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Contact-free Sensing for collective Activity Recognition
Stephan Sigg
Georg-August-University Goettingen, Computer Networks Workshop on Collective Adaptation in Very Large Scale Ubicomp: Towards a Superorganism of Wearables
Contact-free Sensing for collective Activity Recognition Stephan - - PowerPoint PPT Presentation
Contact-free Sensing for collective Activity Recognition Stephan - - PowerPoint PPT Presentation
Contact-free Sensing for collective Activity Recognition Stephan Sigg Georg-August-University Goettingen, Computer Networks Workshop on Collective Adaptation in Very Large Scale Ubicomp: Towards a Superorganism of Wearables 07.09.2015 A
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A superorganism of Wearables
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A superorganism of Wearables
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A superorganism of Wearables
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A superorganism of Wearables
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A superorganism of Wearables
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Social Centric Networking
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Social Centric Networking
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Social Centric Networking
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Device-Free RF-based Activity Recognition
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Device-Free RF-based Activity Recognition
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Device-Free RF-based Activity Recognition
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Device-Free RF-based Activity Recognition
mobile phone within arms reach only 54% of the time
- A. K. Dey et al.: ’Getting closer: An empirical investigation of
the proximity of user to their smart phones’ Ubicomp 2011
- S. N. Patel et al.: ’Farther than you may think: An empirical
investigation of the proximity of users to their mobile phones’ Ubicomp 2006
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Using Fluctuation of electromagnetic waves at a receiver to recognise activities
Scholz, Sigg, Shihskova, von Zengen, Bagshik, Guenther, Beigl, Ji: SenseWaves: Radiowaves for context recognition, in Video Proceedings of Pervasive 2011
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RF-based device-free activity recognition
L y i n g empty S t a n d i n g Crawling W a l k i n g
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RF-based device-free activity recognition
L y i n g empty S t a n d i n g Crawling W a l k i n g
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Recent related work
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Whole-Home Gesture Recognition Using Wireless Signals, Q. Pu, S. Gupta, S. Gollakota, S. Patel, Mobicom’13 See Through Walls with Wi-Fi!, F Adib, D. Katabi, SIGCOMM’13
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Situation and gestures from passive RSSI-based DFAR
10cm 10cm
Towards Away Hold over Open/close Take up Swipe bottom Swipe top Swipe left Swipe right Wipe No gesture
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Situation and gestures from passive RSSI-based DFAR
10cm 10cm
Towards Away Hold over Open/close Take up Swipe bottom Swipe top Swipe left Swipe right Wipe No gesture
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Which sample rate can we expect?
Suburban flat channel
P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11 University (ETH) channel P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11 34.06 0.61 4.45 0.10 53.97 0.14 0.28 0.11 26.46 0.06 0.05
ground 3 r d fl . 14.88 5.02 0.19 0.04 192.29 0.04 0.03 0.07 2.88 0.09 0.02
Dormitory channel
P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11 10.28 10.03 12.13 9.92 9.30 1.77 0.09 0.19 6.92 0.47 0.36 10.45 9.18 9.01 21.91 23.70 22.31 21.34 21.58 0.55 0.62 14.51 Train station channel P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11 0.85 0.35 0.32 0.20 0.85 3.10 2.59 11.85 4.46 2.05 9.61 15.29 8.86 11.06 1.41 2.15 10.99 4.45 1.23 11.09 10.79 23.30 Café in center channel P a c k e t s / s e c 1 2 3 4 5 6 7 8 9 10 11
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Sensing and recognition
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Envisioned paradigm shift in mobile computing
Parasitic operation Communication virtually for free Miniaturisation Processing & storage capabil. limited (passive, parasitic, backscatter)
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Envisioned paradigm shift in mobile computing
Parasitic operation Communication virtually for free Miniaturisation Processing & storage capabil. limited (passive, parasitic, backscatter)
Trade processing load for communication load
Shift computation towards the wireless communication channel Computation below computational complexity possible?
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Calculation during transmission on the wireless channel
Utilising Poisson-distributed burst-sequences
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t K burst
superimposed received burst sequence transmit burst sequences time
Basic operations Addition, subtraction, division and multiplication at the time of wireless data transmission via Poisson-distributed burst-sequences
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Calculation during transmission on the wireless channel
Case study to compare the calculation accuracy
Utilise data from the Intel Berkeley laboratory network (here: temperature)1 Transmission of data by simple sensor nodes 2 3
1http://db.csail.mit.edu/labdata/labdata.html 2Sigg et al.: Utilising convolutions of random functions to realise function calculation via a physical channel, SPAWC’2013 3Sigg et al.: Calculation of functions on the RF-channel for IoT, IoT’2012
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Calculation during transmission on the wireless channel
Offline Online
Offline Online
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Mobile Crowdsourcing – Big Data – Smart City
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Probabilistic graphical models
Conditional random fields
Distinguishing between observed variables X and target variables Y , in the unnormalized measure P[X, Y ] =
- C
φC(XC) we can define a conditional random field as P[Y |X] = 1 Z(X)
- C
φC(XC) Z(X) =
- X
P[X, Y ]
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Application recommendation utilising Large-scale trends
832,236 devices Android + IOS 2012 - today http://carat.cs.helsinki.fi/
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Application recommendation utilising trends
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Application recommendation utilising trends
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Application recommendation utilising trends
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Application recommendation utilising trends
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