CapSense: Capacitor-based Activity Sensing for Kinetic...

CapSense: Capacitor-based Activity Sensing for Kinetic EnergyHarvesting Powered Wearable Devices

Guohao LanUNSW & Data61-CSIRO

Sydney, [email protected]

Dong MaUNSW & Data61-CSIRO


Weitao XuUNSW


Mahbub HassanUNSW & Data61-CSIRO


Wen HuUNSW & Data61-CSIRO


ABSTRACTWe propose a new activity sensing method, CapSense, which de-tects activities of daily living (ADL) by sampling the voltage of thekinetic energy harvesting (KEH) capacitor at an ultra low samplingrate. Unlike conventional sensors that generate only instantaneousmotion information of the subject, KEH capacitors accumulate andstore human generated energy over time. Given that humans pro-duce kinetic energy at distinct rates for dierent ADL, the KEHcapacitor can be sampled only once in a while to observe the en-ergy generation rate and identify the current activity. us, withCapSense, it is possible to avoid collecting time series motion dataat high frequency, which promises signicant power saving for thesensing device. We prototype a shoe-mounted KEH-powered wear-able device and conduct experiments with 10 subjects for detecting5 dierent activities. Our results show that compared to the existingtime-series-based activity recognition, CapSense reduces sampling-induced power consumption by 99% and the overall system power,aer considering wireless transmissions, by 75%. CapSense recog-nizes activities with up to 90%.

CCS CONCEPTS•Computing methodologies →Activity recognition and un-derstanding; •Human-centered computing→Ubiquitous com-puting;

KEYWORDSEnergy-eciency, Activity Recognition, Wearable Device

ACM Reference format:Guohao Lan, Dong Ma, Weitao Xu, Mahbub Hassan, and Wen Hu. 2017.CapSense: Capacitor-based Activity Sensing for Kinetic Energy HarvestingPowered Wearable Devices. In Proceedings of e 14th EAI Conference on Mo-bile and Ubiquitous Systems: Computing, Networking and Services, Melbourne,Australia, November 7-10, 2017 (Mobiquitous’17), 10 pages.

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor prot or commercial advantage and that copies bear this notice and the full citationon the rst page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permied. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specic permission and/or afee. Request permissions from [email protected]’17, Melbourne, Australia© 2017 ACM. 978-xxxx-xx-xxx/17/11. . .$15.00DOI: 10.475/123 4

DOI: 10.475/123 4

1 INTRODUCTIONe rapid development of embedded technology has enabled wear-able systems [32] that provide autonomous health, tness, andwellness monitoring services, such as step-counting [7, 27] andrecognition of ADL [15, 16]. To improve user-experience, wearabledevices are restricted to very small form factor, limiting the sizeof baery that can be embedded in the device. However, mostusers desire 24/7 monitoring, which leads to long-term continuoussampling of motion sensors, such as accelerometers, and frequentdata transmission to the server for further processing. e highpower consumption due to frequent sensor sampling and wirelesstransmission limits the baery life of these wearable devices.

e rst stage in a typical activity recognition system is the ac-quisition of a time-series of samples from the sensors [5]. In general,the power consumption of sensor sampling is directly proportionalto the sampling rate, as the higher the sampling rate, the morepower is consumed by the sensors as well as the microcontroller(MCU), which has to wake up more frequently to read, process,and store the samples. A large volume of past research on humanactivity sensing, therefore, have focused on reducing the samplingrates of accelerometer-based systems. Depending on the type ofactivities, reported sampling rates for accelerometer-based activitysensing range between 25Hz to 100Hz for recognition accuraciesover 85% [5, 8, 25].

To further reduce power consumption of sensor sampling, re-searchers have recently proposed the use of KEH transducer, whichproduces dierent AC voltage paerns for dierent activities, asan alternative to accelerometer [18, 35]. By saving the energythat would have been otherwise consumed by the accelerometer,transducer-based systems can reduce the sampling power consump-tion to some extent [18]. However, as we will demonstrate later inthe paper, the sensor itself consumes a small fraction of the systempower during sampling; a large fraction of the sampling power isactually consumed by the MCU. e transducer-based approach,which also relies on time series data, therefore still consumes sig-nicant amount of limited baery power in a small form-factorwearable device.

Based on this observation, we propose a new way to exploitthe KEH circuit for detecting human activities. e new method,

Mobiquitous’17, November 7-10, 2017, Melbourne, Australia G. Lan et al.

which we call CapSense, avoids the acquisition of time series dataof instantaneous motion (for accelerometer) or AC voltage (fortransducer) information, thereby drastically reducing the sensorsampling rate. To cut down the sampling rate, CapSense capitalizestwo important facts in KEH-powered systems:• First, instead of providing only instantaneous information (i.e.,

the instant acceleration or AC voltage), the KEH capacitor canprovide accumulated information. at is, the energy generatedcontinuously by the KEH transducer for a given period of Tsecond is accumulated in the capacitor. erefore, a capacitorcan directly yield the energy generation rate during the last Tsecond with a single sample of its current voltage. In essence,the capacitor works as a free computation engine that computesthe current energy generation rate without involving the MCUor collecting any time series data.

• Second, it has been demonstrated that the energy generationrates of human activities are distinctively dierent [9, 39].us, it should be possible to classify human activities bysampling the capacitor voltage only once in every T second.For example, for a choice of T = 5, we can achieve activityrecognition with a sampling rate of only 0.2 Hz.

e novelty and contributions of this paper can be summarizedas follows:• Wepropose a newmethod for human context sensing, CapSense,

to drastically reduce sampling rates and power consumptionof KEH-powered wearable systems. CapSense detects activityfrom the voltage of the KEH-capacitor, which to the best ofour knowledge has not been explored before.

• We have implemented the idea of CapSense in shoe formfactor using piezoelectric bending energy harvester. We derivedesign parameters for the capacitor that produce eectivevoltage samples for human activity recognition.

• We conduct experiments with 10 subjects for detecting 5 dif-ferent activities. We demonstrate that CapSense is capable ofdetecting ADL with up to 92% accuracy.

• We conduct a detailed power proling to quantify the powersaving opportunity of CapSense. Our measurement resultsindicate that, compared to the state-of-the-art, CapSense re-duces sampling-related power consumption by 99% and theoverall system power by 75%.

e rest of the paper is organized as follows. We review therelated works in Section 2. en, we present the design and imple-mentation of CapSense in Section 3, followed by its performanceevaluation in Section 4. e power measurement study is presentedin Section 5. We conclude our work in Section 6.

2 RELATEDWORKIn this section, we rst review existing works on reducing the sam-pling frequency. en, we discuss some recent eorts in utilizingKEH-transducer as an energy-ecient motion sensor.

2.1 Reducing Sampling FrequencyA large volume of works in the literature focused on reducing thesampling rates [5, 8, 29, 36] to improve energy eciency in sensing.In [20] Krause et al., studied the trade-o between power consump-tion and classication accuracy for the e-Watch wearable device.

ey demonstrated that the lifetime of the device can be extendedby selecting the optimal sampling strategy without accuracy losing.Similar results are presented in [36], the authors pointed out thatthere is a trade-o between sampling frequency and classicationaccuracy, and introduced the A3R algorithm which adapts the sam-pling frequency and classication features in real-time based on theactivity type. In addition, researchers have also proposed the use ofcompressive sensing theory to reduce sampling frequency [6, 26, 28]by leveraging the temporal-sparsity of human activity.

2.2 KEH-transducer based Context SensingRecent eorts in the literature start to apply KEH transducer asa low power vibration sensor. In [18], Khalifa et al. proposed theidea of using the power signal generated by a VEH device for hu-man activities recognition. e proposed system can achieve 83%of accuracy for classifying dierent daily activities. In [23], theauthors investigate the feasibility of using KEH as a motion sensorfor transportation mode detection. In [22], Lan et al. demonstratedthe use of KEH voltage signal to estimate calorie expenditure ofhuman activities. In [14], a piezoelectric energy harvester-basedwearable necklace has been design for food-intake monitoring. eproposed system achieves over 80% of accuracy in distinguishingfood categories. In [2], Blank et al. proposed a ball impact local-ization system using a piezoelectric embedded table tennis racket.More recently, Xu et al. [35] proposed an authentication systemwhich utilizes the AC voltage signal to authenticate the user basedon gait analysis. e proposed system can achieve an recognitionaccuracy of 95% when ve gait cycles are used. In [24], the authorsproposed the use of KEH-transducer as an energy-ecient receiverfor acoustic communication. Although the use of KEH-transducercan save the energy consumption in powering the motion sensor,it still continuously sense and process the power signal from theKEH transducer at a high sampling rate, and thus, will continue toface the energy consumption problem.

3 SYSTEM OVERVIEW3.1 CapSense ArchitectureAlthough there are a various of designs in kinetic energy harvest-ing powered wearable devices, such as backpack [34], fabric [37],wristband [31], and footwear [21, 33], we designed our system inthe form-factor of shoes for several reasons: rst, shoes are wornby users for the majority of time in their daily lives, includingworking/studying in the oce/school; second, unlike many otherwearable devices that their form-factors have been shrunk dramati-cally, shoes have a much larger space to place an energy harvester;third, it has been widely reported that a desirable amount of energycan be harvested from human daily activities using the insole-basedharvesting system [11, 12, 33].

Figure 1 presents the system architecture of CapSense whichconsists of two parts: the Energy Harvesting and Load. e EnergyHarvesting corresponds to the functional components that harvestand accumulate energy from human activity. It includes a piezo-electric energy harvesting (PEH) transducer to generate electricpower (i.e., AC voltage) from foot strikes, and an rectifying circuitthat is able to rectify the intermient or continuous AC voltageoutput from the PEH transducer into stable DC power. en, the

CapSense: Capacitor-based Activity Sensing Mobiquitous’17, November 7-10, 2017, Melbourne, Australia

Figure 1: System architecture of CapSense.

rectied DC voltage will be accumulated in a capacitor before it issucient to turn-on the buck converter and power any electronics(i.e., the voltage of the capacitor should reach a pre-dened thresh-old congured in the buck converter). e Load represents anysystem components responsible for data sensing, processing, andcommunication, or could be a rechargeable baery that can be usedto power a wearable system.

As an illustration, considering the scenario in which a subjectis wearing the KEH-powered shoes and doing some activities, e.g.,walking or running, her heel will hit the ground oor and the footstrike induced pressure will bend the energy harvester inside theshoe. Consequently, the energy harvester will generate electricpower from the foot strikes when the subject is doing dierentactivities, and the generated energy are naturally accumulated inthe capacitor [10, 11, 38]. In CapSense, the voltage of the capac-itor is sampled periodically by the system load (e.g., once everyT seconds), and is send to the remote server (i.e., smartphone orcloud-server) for further processing. As the power generated fromdierent human activities such as walking, running, and relaxing,are distinctively dierent [9, 39], and the energy generated by thePEH transducer within the last T second(s) is accumulated in thecapacitor, it would be possible to estimate the power generation rateduringT by a single sample of the capacitor voltage. en, theestimated rates can be used to recognize the activity performed bythe user in the last T second with a sampling rate of 1

T . As we willdemonstrate later in Section 4, CapSense can detect activities withsampling rates as low as 0.14Hz compared to tens of Hz requiredby the state-of-the-art. us, CapSense can reduce sensing-inducedpower consumption of wearable devices by several orders.

e fundamental novelty of CapSense is that, unlike accelerom-eter or KEH transducer that can only generate instantaneousmotion information of the subject at a particular time, capacitorstores the generated KEH energy over time, and thus, it providesaccumulated information that can be used to detect the subject’s

Prototype Attached to Subject’s Leg PEH inside the shoe

Capacitor

Energy Harvesting Circuit

SD card

PEH Transducer

Figure 2: Pictures of CapSense prototype.

+

PEH

T

Buck Converter

Capacitor (470uF,25V)

Arduino

ADC

Figure 3: Circuit diagram of energy harvester.

activity within a period of time. erefore, unlike accelerometer-based [5] or KEH transducer-based [18, 35] sensing systems thatrequire a time-series of signal sampled from the sensor (i.e., asequence of signal sampled from the accelerometer or KEH trans-ducer), CapSense utilizes a single sample of the capacitor voltagefor activity recognition.

3.2 CapSense PrototypeIn this subsection, we present the design and implementation ofour prototype. Figure 2 gives the pictures of our prototype whichwe implemented in the form of shoe. As discussed previously,our prototype consists of two parts, the Energy Harvesting andLoad. For the Energy Harvesting part, we select the EH220-A4-503YB PEH bending transducer from Piezo Systems1 as our PEHtransducer and aached it to the shoe-pad. e PEH transduceris only 10.4 grams weighted with a dimension of 76.2×31.75×2.28mm3, which is easy to be placed inside the shoe. e output pins ofthe PEH transducer are connected to an energy harvesting circuit,namely the LTC3588-1 from the Linear Technology2. e LTC3588-1 integrates a low power-loss bridge rectier that can be used torectify the AC voltage output from the PEH transducer, and a high

1Piezo System: hp://www.piezo.com/prodexg8dqm.html.2Linear Technology LTC3588: hp://www.linear.com/product/LTC3588-1.


eciency buck converter that is able to transfer the energy storedin the capacitor into stable DC power to power/charge the load.We select an electrolytic capacitor with a capacitance of 470µF anda rating voltage (i.e., the maximum voltage) of 25V to store thegenerated energy (we will discuss how we select the capacitor inSection 3.3). When the voltage of the capacitor rises above theundervoltage lockout rising threshold of the buck converter (i.e.,denoted by VUV LO risinд, and equals to 4V in our seing), thebuck converter will be enabled to discharge the energy stored inthe capacitor. On the other hand, when the capacitor voltage hasbeen discharged below the lockout falling threshold (i.e., denotedby VUV LO f allinд, and equals to 3.08V in our seing), the buckconverter will be turned o, and the capacitor starts to be chargedagain. In our current prototype, an Arduino Uno is used for datalogging. e voltage of the capacitor is sampled by the Arduinothrough onboard 10-bit ADC at 100Hz and stored on the SD cardfor oine data analysis. e circuit diagram of the prototype isshown in Figure 3.

3.3 Ensuring Linearity in Capacitor VoltageBefore presenting the details of capacitor-based sensing, we rstanalyze some properties of the capacitor when the system is pow-ered by an energy harvester, and discuss the feasibility and designrequirement of leveraging the capacitor voltage for activity sensing.For this purpose, we use our CapSense hardware to collect somevoltage samples from a subject when she is conducting dierentdaily activities, namely, walking, running, ascending/descendingstairs, and stationary.

e voltage of the capacitor,VC (t), at time t during the chargingis given by:

VC (t) = Vmax (1 − e−t/τ ), (1)

in which, Vmax is the maximum voltage to which the capacitorcan be charged, and it is bounded by Vmax = minVratinд ,VS ,where VS is the voltage applied to the capacitor (i.e., the rectiedDC voltage from the rectier in our case), and Vratinд is the ratingvoltage of the capacitor; and τ is dened as τ = RC , in which, R isthe resistance of the resistor in the equivalent resistor-capacitorcharging circuit (RC circuit), and C is the capacitance of the capaci-tor. For a given capacitor, τ is known as the time constant of theequivalent RC circuit, which is a constant value (in second).

Figure 4 plots the voltage of a capacitor when it is charged overtime (in the unit of RC). e rst observation is that, within theexamining time of 5RC , the increasing rate of capacitor voltage isnot constant. For instance, the voltage increment in the rst RCinterval (i.e., from time 0 to RC) is not equal to that increased inthe second RC interval (i.e., from time RC to 2RC). As the onlyinformation we can obtain from the capacitor is its voltage, and weare using the voltage increasing rate for activity sensing, the non-linear increment in the capacitor voltage will introduce additionaluncertainties in the activity recognition.

Fortunately, as we can observe in Figure 4, with time t ≤ 12RC ,

the theoretical curve of VC , dened in Equation (2), can be approx-imated by a linear curve. According to the RC circuit theory, acapacitor can be charged to 39.3% of Vmax with a charging time of12RC , this means that to ensure the linearity in the capacitor voltage

0 RC 2RC 3RC 4RC 5RC

Time

0

0.2Vmax

0.4Vmax

0.6Vmax

0.8Vmax

Vmax

Cap

acito

r V

olta

ge (

Vol

t)

Theoretical CurveLinear Curve

Linear Approximation

Figure 4: e theoretical curve shows the voltage of a capac-itor when it is charged over time. And the theoretical curveis approximated by an linear curve. e unit of time is RC(i.e., the time constant).

for a maximum time of 12RC , Vmax should satisfy:

Vmax ≥VUV LO risinд

0.393 , (2)

which yields:

minVratinд ,VS ≥VUV LO risinд

0.393 , (3)

in which,VUV LO risinд is the undervoltage lockout rising thresholdof the buck converter, at which the capacitor starts to be discharged.is means that, to ensure the linearity in the capacitor voltage,the selection and conguration of the hardware components (i.e.,the PEH transducer, the buck converter, and capacitor) should beconsidered interactively.

As discussed in Section 3.2, in our prototype,VUV LO risinд of thebuck converter has been congured to 4V3. is means that, givenEquation (3), we have: minVratinд ,VS ≥

4V0.393 = 10.18V . e

rectied DC voltage from the rectier, VS , depends on the energyharvester that is used in the system. Given dierent materials andcongurations of the energy harvester, VS could be as high as tensof volts. In our case, the rectied voltage from the PEH transducerwe used in the current prototype is up to 20.8V which is muchhigher than 10.18V. erefore, it turns out that the rating voltageof the capacitor, Vratinд , should be larger than 10.18V. We select acapacitor with rating voltage of 25V to meet the requirement.

3.4 Activity Sensing using Capacitor VoltageIn this section, we discuss how does CapSense leverage the capacitorvoltage for activity sensing.

An actual voltage trace showing the capacitor charging anddischarging status is presented in Figure 5. Initially, the capacitorvoltage starts from 0, and takes approximately 60 seconds to reach4V and triggers the buck converter to discharge the accumulatedenergy (i.e., the VUV LO of the buck converter is 4V). en, whenthe capacitor voltage is discharged to 3.08V, the buck converter isshut o until the capacitor voltage being charged above theVUV LO

3According to the datasheet of LTC3588, to ensure an output DC voltage of 2.5V, thelowest voltage threshold is 4V.


Charging/Discharging Cycle

Discharge

Charge

VUVLO rising

VUVLO falling

Figure 5: Voltage trace showing the capacitor is charged anddischarged periodically when the subject is running.

TC

Vn

TS

Tcycle

MCU Sleep

Capacitor Charging Capacitor Discharging

Data Sampling

Charging cycles of the Capacitor

System Operation Cycles

(a)

(b)

TC

TS

V1

Tcycle

V0

...

n-1 cycles

V2~Vn-1

...

TDC

Tcycle Tcycle

n-1 cycles

TCharge

V1V0

TCharge

V2~Vn-1 Vn

Figure 6: Timeline (a) is the charging cycles of the capacitor.Timeline (b) is the system operation cycles of CapSense.

rising threshold again. As shown, the capacitor is charged anddischarged periodically under the control of the energy harvestingcircuit depending on its voltage level. e length of each chargingcycle is approximately 20 seconds when the subject is running(i.e., the most intense activity we consider). e cycle length willbe bigger if the subject is doing some modest activities such aswalkings. is means that there is at least 20 seconds of time, duringwhich the voltage samples could be used for activity sensing.

Figure 6 presents the system operation cycles of CapSense, to-gether with the charging cycles of the capacitor. As shown, withineach capacitor charging cycle, TCharдe , CapSense duty-cycles thesystem MCU to sample capacitor voltage periodically. At the begin-ning of each charging cycle, MCU is in the sleep mode, while thecapacitor starts to accumulate the energy generated by the KEHtransducer. Aer an accumulation time of TC , MCU awakes andstarts to sample the voltageVC of the capacitor. It takes timeTS forMCU to complete the data sampling (according to our measurementin Section 5, TS equals to 0.6ms). Once the sampling is nished,MCU goes back to sleep again. As shown in Figure 6, in each capac-itor charging cycle, CapSense reads a series of capacitor voltage,V1,V2, ...,Vn−1,Vn . By using two adjacent voltage samples, for in-stance, Vk−1 and Vk , it is straightforward to estimate the increaserate, r , of the capacitor voltage within the last accumulation time

Figure 7: emeasured voltage of a self-discharge capacitor.

of TC by:r =

Vk −Vk−1TC

. (4)

erefore, using the voltage reading of the capacitor, we can esti-mate the energy generation rate of the transducer in timeTC , with-out the need of high frequency data sampling. It is also noteworthythat, as the MCU has no knowledge about the charging/dischargestatus of the capacitor, it is possible that those two adjacent volt-age samples are obtained in two dierent charging cycles (i.e., itmay result in Vk−1 ≥ Vk , as Vk is sampled at the initial chargingstate of the capacitor in a new charging cycle). In this case, whencalculate the increase rate, r , we disregard all adjacent voltage peer,Vk−1,Vk , with Vk ≤ Vk−1.

However, the estimation of r may be aected by the dischargingtime of the capacitor (i.e., TDC shown in Figure 6). As if it takes along time for the buck converter to discharge the capacitor from4V to 3V, it is possible that the MCU may wake-up and sample anincorrect voltage value during the discharge of the capacitor, whichwill result in a wrong estimation in r . Fortunately, as shown inFigure 5, the time required to discharge the capacitor from 4V to3V, TDC , is extremely short which is less than tenms in our cur-rent design. us, it is highly unlikely that the voltage is sampledincorrectly when the capacitor is discharging. Another factor thatmay aect the estimation is the capacitor leakage. Figure 7 showsthe measured voltage of our capacitor when it self-discharges (i.e.,leakage) from 5V to 0V. Specically, we are interested in the be-havior of the capacitor within the 3-4V range. As specied in thesubgure, we can observe that it takes more than 700 seconds (i.e.,12 minutes) for the capacitor to self-charge from 4V to 3V, whichmeans the leakage of the capacitor is negligible within a short timeof a few seconds. us, the capacitor leakage will not aect ourestimation with a few seconds accumulation time TC .

As an example, Figure 8 plots the voltage samples of the capacitorwhen a subject is doing dierent activities. We can observe that, forall ve activities, our hardware design ensures a linear increasing inthe voltage when the capacitor is powered by the energy harvester.As dierent human activities can generate distinctive amount ofenergy [9, 39], intuitively, the increasing rate of capacitor voltagewould directly yield the energy harvesting rate during the last fewseconds, thus, we can achieve activity recognition by simply usingthe capacitor voltage. CapSense utilizes solely the capacitor voltage


0 2 4 6 8 10

Time (s)

0

0.5

1

1.5

Ca

pa

cito

r V

olta

ge

(Volt) Stairs-Down

Running

Stairs-Up

Walking

Stationary

Stationary

Figure 8: Voltage traces of dierent activities.

to classify dierent human activities using conventional machinelearning algorithms, which enables system level power saving byenabling the MCU to stay in the energy-saving low-power mode forextended periods of time. In the following section, we will evaluatethe performance of CapSense using our prototype.

4 SYSTEM EVALUATION4.1 Data Collectione dataset we used to evaluate the proposed system is collectedfrom 10 healthy subjects volunteered in our lab4. e subjects arediverse in gender (8 males and 2 females), age (range from 24 to30), weight (from 55 to 75Kg), and height (from 168 to 183cm). Weconsidered ve dierent activities, including: walking, running, as-cending/descending stairs, and stationary (i.e., siing or standing).During data collection, the subjects were asked to wear our energy-harvesting embedded shoe (we prepared shoes with dierent sizesto meet the requirement of our subjects) and the prototype systemis aached to the subject’s ankle (as shown in Figure 2). en, thesubjects were asked to perform the activities normally in their ownway without any specic instruction. As illustrated in Figure 9, foractivities such as running and walking, they are performed in bothindoor and outdoor environments to capture the inuence of dif-ferent terrains. For ascending and descending stairs, we conducteddata collection in two building environments with dierent stylesof stairs. For all the ve activities, each volunteer participated atleast two data collection sessions for both indoor and outdoor envi-ronments. For walking, running, and stationary, each session lastsat least 20 seconds, whereas, for ascending/descending stairs (i.e.,the slope and steps of the stairs are dierent), each session may last7 to 10 seconds depending on the number of steps and the speedof the subject. For each of the ve activities, we have collected atleast four sessions of samples from each of the 10 subjects. In total,we have 210 sessions of data.

4.2 Evaluation Methodologye evaluation is carried out in WEKA5 using 10-folds cross valida-tion with 10 repetitions for each test. Six typical machine learningalgorithms are used: the C4.5 decision tree algorithm (C4.5) [30], IBk4Ethical approval for carrying out this experiment has been granted by the correspond-ing organization (Approval Number HC15888).5WEKA: hp://www.cs.waikato.ac.nz/ml/weka/.

Running Walking Stationary

Stairs-Up

Running

(a) Subject doing dierent activities.

(b) Indoor environment. (c) Outdoor environment.

Figure 9: e illustration of data collection.

K-Nearest Neighbor classier [1], Naive Bayes with kernel estima-tion [13], RandomForest [4], DecisionTable [19], and BayesNets [3].e parameters of the classiers are optimized using the CVParam-eterSelection algorithm [19]. We evaluate CapSense performance interms of activity recognition accuracy (i.e., True Positive Rate). eexperiment is conducted in a subject-dependent manner. It is note-worthy that, unlike conventional accelerometer-based [5] or KEHtransducer-based activity sensing system [18] that require complexsignal processing and feature extractions, CapSense relies only onthe voltage dierence of the capacitor for activity recognition.

4.3 Performance Evaluation4.3.1 Recognition Accuracy v.s. Subject. e rst thing we are

interested in is the impact of subject dierence on the recognitionaccuracy, as subjects will perform activity in dierent ways whichmay aect the system accuracy [5]. Intuitively, given the diversityin subject’s gender, weight, and height, the foot strike pressuresapplied on the energy harvester dier in the way subjects performthe activity. e classier we used in this experiment is NaiveBayes.

Figure 10 exhibits the achieved accuracy of CapSense for all the10 subjects given dierent accumulation windows TC . As expected,given a specic classier and a xed TC , the achieved classicationvaries with subjects. More specically, Figure 11 plots the confusionmatrix of the classication results for two subjects, Subject 4 and8, with TC=3s. Subject 4 achieves the lowest accuracy among allsubjects, whereas, Subject 8 achieves the highest. As shown inFigure 11, for both subjects, the major classication error occurbetween the class ‘Stairs’ (i.e., ascending/descending stairs) and‘Walk’, due to the high similarity between those activities. ForSubject 4, there are more classication errors appear between thosetwo classes than that for Subject 8. We can also notice that about13% of the ‘Run’ instances have been classied incorrectly as ‘Stairs’for Subject 4, whereas, there is no classication error for Subject8. Clearly, as expected, the results indicate that dierent subjects


1s 2s 3s 4s 5s 6s 7s

TC (s)

S1

S2

S3

S4

S5

S6

S7

S8

S9

S10

AVG

Sub

ject

64.54

65.03

82.18

67.82

83.28

81.20

84.93

71.26

72.77

75.90

68.82

76.28

74.78

82.85

82.94

83.39

83.85

82.47

76.82

83.48

78.57

84.71

78.88 78.90 79.46

85.97

89.01

86.99

90.13

95.36

86.74

93.71

86.65

93.88

85.79

96.79

85.71

92.71

87.84

87.13

96.78

88.65

93.98

87.85

97.98

85.56

95.10

89.51

88.54

97.44

88.61

94.66

89.95

98.71

87.79

96.64

90.59

89.86

86.16

99.80

89.53

96.03

91.33

99.31

89.05

99.23

91.92

93.17

87.58

99.84

89.52

96.00

91.47

99.94

89.86

99.93

92.68

Figure 10: CapSense accuracy (in %) with Naive Bayes clas-sier for all the ten subjects given dierent accumulationwindow TC . (results with accuracy ≥ 85% are highlighted)

Stairs Walk Run SitStairs 0.47 0.34 0.19 0.00Walk 0.23 0.73 0.04 0.00Run 0.13 0.00 0.87 0.00Sit 0.00 0.00 0.00 1.00

(a) Subject 4, TC = 3s .

Stairs Walk Run SitStairs 0.92 0.08 0.00 0.00Walk 0.00 0.00Run 0.00 0.00 1.00 0.00Sit 0.00 0.00 0.00 1.00

0.950.05

(b) Subject 8, TC = 3s .

Figure 11: Confusion matrix of CapSense with Naive Bayesclassier for Subject 4 and 8. e TC = 3s.

1s 2s 3s 4s 5s 6s 7s

TC (s)

S1

S2

S3

S4

S5

S6

S7

S8

S9

S10

AVG

Sub

ject

65.00

64.73

82.02

68.45

84.95

80.67

84.55

71.00

72.97

76.02

69.86

76.00

72.71

83.00

83.22

83.33

84.85

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75.02

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78.63 77.94 80.85

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92.72

Figure 12: e accuracy (in %) achieved by CapSense givendierent subjects and accumulation windowTC . e resultsare the averaged accuracy achieved by the seven dierentclassiers.

perform the activities in dierent ways, and those dierences haveimpacted the CapSense performance. Fortunately, CapSense canstill achieve high classication accuracy for 90% of the subjectsbased on our evaluation.

4.3.2 Recognition Accuracy v.s. Accumulation Time. Now, weinvestigate the impact of accumulation time TC on the recognition

1s 2s 3s 4s 5s 6s 7s

TC (s)

NaiveBayes

C4.5

RandomForest

IBK

DecisionTable

BayesNets

AVG

Cla

ssifi

er

75.89

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92.93

92.69

92.29

92.72

Figure 13: e accuracy (in %) achieved byCapSensewith dif-ferent classiers given dierent accumulation window TC .e results are the averaged accuracy across the ten subjects.

accuracy. First, as we have shown in Figure 10, for a specic clas-sier (i.e., Naive Bayes), the achieved accuracy increases with TC .Similarly, Figure 12 exhibits the averaged accuracy achieved by thesix classier given dierent TC for all the subjects. We can see thatthe accuracy increases with TC irrespective of the classier. esecond observation is that, 9 out of 10 subjects can achieve over85% of accuracy with TC ≥ 6s. Intuitively, this is because a largeraccumulation window can lead to a more distinctive dierence inthe capacitor voltage. us, a large TC is preferable in improv-ing the classication accuracy. However, the sojourn time for asubject in performing a specic activity is short, and transitionsbetween activities may occur in the middle of TC . erefore, TCshould not be set too large that exceeds the activity sojourn time.For instance, during our data collection, we have noticed that, forascending/descending a 10-steps stair, the sojourn time is usuallywithin 8 to 10 seconds depending on the subject’s speed. us,the maximum value of TC is congured to 7 seconds based on ourcurrent dataset. Moreover, as reported in [36], for activities such aswalking, running, and standing/siing, the sojourn time is at least1 to 2 minutes. For activities such as ascending/descending stairs,the sojourn time is much shorter, but still longer than 5 secondsfor over 99.9% of the time. erefore, as a trade-o between thesystem performance and robustness, we recommend the maximumvalue of TC for CapSense to be congured to 5 seconds.

4.3.3 Recognition Accuracy v.s. Classifier. Lastly, we analyzethe performance of CapSense with dierent classiers. Figure 13exhibits the averaged performance of CapSense across all the tensubjects with dierent TC when apply dierent classiers. We canobserve that, all the six classiers can achieve over 85% of accuracywith TC ≥ 3s . With TC ≥ 5s , the average accuracy of CapSenseacross the 10 subjects will increase to 90% for all the classiers.With TC = 7s , CapSense is able to achieve over 92% of accuracywhich is comparable to the performance achieved by conventionalmotion sensor-based systems [25].

An interesting observation is that CapSense exhibits no bias onthe selection of classier, as all the six classiers achieved similarclassication results. In general, the selection of relevant featuresand the decision on how to construct a classication model on theselected aributes can have an enormous impact on the classi-cation result. us, for conventional accelerometer-based or KEHtransducer-based activity sensing systems [5, 18, 35], they require


very large feature set and complex feature selection algorithm tobuild the learning model carefully. Dierently, as CapSense doesnot require very complex feature sets and relies only on the voltagedierence for activity recognition, it shows no bias on the selectionof classier. erefore, it simplies the procedure in building theclassication model and minimizes the errors that could result fromselecting an inappropriate classier.

5 POWER CONSUMPTION ANALYSISBaery lifetime is the major roadblock for the mainstream uptakeof wearable technology6, and system energy consumption optimiza-tion is an active research area for both academics and industries.Existing eorts [18, 23, 35] have demonstrated the superiority ofKEH transducer-based sensing system in energy saving. Compar-ing to accelerometer-based system, KEH transducer-based systemcan save the energy consumption in power energy-hungry motionsensor. In this section, we will conduct an extensive power con-sumption proling of o-the-shelf wearable activity recognitionsystems. We will demonstrate that, for KEH transducer-based sys-tem, a non-negligible amount of system energy consumption stillcomes from powering the MCU for sampling the AC voltage signal.As KEH transducer-based activity recognition system still requirestens of Hz sampling frequency, it turns out that the MCU needsto wake-up and polling the signal from the transducer frequently.In contrast, by appropriately use the properties of the capacitor,CapSense enables system level power saving by enabling the MCUto stay in the energy-saving low-power mode for extended periodsof time comparing to the state-of-the-art KEH transducer-basedsystem. Consequently, we will show that CapSense can reduce thesystem power consumption by several factors.

5.1 Measurement SetupWe use an o-the-shelf Texas Instrument SensorTag7 as the targetdevice, which is embedded with the ultra-low power ARM Cortex-M3 MCU that is specically designed for today’s energy-ecientwearable devices8. e SensorTag is running the Contiki 3.0 operat-ing system9 which duty-cycles the MCU to save energy. To furtherreduce the power consumption of MCU in processing, a lower clockfrequency of 12MHz is used instead of the default 48MHz. More-over, all unnecessary components, including the onboard ADC, SPIbus, and the on board accelerometers are powered-o when it is notsampling. e SensorTag is connected with a 10Ω resistor in seriesand powered by a 3V coin baery. e average power consumptionand time requirement for each sampling event are measured byusing the built-in function in the Agilent DSO3202A oscilloscope.We are interested in the power consumption of SensorTag in datasampling (i.e., either in sampling the capacitor voltage or the ACvoltage signal from KEH transducer), and the power consumptionin data transmission. In the following, we present our measurementresults of those parts in order.

6hp://www.theverge.com/2016/1/15/10775660/tness-tracker-smartwatch-baery-problems-apple-motorola-lumo (accessed on July 29th 2016).7SensorTag: hp://www.ti.com/ww/en/wireless connectivity/sensortag/.8Mainstream wearable devices such as FitBit are using ARM Cortex-M3: see hps://www.ixit.com/Teardown/Fitbit+Flex+Teardown/16050.9Contiki OS: hp://www.contiki-os.org/.

S_sample

S_sleep S_sleep

MCU wakes up to sample periodically.

MCU in deep-sleep mode.

Figure 14: Proling of voltage sampling.

Table 1: States of MCU in sampling the ADC signal.

StateTime(ms)

Power(µW)

Description

Ssample 0.6 480 MCU wakes up to sample ADC signal.Ssleep null 6 MCU in deep-sleep mode.

Table 2: e power consumption (µW) in data sampling.

KEH transducer-25Hz CapSense-0.14HzSensing 7.11 0.04MCU Leakage 6 6Overall 13.11 6.04

5.2 Power Consumption in SamplingFirst, we investigate the power consumption in data sampling. Inthemeasurement, both capacitor voltage and KEH transducer signalare simpled through the on board ADC of SensorTag. e samplingfrequency of MCU is congured as 25Hz to meet the requirementof KEH transducer-based sensing system. Figure 14 presents anoscilloscope trace when MCU sampling the signal from ADC pe-riodically. As shown, MCU is triggered by the timer to sampleperiodically. It takes approximately 0.6ms for the MCU to com-plete a single voltage sampling event (i.e., state Ssample shown inFigure 14). Aer that, MCU turns back into the deep sleep mode(i.e., LPM3 in Contiki OS) to save power. e average power con-sumption of the system for a single ADC sampling is 480µW, andthe baseline system power consumption when MCU is in the deep-sleep-mode is only 6µW. e details of power consumption andtime duration for MCU sampling the ADC signal are summarizedin Table 1.

In general, for the duty-cycled activity sensing system, the aver-age power consumption in data sampling, Psense , can be obtainedby the following equation:

Psense =

TS×n1000 Psample + (1 −

TS×n1000 )Psleep if 0 ≤ n ≤ 1000

TS,

Psample if 1000TS

< n.(5)

where, Psample is the average power consumption of the systemduring the sampling event, and Psleep is the average power con-sumption when the MCU is in deep-sleep mode (with all the other

https://www.ifixit.com/Teardown/Fitbit+Flex+Teardown/16050

https://www.ifixit.com/Teardown/Fitbit+Flex+Teardown/16050


S_sleep

S_sleep

S1

S2 S3 S4 S5 S6

S7

Figure 15: Proling of BLE broadcasting event.

system components power-o). n is the sampling frequency, andTS is the duration of time (in milli-second) required by a singlesampling event. Based on the measurement results given in Ta-ble 1, we can have the average power consumption for KEH voltagesampling event, Psample , equals to 480µW with a duration, TS ,equals to 0.6ms. e power consumption when MCU in deep-sleepmode, Psleep , is 6µW. For KEH transducer-based system, givendierent application scenarios, a sampling frequency of 25Hz-50Hzis required to achieve good accuracy for human activity recog-nition [17, 18, 23, 35]. erefore, given the minimum requiredsampling frequency of 25Hz, following Equation 5 we can obtainthe power consumption in data sampling for KEH transducer-basedsystem equals to 13.11µW. On the other hand, as demonstratedin Section 4, to achieve an overall classication accuracy of 90%,CapSense need only to sample the ADC signal once every 7s. Fol-lowing Equation 5, the power consumption in data sampling forCapSense is only 6.04µW.

e results are summarized in Table 2. As shown, CapSenseis able to save 54% of the overall power consumed by thetransducer-based system in data sampling. It is noteworthythat, in case of Sensing, the power consumption of CapSense isonly 0.6% of the KEH transducer-based system (i.e., 0.04µW7.11µW ). Wecan also notice that for CapSense, the main energy expenditureis the MCU Leakage (i.e., the power consumption of the systemwhen MCU is in deep-sleep mode), which consumes 99% (6µW over6.04µW) of the overall sampling power consumption. Fortunately,with the rapid development of energy-ecient micro-controllers,we can expect the power consumption in System Leakage could befurther reduced. For instance, the EFM32 Gecko MCU10 consumesonly 2.7µW in the deep-sleep mode, it can help CapSense achievingan ultra-low system power consumption of 2.71µW .

5.3 Power Consumption for Data TransmissionIn this subsection, we study the power consumption for the wire-less transmission using the BLE beacons in broadcasting mode. Weprogrammed the Contiki OS which wake-ups the CC2650 wirelessMCU periodically to transmit a BLE beacon packet, which is re-peated three times on three separate channels (repetition improvesreliability of broadcasting). e transmied beacon packets areall 19 Bytes (all for protocol payloads. e details for each BLEbroadcasting event are visualized in Figure 15, and summarizedin Table 3. Note that, the transmission time is depending on thepacket size. e time stated in Table 3 (0.28ms for state S2, S4, and10EFM32 Gecko 32-bit MCU: hp://www.silabs.com/products/mcu/32-bit/efm32-gecko.

Table 3: States of BLE broadcasting event.

StateTime(ms)

Power(uW)

Description

S1 1.12 1008 Radio setup.S2, S4, S6 0.28 3990 Radio transmits a beacon packet of 19 Bytes.S3, S5 0.30 2460 Transition between transmissions.S7 1.72 744 Post-processing before sleep.S sleep null 6 Radio o; MCU in deep-sleep mode.

Table 4: Summary of data transmission power consumption.

KEH-25Hz CapSense-0.14HzPower 3.212mW 1.716mWTime 33.9ms 4.3msEnergy 108.88µ J 7.43µ J

S6) is the minimum required time to transmit the 19 Bytes packetper channel. For every additional Byte11 to be transmied, 8µs timeneeds to be added to the total transmission time.

For transducer-based system with a sampling frequency of 25Hz,it has 25Hz × 7s = 175 voltage samples (2 Bytes for each 12-BitsADC reading, and 350 Bytes in total) to be transmied per Channelfor every seven seconds. Given the maximum additional data canbe added to each beacon packet is 28 Bytes, this requires d 35028 e = 13packets to be transmied per channel. As a result, for transducer-based system, it consumes 108.88µJ12 to transmit the 13 packetson three dierent channels. e average power consumption is3.212mW with time duration of 33.9ms . On the other hand, forCapSense, it has only one voltage sample to be transmied forevery seven seconds (in total, 2 Bytes), results in one packet tobe transmied per channel. e total energy consumption forCapSense is only 7.43µJ13. e average power consumption is1.716mW with time duration of 4.3ms . e results are summarizedin Table 4. is means that, CapSense is able to save over 93%of the energy consumption in data transmission. Clearly, forKEH transducer-based systems, the radio has to stay for a longerperiod of time to transmit more sampling data. Although, dierenttransmission mechanisms (data aggregation and feature selection)can be applied to reduce the amount of data to be transmied [25],and thus, reduce the transmission power consumption. However,additional on-board computations in those mechanisms may stillintroduce inevitable power consumption. Combining the powerconsumption in data sampling and transmission together, the over-all system power consumption for KEH transducer-based systemis 28.53µW 14, whereas, the overall system power consumption forCapSense is only 7.09µW15. is means that CapSense is able tosave 75% of the overall system power consumption of state-of-the-art KEH transducer-based system.11According the protocol, up to 28 Bytes of data could be added to the 19 Bytes payloadsper packet.12Obtained by: 1.12µs ×1.008µW +1.72µs ×0.744µW +39×(0.28µs +0.008µs ×28) × 3.99µW + 38 × 0.3µs × 2.46µW = 108.88µ J .13Obtained by: 1.12µs × 1.008µW + 1.72µs × 0.744µW + 3× (0.28µs + 0.008µs ×2) × 3.99µW + 2 × 0.3µs × 2.46µW = 7.43µ J .14Obtained by:( 13.11µW ×7sec+3.212mW ×33.9ms

7sec+33.9ms ) = 28.53µW .15Obtained by:( 6.04µW ×7sec+1.716mW ×4.3ms

7sec+4.3ms ) = 7.09µW.


6 CONCLUSION AND FUTURE DIRECTIONSIn this paper, we present CapSense, a novel activity sensing schemefor KEH-powered wearable devices. By simply using the voltagereadings of the capacitor which is sampled at a frequency down to0.2Hz, CapSense is able to achieve over 90% of accuracy in classi-fying dierent daily activities, and reduce sampling-related powerconsumption of the state-of-the-art system by 99% and the overallsystem power consumption by 75%.

e current work is a rst step in capacitor-based human activityrecognition. As such, it can be extended in many directions. First ofall, we have only evaluated its potential under the ideal conditionwhen activities do not overlap within the sampling interval. Inthe future work, we will investigate solutions that address themore practical cases where activity changes occur at arbitrary timeboundaries. Second, as the current hardware prototype is quitecumbersome, another direction for future work is to design theprototype with a smaller form-factor, and provide detailed userstudy on the practical user experience of this device. Finally, asour hardware can harvest energy from dierent user activities, wewill investigate ways to utilize the harvested energy to power oursystem, thus making it baery-free.

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