vehicle Seat Occupant. U.S. Patent 7082360, 25 July 2006. 2. Böse, H.; Fuß, E.; Ehrlich, J. Capacitive sensor mats for pressure detection with high sensitivity. In Proceedings of the Sensor and Measurement Technology 2015, Nürnberg, Germany, 19–21 May 2015; pp. 55–60. 3. Kisic, M.G.; Blaz, N.V.; Babkovic, K.B.; Zivanov, L.D.; Damnjanovic, M.S. Detection of seat occupancy using a wireless inductive sensor. IEEE Trans. Magn. 2017, 53, 1–4. [CrossRef] 4. Breed, D.S.; DuVall, W.E.; Johnson, W.C. 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