SiNOxSense: A textile-based wearable simple NOxsensing system

Kasper Benjamin HansenIT-University of Copenhagen

Rued Langgaards Vej 7DK-2300 Copenhagen S

kben@itu.dk

Natalia KuraszynskaIT-University of Copenhagen

Rued Langgaards Vej 7DK-2300 Copenhagen S

naiz@itu.dk

ABSTRACTAir pollution has significant negative effects on our health.We aim to improve the functionality, and encourage the useof air sensor systems by introducing a sensor system featur-ing a novel sensor unit design that is wearable, textile-basedand compatible with existing mobile technologies. We basedthe functional requirements of our system on results from aliterature search for wearable, portable and mobile air qual-ity sensor systems. Our system was iteratively designed andevaluated and comprises of the wearable, textile-based sen-sor unit, an Android app and a Google App Engine (GAE)website. Our research show, that our textile-based designis preferred compared to related projects, and that textile-based designs might encouraged people to use mobile sens-ing technologies. However people are more likely to usesensor technologies, if it provides multiple functions, is in-corporated into everyday items and direct feedback is givenempathically and with consideration to avoid stress and/orhysteria.

Author KeywordsAir quality sensing, mobile participatory sensing, crowd basedsensing, wearable computing, NOx pollution, textile.

INTRODUCTIONConcerns for air pollution and its impact on human healthare increasing. Nitrogen oxides (NOx) can penetrate deeplyinto sensitive lung tissue and damage it, causing prematuredeath in extreme cases [23].

In order to raise the awareness of air quality, it is desir-able for citizens to be involved in process of monitoring pol-lution. Development of mobile and ubiquitous computing(ubicomp) opens new possibilities to collect geospatial sen-sor data by users, directly from their environment [25].

In order to cause improvements in air quality, communityand government actions are required. In Denmark, air pol-lution was recognized as a public health problem alreadyin the 1960s. Although there has been great progress onreducing air pollution in recent decades, it remains a chal-lenge, especially for public health [20]. Moreover, in orderto achieve this, pollution concentrations must be monitored

Paper submitted for evaluation in the Pervasive Computing Course, Fall2012, IT-University of Copenhagen. Copyright remains with the authors.

accurately and ideally in situ so that sources may be iden-tified quickly and the atmospheric dynamics of the processare understood [10].

In [3] it is illustrated how the framing of environmental sens-ing and data collection can be expanded to encourage partic-ipation in public discussion of political issues. We believethat by designing a crowd-based air quality sensor system,we will empower people with the ability to participate ina gathering revolution that ultimately can improve people’shealth and quality of life. It will give them the possibility oflooking over the shoulders of lawmakers and politicians inregards to officially measured emission levels [21].

Our research goals are; a) perform a literature search to dis-cover air quality system sensing functionalities, b) build aprototype to encompass the functionalities, c) follow a userdriven, iterative method for prioritizing the functionalitiesand evaluating if they are achieved, d) design a wearable,textile-based sensor unit and evaluate if these properties makesdaily use more likely.

In the following part of this paper after describing relatedwork, we present our method and the iterative design pro-cess. We also describe the architecture of the SiNOxSensesystem and its functionality. We present our design deci-sions and challenges we encountered during the design pro-cess. Finally we discuss considerations and opportunities forfuture work.

RELATED WORK

Environmental SensingA lot of research and development has been conducted en-compassing environmental sensing technology. Environmen-tal sensors evolved from automated loggers that record dataat specific intervals and require manual downloading by amaintenance team, to wirelessly communicating units thatare able not only to share recorded data but also performpreprocessing required for further analysis [11].

Mobile Participatory SensingBased on our research we can observe a significant increaseof interest in a environmental mobile sensing. In 2005 a uni-versity group from University of Glasgow was working on aproject, where users could gather air pollution data by car-rying a networked tablet PC and a sensor unit with attached

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GPS [24]. A map displayed on a tablet PC would show pol-lutant values as the user moves around the city.

In [15], GPS receiver and sensor datalogger were used incombination with a mobile phone. GPS was implemented asan external hardware component, and geotagging of pictureswas possible.

A more intuitive and user friendly setting was proposed inN-SMARTS [13] where a sensor module was attached to aGPS enabled mobile phone.

As SiNOxSense, all systems mentioned above have adapteda solution of Bluetooth communication between the sensorunit and a mobile phone.

Crowd Based Air Monitoring in CopenhagenIn 2009, SENSEable City Lab for Koebenhavns Kommune(municipality of Copenhagen) revealed the Copenhagen Wheelproject [22]. Sensor technology was located in the bicycle’swheel, making it function as a mobile sensing unit and pro-viding feedback on air pollution in real-time. Gathered datacould be accessed through the users’ smartphones and usedto plan healthier bike routes. Data could be shared withfriends or anonymously with the city, thereby contributingto a fine-grained database of environmental information.

In 2011, a group of students from the IT University of Copen-hagen conducted the NoxDroid project [6]. A sensor unitwas integrated into a popular bike light product, that couldbe attached to the handlebar of a bicycle. It communicatedthrough a USB cable to an Android mobile phone. Nox-Droid consisted of a MQ-135 Air Quality sensor, LEDs forproviding feedback and a IOIO board to control the sensorand LEDs. A huge drawback in NoxDroid were the verylimited battery time. Using USB between the sensor unitand the phone made the sensor device behave as a USB host,charging up the connected phone with power from the Ar-duino batteries.

A key differences between SiNOxSense and NoxDroid isthat our sensor unit is textile-based. It can be used not onlywhen biking but also when walking, running etc. Users canattach it to their clothes, fashion accessories or, for example,a pram. Another key difference is that SiNOxSense commu-nicates wirelessly via Bluetooth between the sensor unit andthe phone, compared to NoxDroids cable connection - andour battery life is multiple that of NoxDroid.

Wearable Environmental Sensor TechnologyA significant amount of work on wearable environmentalsensor technology has already been done. The Common-Sense project group designed a handheld device that has car-bon monoxide, ozone, NOx, temperature and humidity sen-sors [2]. AIR [1] is a public social experiment conductedin 2006, in which people were invited to use a portable air-monitoring device to explore their neighborhood and urbanenvironments for air pollution and fossil fuel burning hotspots. Finally a few mobile/wearable air quality sensors,similar to SiNOxSense, can be currently found on the mar-

Figure 1. The functional requirements for SiNOxSense, extrapolatedfrom the sensor systems described in the related work section.

ket. One of them is Sensaris [26], a GPS- equipped andBluetooth-enabled sensor that can be worn on the wrist andallows users to measure real-time air quality.

The appearance of electronic textiles has encouraged manyusers to develop different devices and they are an increas-ingly important part of pervasive and wearable computing [7].One of the most common programmable microcontroller boards,designed for wearable projects and e-textiles is the Arduinoboard called LilyPad [19].

The idea of using Lilypad Arduino with combination of en-vironmental sensors was investigated in the WearAir project[17]. An expressive T-shirt was designed to sense the airquality and publicly express those levels through visual pat-terns on the shirt. The sensor data is not recorded, and it isnot really possible for the wearer to get direct feedback, un-less walking head down. We based SiNOxSense on Lilypaddue to the textile nature of this project.

We extrapolated the overall functional requirements of allthe above mentioned projects (see Figure 1) and let themform the base of SiNOxSense. A feature comparison overviewfor all the described systems can be observed in Figure 2.

SINOXSENSE DESIGNSiNOxSense design was based on the overall functionalitiesstated in Figure 1. We worked towards making it easy togather, share and receive feedback on air pollution sensordata. Our conceptual idea is that people wear a textile-basedsensor unit on them or on their belongings whenever theywalk outside. While being in use the sensor unit collectsdata about the air pollution which can be shared with othersvia the Internet. If the pollution crosses a threshold, the userwill get direct feedback through their mobile phone.

After doing research on available sensor technology we de-cided to measure NOx and to use the MQ-135 Air Qual-ity sensor (the same sensor that NoxDroid employs), basedon the similarity between our projects, price and availabil-ity on the market. NOx is a highly relevant chemical com-pound for environmental sensing, especially in urban set-tings. Our choice is furthermore motivated by recent re-search concluded by Danish scientists. It has been demon-strated that short-term exposure to air pollution can trigger

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Figure 2. A feature comparison between portable air quality sensingsystem.

hospital admissions for asthma in children [14]. A signifi-cant association was found between hospital admissions forasthma in children aged 0 to 18 years and NOx emissions.

MethodBased on the performed literature search, a prototype systemwas designed. The system and the sensor unit were thenevaluated. Our method is described in more detail below.

1. We conducted a literature search on wearable, portableand mobile sensor system. The most relevant of these sys-tems were described in the related work section (see Fig-ure 2), and used as a base for the proposed system func-tionalities.

2. We built a prototype system consisting of the sensor unit,an Android app and a GAE website. All the stated func-tionality (see Figure 2) was developed - although due totime constraints it was not integrated to a functioning whole.

3. In order to design, implement and evaluate our system wefollowed the Iterative Design Model [5]. We devised athree staged evaluation, where each preceding stage feedsthe next, which is described in more detail in the stepsbelow.

· Scenario and 1st questionnaire: Conclusions fromthe literature search and NoxDroid’s ”Questionnairefor test users of the NOxDroid” material combinedwith a brainstorming session resulted in a scenarioand a set of system features which was evaluated bya questionnaire.

· Video prototype and 2nd questionnaire: A video pro-totype was made in order to show our implementa-tion of SiNOxSense system features. The scenariodeveloped in the first iteration served as a basis for astoryboard. Another questionnaire was used to eval-uate the usability of the portrayed system and to see,if the respondents agreed with the proposed way ofachieving the functionalities (see Figure 1).

· User evaluation and open ended interviews: The userevaluation consisted of; a) presenting SiNOxSense

by showing the video prototype, b) presentation ofvisual material from the related systems (see Fig-ure 2) that we use for comparison, c) physical inter-action with the sensor unit and d) open ended inter-views. We wanted to evaluate if the textile and wear-able properties of the sensor unit would encouragethe use. Additionally, we asked for further inspira-tion on how to improve the appearance of the sensorunit to increase the probability of daily use.

ScenarioAnna lives in Copenhagen and is concerned about her health.She would like to learn about air quality in her location be-cause she walks and bikes a lot. She finds a website with in-formation about air quality in Denmark provided by AarhusUniversity [4], but is disappointed to see that there are onlythree sensors in the whole of Copenhagen. She finds theSiNOxSense website with a online map showing pollutionlevels based on the data gathered and shared by other peopleusing their wearable sensor units. After a couple of weeksof using their data for her decisions regarding her transporta-tion routes, she learns that by having a SiNOxSense sensorunit, it is possible for her to get instant, direct feedback aboutair quality. She relish the idea of being a part of the system,contributing data for the common good. She decides to geta sensor unit and after a quick setup she steps outside andstarts sensing the air quality. When she gets home in theevening, she charges her sensor unit using one of her stan-dard USB wall socket chargers, and the sensor automaticallyuploads and shares her sensor data with the rest of the worldwithout requiring her attention at all. She is happy to learnmore about the air quality around her while contributing withsensor data for the common good.

1st questionnaireThe questionnaire consisted of 18 mandatory questions di-vided into 7 main categories:

1. Personal information about the user (sex, age etc.)

2. The mobile phone characteristics

3. Concerns about air pollution in regards to children

4. Medical questions about asthma and respiratory problems

5. General concerns about air quality and air pollution

6. Rating the SiNOxSense idea

7. Several ratings of specific properties of the sensor unit

The questionnaire was distributed among friends, contacts,co-workers, ex-colleagues etc. that live in Denmark, usingboth social medias and email for recruiting. The survey wascompleted by 66 participants (37 males, 29 females) fromwhich 66,66% were between 25 and 40 years old.

The respondents were in general positive about the idea;48,48% rated the idea higher than ”average” with a total of15% being completely positive about the system.

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Figure 3. The rating of SiNOxSense’s features by questionnaire respon-dents.

19,69% of all respondents were very negative about the sen-sor idea. One person claimed that it could cause hysteriaand provoke mental health issues. We find this to be an in-teresting point of view, which requires additional research infuture works - especially regarding empathically designedGUI and direct feedback functionality.

More than half (61,53%) of those who were negative aboutthe idea did not like the concept of using social medias toshow their concern about health and air quality. We also sawthe first privacy concerns relating to data sharing.

79% of all recipients had at one time worried about the airquality around them, and of those 75% mostly worry whenthey are outside. This confirmed our initial assumption thatwe should focus on measuring outdoor air pollution.

About one third of the respondents were using a phone withAndroid OS, and one third is using iOS. 71,21% of all re-spondents can download apps. We decided to develop anAndroid application, based on prior experience with the Javaplatform.

We asked the respondents to rate the features (see Figure 3).

Battery life was rated the most important feature. Users ex-plain that they would prefer not to have to charge the deviceand/or having it run forever. This cannot be achieved withtechnologies available for us at the moment, therefore we fo-cused on limiting the battery consumption. One of our earlydecisions was to try to make charging simple for the user,by making it possible to use a standard USB wall socketcharger.

Direct feedback was rated second highest. The most com-mon suggestions were; phone alarm, vibration, sms mes-sage, light signal, shown on a map with calculation of aless polluted alternative route, customizable (eg phone soundwith possibility to change it into vibration only), lights andbeeps on the sensor device and a report via email. All of thesuggested features are possible to develop and it remains atask of prioritizing which features takes precedence.

Online Map was rated the third most important. Some re-spondents came to the conclusion that gathering environ-mental data is beneficial for raising awareness of the influ-ence of air pollution in healthy lifestyles and that it couldlead to important political changes on the decision makingconcerning the environment. This directly corresponds toone of the desired functionalities (see Figure 1). Few respon-dents mentioned privacy-related concerns, and therefore acustomizable privacy protection mechanism in relation tomapping and social medias should be analyzed properly infuture work.

Appearance was rated the least important feature. An in-teresting observation is that the rating of the sensor units’appearance is equally low for both sexes. The analysis re-vealed that it should be small, discrete and be possible toattach to clothes, bags, a bikes or similar.

Video prototype and 2nd questionnaireThe video prototype was followed by the second question-naire that was distributed in the same way as the first ques-tionnaire and completed by 49 respondents. In 29 closedquestions we aimed to:

· verify if respondents gained good understanding of thesystem

· learn if respondents agree on our vision of the purposeof using SiNOxSense and if they agree that our systemachieve the functional requirements (see Figure 1).

· get a preliminary feedback to the possibility of changingone’s behavior after using SiNOxSense and recommend-ing the system to others

· learn about the respondents general feelings about usingSiNOxSense with the help of the word-pairs (eg practical-impractical, cumbersome-straightforward, rejecting-inviting)

· see if people agree/disagree on our planned way of achiev-ing the functions

96% of all respondents claim to gain good understandingof the general idea of using SiNOxSense after watching thevideo.

We asked the respondents to rate previously stated func-tional requirements (see functionality 1-5 from Figure 1).From the results (see Figure 4) we can see, that most respon-dents agree that using SiNOxSense works toward achievingthe functional requirements, and many of them think that itworks excellently. The functional requirement of ”Improv-ing the health and quality of life for the user” was ratedlowest by the respondents, with a total of 42% being pos-itive towards that functionality being provided. Improvingthe health and quality of life is an ambitious and long termgoal, and might only be truly evaluated if the system is putto large scale use.

78% of all respondents were willing to change the routesthey used daily in order to lower their exposure to air pollu-tion. 78% thought that their everyday behavior would change

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Figure 4. The rating of SiNOxSense’s functional requirements by ques-tionnaire respondents.

if they used SiNOxSense and 83% believed that it would be amotivating change. We believe we can argue that SiNOxSensehas the potential to improve people’s health and life quality,however evaluating this goes far beyond the scope of thisproject and we regard it as future work.

98% of all respondents agree that it is possible for SiNOxSenseusers to measure, record and share data about the air pol-lution data in their environment. We asked respondents torate some of the usage-related aspects with the help of sixword-pairs - inspired by AttrakDiff questionnaire [12]. At-trakDiff is used to measure attractiveness and usability ofthe concept by using word-pairs which simplify the ratingprocedure. The results are as follows:

Simple (80%) vs. Complicated (20%)Practical (64%) vs. Impractical (36%)Useable (80%) vs. Unusable (20%)Straightforward (84%) vs. Cumbersome (16%)Integrating (82%) vs. Alienating (18%)Inviting (86%) vs. Rejecting (14%)

Although the general outcome for all word-pairs is posi-tive, there is still room for improvement in order to makeSiNOxSense even more user friendly. This concern was ad-dressed in the next iteration in the user evaluation.

In the introduction section we argue that in order to causeair quality improvement on a bigger scale, community andgovernment actions are required and that a productive in-volvement of citizens might be useful for stirring up politicalengagement. We wanted to check, if respondents agree withthis assumption. 84% of all respondents answered that ifSiNOxSense will be used by a lot of people, this mass atten-tion on air quality might initialize some political decisionstowards lowering the allowed pollution emissions.

Results from the first questionnaire showed that some of therespondents were worried about the privacy of their data.52% thought that anonymization of their geospatial data isimportant but 20% answered ”Don’t know / don’t care”. Thereis a functionality tradeoff between privacy and the use of so-cial medias.

72% found charging the sensor unit with a standard USBcharger to be a professional solution, which we believe makesit more likely that users will charge the sensor unit and therebyfacilitate the use of SiNOxSense.

User evaluation and open ended interviewsWe interviewed 10 people, aged 20 to 44, all chosen ran-domly. 8 of them were students.

SiNOxSense was perceived as simple and straightforward touse. It was also found to be practical - although only useablefor certain target groups - most of the interviewed peoplewould not use it by themselves because they lacked interestin air quality.

Due to the relatively clean air quality of Copenhagen, somebelieved the system would be more usable in more pollutedcities. We received valuable feedback about improving theusability, most noteworthy that it should be extended withbody monitoring functionality.

The textile-based design was received positively, some re-spondents found it to be more environmental friendly andgreen. The response was that making the sensor unit textile-based can encourage some people to use sensor technolo-gies. Only one person liked another system over SiNOxSense,all others favored the SiNOxSense design. This indicatesthat if they would use an air quality sensing system, theymight choose a wearable and textile-based sensor unit.

When compared to the other systems, SiNOxSense was foundto be more flexible in regards to customization, more play-ful and fun, softer, cozier and more discrete. It was alsoperceived less likely to break if dropped on the floor, butconcerns about cleaning the fabric and resistance to waterwere made. Some respondents noted, that it would be goodto provide different covers for the sensor unit (colorful, ele-gant, sophisticated) so they could match sensor appearancewith their own look and mood.

Regarding wearability, respondents liked that the sensor unitwas small, discrete and innocently looking. It was deemedpractical and easy to carry around, however it would be morepractical if smaller, and integrated into everyday items suchas key chains, clothes or bike helmets.

RequirementsThe requirements for the system based on the results fromall iterations:

Sensor unit:

· small (should fit the palm of the hand)

· light weight (should way under 50 grams)

· easily attachable to clothes, pram etc

· long lasting battery (at least 8 hours)

· textile-based and wearable

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Figure 5. The SiNOxSense architecture on the left, and the textileArduino-based sensor unit, the Android app and the GAE website onthe right.

Android app:

· customizable direct feedback feature if the air quality cros-ses a threshold

· work in a simple and unobtrusive manner

Website (GAE):

· presents air quality data on a map

· be searchable by location and time

SYSTEM ARCHITECTUREThe entirety of SiNOxSense consists of three connected parts;1) the Arduino sensor unit, 2) the Android app and 3) theGAE website (see Figure 5). The system was developed inJava 1.6 SE, using Android SDK 8 and Google App Engine1.7.2.

The sensor unit (Figure 5) is turned on by holding the pushbutton (hidden inside the striped textile corner) for more thantwo seconds, and will immediately connect to the Androidapp via Bluetooth, and request a new location id. The lo-cation id will be used as a filename for storing the sensorreadings on the sensor unit’s internal memory card, therebymaking it possible to combine the persisted location datafrom the Android app with the sensor readings. Two setsof timestamps are used for combining the sensor readingswith the locations. The sensor unit devoid of its textile cas-ing can be seen in Figure 6. The sensor unit flow can be seenin Figure 7.

When the sensor unit is connected to a power source, it startstransferring the persisted recordings to the Android app. Af-ter each successful transfer, the file will be deleted from thesensor unit to avoid redundant uploads and save space onthe memory card. The Android app will match each sen-sor reading with a corresponding location by matching thetimestamps recorded by the location service and the sensorunit. The Android app flow can be seen in Figure 8.

CHALLENGES & DESIGN DECISIONSAs it is well known in the area of ubicomp, the systemsface multiple challenges [18] - many of which applies toSiNOxSense. In this section we will go through the design

Figure 6. The sensor unit is measuring 65mm x 50mm - having the1000mA battery glued to the back of the Lilypad circuit board.

challenges related to all three parts of SiNOxSense and thedesign decisions based on the discovered challenges.

The overall challenge of the entirety of a system spanningthree realms is how we make it easy and simple for the userto record sensor data, combine it with the location and up-load it to the website in one understandable process. Basedon our brainstorm and the 1st iteration feedback and Nox-Droid related material, we started the system flow designearly in the project to encompass this overall challenge. Thisis then indirectly evaluated in the video prototype question-naire, and more directly in the final evaluation.

The highest ranking feature requested by users is batterylife, therefore we find it prudent to show the sensor’s powerbudget, which was calculated by using the consumption val-ues published by the component manufactures. Future workshould include high sensitivity ammeter measurements doneon the actual electronic components. The below list does notcontain the power requirements for the Bluetooth module,since it is only really used when the sensor unit is connectedto a power source. While the sensor unit is on battery power,it is only used when direct feedback is needed (and eventhen, only for a short burst of data) - so the vast majority ofthe battery consumption stems from;

· 100mA - The sensor heating element

· 4,5mA - Arduino Lilypad

· 6mA - OpenLog

This uses in total 110,5mA, which will theoretically give usaround 9 hours of usage with our current 1000mA PolymerLithium Ion Battery. For only 14 grams extra, we can doublethat amount of sensor time, by using a 2000mA battery. Inpracticality, we will have even more sensor time than listedabove, due to the fact that OpenLog is using only 2mA whenidle, and since we are not recording sensor data continu-ously, but relying on a timer function to make a recordingevery 30 seconds, a more realistic power consumption ofOpenLog is 2,16mA which will give us about half an hourextra with the smaller battery.

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Figure 7. An illustration of the software flow on the sensor unit.

The sensor unit comprised of these challenges;

· Using the same serial connection for multiple components- and avoiding having to disconnect them while program-ming the board

· Conserving energy and updating the battery status

· Maintaining data consistency while handling multiple record-ing series

· Making it unlikely that it runs out of battery while trans-mitting the data to the Android app

· Choosing an appropriate communication framework

Many Arduino boards only provide one serial connection,consisting of a transmit and a receive pin (rx and tx), andthat same serial connection is used when programming theboard. Our solution uses the LilyPad FTDI Basic Breakoutwhich makes it unnecessary to sever any onboard connec-tions with the serial when programming the board. Also, bycontrolling power to each component (the Bluetooth mod-ule and openlog), we determine which component shouldreact on the serial communication - since the components’serial connections were soldered together. By controllingthe power for each component (which we made possible inthe way that the sensor flow (see Figure 7) functions) wealso save battery, avoiding component idle time. It was nec-essary to build a transistor circuit, since the Bluetooth mod-ule’s power requirement exceeded the standard output givenby any port. For future implementations of SiNOxSense it

Figure 8. An illustration of the software flow on the Android app.

might be advisable to use smaller hardware than Lilypad -which we only chose because of our intention of examiningthe wearable and textile-based design on system usage.

Data consistency was a priority, along with usability, andin some ways these can be mutually exclusive. For exam-ple; the sensor unit can be thought of as having two states;recording and uploading. Clearly, we want the sensor unit towork seamlessly between those two states, preferably withno user involvement other than oblivious, non-technical us-age of the device. We achieved this by connecting the FTDI’sDTR to a port, continuously checking if it is high. If so, weknow that the sensor unit has been been plugged into a powersource, and it has been set in the uploading state. This alsomakes it unlikely that the device runs out of battery whiletransmitting the data, providing a degree of data consistency.

We also wanted to use the built in charging circuit [8] STATfunctionality, to let the user know when the battery was fullycharged. Unfortunately there were several problems relatedto this. For example, this circuit does not seem to changebetween states automatically but needs a restart - which alsoproved challenging solving by a hardware based solution(since a software based reset does not put our componentsin the right reset state). We intend to solve this in futurework.

Regarding the actual communication between the sensor unitand the Android app, we had initially expected to use Amarino[16]. Unfortunately Amarino does not have connect / dis-connect functionality, and since we want to initiate the com-munication from the sensor unit - and we want to make surethat we receive an answer (in this case turning on the An-droid location capturing service and returning a location id)before we continue in the program flow, we ended up de-veloping our own Bluetooth server in Android and commu-

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nicating with it using stream read/write operations. Futurework might simplify this code.

For timestamping sensor readings, we are using Arduinosmillis() function that gives us a number of milliseconds sinceboot. For location timestamping, we are saving the start timewhen returning the location id and then using System.current-TimeMillis() at regular intervals corresponding to the inter-vals used for sensor readings. Each location timestamp isthen substracted from the start time leaving a number ofmilliseconds since the return of the location id. These twotimestamps can be skewed a bit as they are not entirely reg-ular due to code mechanics. Therefore we needed to find away to match them against each other in order to combinethe sensor readings with the location. We assume the viewon our data that each set is worthless without the correspond-ing set, i.e. a sensor reading without location informationis regarded as worthless. We therefore iterate through therecorded locations and select the sensor reading with; a) thesmallest variation in the timestamp compared to the millisec-ond of the location and b) a maximum variation between thetwo timestamp. Our algorithm assures a simple form of con-tingency management [18] since if either device runs out ofbattery, we still successfully merge the data that contain bothsensor readings and location information.

The Android app comprised of these challenges;

· Battery life (should not drain the mobile phone)

· Receive wireless uploads from the sensor unit

· Should be ready to serve the sensor unit whenever it isturned on

· Provide a customizable privacy protection mechanism

· Deciding on a appropriate direct feedback mechanism

Mobile systems need to be energy efficient, and so doesSiNOxSense - not only in the sensor unit but also on theAndroid app. The most energy consuming components ofSiNOxSense’s Android app are Bluetooth and the locationservices. We decided to use Android’s network provider andcoarse location resolution for recording the location of thephone. By doing so we believe that we can save some bat-tery, although network provider is less accurate than GPS.Since our system relies on the ability to give direct feedbackto the user (by means of a datastream triggering sound onthe phone) the Android app needs to have Bluetooth enabledat all times. Moreover, having Bluetooth enabled allows forthe sensor unit to upload data at all times, by sensing if thedevice is connected to external power or not. We could haveopted for a less elegant flow design by having the user man-ually enable and disable Bluetooth when an upload is immi-nent, but in order to keep the system unobtrusive we chosenot to. The GAE website comprised of these challenges;

· Time-parameterized location search

A map feature for SiNOxSense does not make sense if it isnot searchable by time. We use a time as a parameter in

searches done backend, but only in the form of a proof ofconcept. This can be heavily refined in future work.

LIMITATION AND FUTURE WORKOur evaluation demonstrates that SiNOxSense is a promis-ing approach to collect and share environmental data. How-ever, our findings also highlight a number of areas that re-quire further investigation.

For portable and lightweight electronic devices on the rise,highly efficient power generation approaches are a necessity.The battery life could be prolonged by deriving energy fromexternal sources that is known as energy harvesting and isan ongoing research area in ubicomp [9, 27]. After researchinto energy harvesting, it is clear that no satisfying tech-nologies currently exists for SiNOxSense. Further investi-gations into the built in Lilypad charging circuit should beconducted, with the goal of having custom code functional-ity run when the battery is fully charged - which is obscuredin standard Lilypad due to the way that the charging circuitworks in combination with the onboard charging LED.

We also suggest that future work should implement codeto discover the sensor unit - for example using Bluetooth’sclass, major and minor fields. Communication-device spe-cific code should be removed in favor of a general commu-nication framework, adding to the maintainability of the sys-tem.

Due to our functionality oriented focus (see Figure 1) wehave only designed one textile casing. A good focus on fu-ture work would be to provide multiple looks including moreneutral ones that many people could use without feeling in-timidated by the design. Both masculine and feminine ver-sion could be designed, as for age specific editions. Besidespure looks, another important aspect is to assure the sensorunit’s weather resistance. Rain and snow will limit the us-age, unless the user feels convinced that it will function un-der those conditions. Those are just some aspects that futuredesigners might take into consideration.

Communities interested in environmental sensing could ben-efit if the SiNOxSense software was open source, and ifwe provided users with a detailed, DIY-friendly guide. Webelieve that this would strengthen the potential for raisingawareness since people and/or companies might develop sen-sor units contributing to the backend. It could also add con-siderable possibilities for customization, which would ap-peal to creative people with overlapping interests in air quality-and/or pollution area. However our codebase needs to beimproved before making it open source.

Even though our developed Android app fulfills several func-tionalities mentioned in this paper, we did not manage to de-velop all the features that we learned would be useful forthe users. For example, privacy and direct feedback optionsshould be developed further. Specifically, a considerableamount of attention should be given to the exact way thatdirect feedback is achieved, to avoid hysteria, mentioned bysome questionnaire respondents. Privacy mechanisms con-

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cerning location and the relation to social medias should alsobe investigated. Also, we can imagine developing SiNOxSensefor other platforms than Android, like IOS, Windows Phoneto further facilitate the usage of the system.

In our implementation we decided to measure NOx con-centration in the air. Two important points can be madeof the sensor that we chose, the MQ-135 Air Quality sen-sor. First of all, it provides a possibility to sense more com-pounds (NH3, NOx, alcohol, benzene, smoke and CO2) -which can affect the results if these compounds are in theair. In future improvements more attention could be given tochoosing other sensors attuned to specific pollutants and/ora customizable mechanism in the mobile phone application,where the users could specify which pollutants are of theirinterests. Secondly, the sensor (like many other chemicalbased sensors) requires an active heating element being used.The sensor we are using have a heating element that shouldbe active for a period of 24 hours before precise measure-ments can be made. Hopefully sensor technology will im-prove to a point where this massive energy consumption nolonger is needed, while at the same time improving on sensorprecision.

We acknowledge that developing a website with an onlinemap is a crucial part of systems like ours, and have alreadybeen done in a very professional and satisfying manner insome of the projects mentioned in the related work section,like Sensaris [26] or Copenhagen Wheel [22]. It is importantthat the map functionality will be further developed so thatit is searchable by location and time. In this way users canobserve not only pollution levels in the specified zone butalso according to a specific interval of time.

Improving the health and quality of the users’ life is the mostimportant, although distant, overall goal to achieve. Fromthe questionnaires we deducted that people agree that oursystem might be able to achieve this overall goal. Still, thishypothetical result can only be evaluated after putting thesystem to everyday use by a large group of people. Properevaluation requires an appropriate amount of volunteers forat least a week in order to analyze if their behavior willchange and their quality of life will improve. This is out ofthe time frame for this project and we see it as an importantsubject for a future research. Taking into consideration that84% would recommend SiNOxSense to their friends, amongwhich 52% would do it gladly (results from the second ques-tionnaire), spreading knowledge about SiNOxSense and at-tracting volunteers probably would be feasible.

CONCLUSIONWe introduced an air quality sensor system featuring a novelsensor unit design that is wearable, textile-based and com-patible with existing mobile technologies. We performed aliterature search for wearable, portable and mobile air qual-ity sensor systems and extrapolated the overall functional re-quirements of all the related projects and let them form thebase of the system. All the stated functionality (see Fig-ure 2) was developed as a proof-of-concept. Due to timeconstraints we did not manage to integrate all of the system

components, however valuable research findings were stillachieved.

We worked towards making it easy to gather and share airpollution sensor data. Our system was designed, implementedand evaluated in three iterations following an iterative methodbased on the Iterative Design Model [5]. It comprises of thewearable, textile-based sensor unit built using the ArduinoLilypad board, an Android app and a Google App Engine(GAE) website. For future implementations of SiNOxSenseit might be advisable to use smaller hardware than Lilypad -which we only chose because of our intention of examiningthe wearable and textile-based design on system usage.

Based on results from the first questionnaire, we got valuableinsights that helped us to specify and prioritize the systemfeatures. Exposure to air pollution is not something a per-son directly can manipulate other than vacating their currentlocation. A key finding was therefore that GUI and directsensor feedback should be designed empathically and with-out provoking stress or hysteria in the user. Consideration tocustom privacy settings should also be respected.

Based on results from the questionnaire related to the videoprototype, we learned that people agree with our proposedway of achieving the functional requirements and systemgoals. Respondents rated that SiNOxSense was perceivedto be simple, straightforward and usable - although there isstill room for improvements in usability.

The final user evaluation showed, that textile-based designsmight encourage people to use mobile sensing technologies,mainly based on customization, adaptability, ruggedness andenvironmental friendly appeal. However respondents wouldlike more visual designs, a smaller sensor unit and/or thepossibility of the device being incorporated into everydayitems. It should also provide multiple functions, other thanrecording and providing feedback of air quality, for exampleby providing body monitoring functions.

ACKNOWLEDGEMENTSWe would like to thank Bent Hansen for assistance with thesensor unit electronics and Mailin Lindved for assistancewith the video prototype.

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