Students : Kartik Sankaran † , Guo Xiang Fa † , Minhui Zhu † Supervisors : Prof A. L. Ananda † , Prof Chan Mun Choon † , Prof Li-Shiuan Peh # † School of Computing, National University of Singapore # Electrical Engineering and Computer Science, Massachusetts Institute of Technology
▪ Introduction ▪ Related work ▪ Motivation ▪ Methodology ▪ Evaluation 2
Smartphones are becoming “intelligent”, silently understanding what the user is doing and helping in tasks Goog ogle le Now ow Cover er App pp http://www.google.com/landing/now/#cards 3 https://www.coverscreen.com/
Key ingredient of this intelligence is “Context - awareness” derived from phone’s sensors Sensor or Con ontext t de detection ion da data Algor orit ithm hm Runs al all t the he time, must be low-powe ower Intelli llige gent nt Context-Awar ware User’s beh ehavio ior Apps Con ontext http://mobihealthnews.com/26977/google-adds-low-power-step-counting-to-android-4-4/ http://en.wikipedia.org/wiki/Apple_M7 4
Transportation context detection: Phone automatically understands user’s daily commute Activit ity Di Diaries Urban an Planning ning Redu ducing ing pe peak hou our Traffic fic managem ement nt waiting tim ing time https://play.google.com/store/apps/details?id=com.protogeo.moves 5
Accelerometer is the predominant sensor used for transportation context detection Current nt dr drawn in mA mA Sensor Nexus 5 Nexus 4 Proximity 12.675 12.675 Measur ures s acceler leratio ation n Rotation Vector 8.65 4.1 (m/sec 2 ) ) Magnetometer 5 5 Linear Accelerometer 3.65 4.1 in all 3 a axial direction ions Gyroscope 3.2 3.6 Significant Motion 0.45 0.5 Accelerometer 0.45 0.5 Light 0.175 0.175 Relati tively ly low ow-po power er sensor or Barometer 0.004 0.003 (as reported by Android’s Sensor Manager) 6
Although low-power, accelerometer-based approaches have many problems Wai ait an and v d vehi hicl cle detect ction on problems Accl Data Featur ure e Extractio action (c (covered lat ater) Supe perv rvise ised d Con ontext t Featur ures machine ine classifi sification ation High h sam ampling learning rning Statistical Mean, variance, min, max, … rat ate Frequency FFT (1 to 5 Hz), … Position n … … (10 Hz or (1 or more) dependenc nce Expensive computat ation on Extensi sive trai aining 7
We present an altern ernati tive e app pproa oach to transportation context detection using on only barom ometer er Me Measur ures s Altit itude ude air pr pressure e (metres) (mill lliba ibar) ) Present t in Ne Nexus 4/5 /5/6 /6, , Gal alax axy S3/4 /4/5 /5, Gal alax axy No Note 1/2 /2/3 /3/4 /4, man any more! http://www.calctool.org/CALC/phys/default/pres_at_alt.png http://www.hko.gov.hk/education/edu01met/wxobs/pressure/pres-fig2e.jpg 8
Can an sense even 1 metre ch chan ange in he height ht! Current uses of smartphone’s barometer Floor or-change hange Fitne ness ss app pps Weather her pr predi diction tion Faster er GPS fix de detection tion http://cdn.appspirate.com/wp-content/uploads/2013/09/Fix-Android-GPS.jpg http://img.howcast.com/thumbnails/506920/36_running_uphill_xxxlarge.jpg https://play.google.com/store/apps/details?id=com.opensignal.weathersignal 9 http://weather.phillipmartin.info/science_air_pressure.gif
Bar arometer al algo implement nted on Android This is the first wor ork using on only barom ometer er 1000+ users in Singapore and for IDLE, WALKING, and VEHICLE detection Boston Compar ared with h widely-deploye oyed Position ion and or d orientatio tation n inde depe pende ndent nt Google le (Accl cl-Phon hone) ) + FMS (G (GPS-Server) Not affected d by hand d movem ements nts Low ow sampl pling ng rate e of of 1 Hz 1 Hz Simple ple pr proc ocessing ing Available even on old Similar ar overal all ac accurac acy to both Android versions 26 % lower energy tha T er errain ain de depe pende dent nt 26 han Google https://fmsurvey.sg/pages/home_mobile 10 http://developer.android.com/training/location/activity-recognition.html
Popular sensors used for context detection: Accelerometer, GPS, Cellular, WiFi [ Incel et al. B IO N ANO S CIENCE ’13 ] GPS Velo loci city y change e rate, e, Wher ere am e am I on on earth? Stop op rate, e, [ Zheng et al. U BI C OMP ’08] He Headi ding ng change e rate + Accele lerom ometer er [ Ryder et al. CSE ’09] [ Reddy et al. TOSN ’10] [ Future Mobility Survey TRB ’13] Better er Accuracy 11 http://1.bp.blogspot.com/-96e_AJrC3IU/TbZb-gPt55I/AAAAAAAAKIU/2CAKZ0K1NZc/s1600/sign.jpg
Popular sensors used for context detection: Accelerometer, GPS, Cellular, WiFi [ Incel et al. B IO N ANO S CIENCE ’13 ] Cellul lular/ r/WiFi iFi [ BeaconPrint U BI C OMP ’05] Beacon on IDs De Detect t movem ement nt [ Anderson et al. CSTR ’06] Signa gnal l Strengt ngth [ Sohn et al. U BI C OMP ’06] Beacon on [ ParkSense M OBI C OM ’13] Recep eption ion Ratio 12 http://article.sapub.org/image/10.5923.j.ijnc.20120204.02_005.gif
28 8 out of f 36 pap apers listed use A Accelerometer Popular sensors used for context detection: Accelerometer, GPS, Cellular, WiFi [ Incel et al. B IO N ANO S CIENCE ’13 ] Accele lerome ometer er [ Wang et al. APWCS ’10] Training ning and d [ Future Mobility Survey TRB ’13] classifi sification o ation offline line Featur ures extract acted d [ Reddy et al. TOSN ’10] and d fed t d to o Training ning of offline ne [ Siirtola et al. IJIMAI ’12] machine ine learning ning Classifi sification tion on online ne [ Google IO ’13] Accelerometer can an do [ Hemminki et al. S EN S YS ’13] fine-gr fi grai aine ned d vehi hicl cle-mode de Training ning and d [ Gomes et al. MDM ’12] detect ction, on, use ba barometer classifi sification on ation online line as as lo low-po powe wer r trigger Consumes 85 mW (excludes base power) 13
Barometer has mainly been used for floor-change detection [ Zhang et al. PLANS ’12] Aidi ding ng GPS fix Barom ometer er [ Lester et al. P ERVASIVE ’06] Stair irs/Ele /Elevat ator or [ Vanini et al. P ER C OM ’13] de detection tion [ Kartik et al. H OT M OBILE ’14] Rem emoving ing [ Tanigawa et al. WPNC ’08] accele lerom ometer er dr drift 14
Don’t put phone in something air-tight! Why is barometer advantageous over other sensors? Each user handles phone differently Sensor Limitations Barometer advantage Lack of indoor/underground coverage Usable everywhere GPS High power usage Ultra-low power Cellular/WiFi Requires dense access points/cellular towers No external infrastructure required Position dependence Inherently position independent Training required Simple calibration based on terrain Accelerometer Classification complexity Simple processing Affected by hand movements [WAIT detection] Unaffected by hand movements Does not work well in “smooth” vehicles Does not depend on vehicle vibrations Causes false positives when Depends on where phone placed More details in evaluation used to trigger GPS (hand/bag/pocket) 15
Low sam ampling rat ate + simple processing g : L Low power Power advantage over other sensors Pow ower er con onsum umed d at di differ erent nt Current nt dr drawn in mA mA sampl pling ing rates (inclu clude des s base e po power er) Sensor Nexus 5 Nexus 4 Proximity 12.675 12.675 Power Increase over (mW) base power Rotation Vector 8.65 4.1 CPU Asleep 25 x Magnetometer 5 5 CPU Awake (base) 108 x Linear Accelerometer 3.65 4.1 Accl (2 Hz) 164 51% Gyroscope 3.2 3.6 Accl (10 Hz) 180 67% Significant Motion 0.45 0.5 Accl (20 Hz) 230 112% Accelerometer 0.45 0.5 Baro (1 Hz) 110 2% Light 0.175 0.175 Barometer 0.004 0.003 100 00 times low ower er than acceler erom ometer er (as reported by Android’s Sensor Manager) 16
What question is our paper trying to answer? Accele lerom ometer er da data is bo both a b boon on and a b d a bane: e: More Mo e infor orma mation, tion, but but requ quires s com ompl plex x pr proc ocessing ing Does 1D hei Do D height t da data pr provide de sufficie cient nt infor ormatio mation n for or transpor porta tation tion con ontext t de detection ion? In other er wor ords ds, is l less da data enou ough? 17
Understanding the barometer, its strengths and potential sources of error Unaffect Un cted Cross-section ction of of 1. Vibrat ation MEMS ME MS barom ometer er Compe mpensat sated 2. T emperat ature Rem emoved at end of 3. Instal allat ation on bias as produ duction ction line 4. 4. Aging dr drift Ver ery y long ter erm m (mo months/ye hs/year ars) s) 5. Sunlight ht an and d Wind Ch Chip p protect cted d by phone e casing 6. Weat athe her drift The e only thing that can affect ct (3 (3 mtr in 10 mi min (image from Stephen Ming-Chang Hou’s our r context t detect ction on algorit ithm hm PhD thesis, MIT 2012) in storms) 18 http://static.sparkfun.com/images/products/09694-04.jpg
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