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Gary M. Weiss and Jeffrey Lockhart Fordham University, New York, NY 1UbiMI UBICOMP Sept. 8 2012.

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Presentation on theme: "Gary M. Weiss and Jeffrey Lockhart Fordham University, New York, NY 1UbiMI UBICOMP Sept. 8 2012."— Presentation transcript:

1 Gary M. Weiss and Jeffrey Lockhart Fordham University, New York, NY 1UbiMI UBICOMP Sept

2  Mobile sensors becoming ubiquitous  Especially via smartphones  Various architectures are possible ranging from “smart client” to “dumb client”  Each architecture has pros and cons  Worthwhile to enumerate and compare alternative architectures 2UbiMI UBICOMP Sept

3 1. Sensor Collection 2. Data Processing and Transformation 3. Decision Analysis/Model Application 4. Data and Knowledge Reporting Learning/model generation Only step 1 is required 3UbiMI UBICOMP Sept

4  Main focus of WISDM lab  Monitors smartphone accelerometer and uses the data to perform activity recognition  Activities: walk, jog, stairs, sit, stand, lie down  Results available via the Web 4UbiMI UBICOMP Sept

5  Sensor Collection:  Actitracker client collects raw accelerometer data for 3 axes 20 times per second and transmits to server  Data Processing and Transformation  Every 10 sec. server aggregates raw samples into a single example described by several dozen features  Decision Analysis/Model Application  Server applies predictive model to examples; activity classified and saved to database  Data and Knowledge Reporting  User queries server DB any time via web interface 5UbiMI UBICOMP Sept

6 Client Configurations Responsibility CC-1 Dumb CC-2CC-3CC-4 Smart 1Sensor Collection 2Data Transformation 3Model Application 4Reporting Model Generation?? 6UbiMI UBICOMP Sept

7  Mobile devices have CPU power to build models  Only makes sense to build a model on the client device if will apply it on the client  Thus model construction on device only for CC-3 or CC-4  In CC-1 and CC-2 either model hardcoded into client or downloaded from server  Data mining not always required  Can be done dynamically (on client or server) or statically  Our research shows dynamically generated personal models outperform general (impersonal) models 1 7UbiMI UBICOMP Sept Gary M. Weiss and Jeffrey W. Lockhart. The Impact of Personalization on Smartphone-Based Activity Recognition, Papers from the AAAI-12 Workshop on Activity Context Representation: Techniques and Languages, AAAI Technical Report WS-12-05, Toronto, Canada,

8  Resource usage  battery, CPU, memory, transmission bandwidth  Scalability  Support for many mobile devices  Access to data  Researchers and others may want raw data  Transformed data loses information ▪ With raw data can alter features for data mining and regenerate results UbiMI UBICOMP Sept

9  Privacy/Security  Users will want to keep data secure and/or private  User Interface  Users want aesthetics (screen size) & accessibility  Crowdsourcing  Some applications will require a central server in order to aggregate data from multiple users/devices ▪ Navigation software that tracks traffic UbiMI UBICOMP Sept

10  Resource Usage  Unclear. Resource usage minimized except heaviest use of transmission bandwidth (power drain)  Scalability  Poor since maximizes server work  Actitracker’s server can handle 942 simult. users  Access to Data  Best since all raw data can be preserved on server ▪ But Actitracker requires 791 MB/month per user. UbiMI UBICOMP Sept

11  Privacy/Security:  Poor: The more data sent the greater the risk  User Interface:  Good: data and results on server and can be viewed over Internet  Crowdsourcing  Best: All data available on server UbiMI UBICOMP Sept

12  Similar to CC-1 except:  Less data to transmit so bandwidth/energy savings ▪ For Actitracker 95% reduction in data ▪ But more processing which takes up CPU and power  More scalable (less server work)  Less access to data (raw data not available)  Slight improvement in privacy/security (no raw data)  Minimal impact on user interface (results still on server)  Crowdsourcing only on aggregated data UbiMI UBICOMP Sept

13  Resource usage:  more processing on the client (more CPU and power); but only need to transmit results  Much more scalable: server only collects results  Access to data: only results available  Much improved security/privacy  results may not be nearly as sensitive  Can still view results via web-based interface  Can only crowdsource on results UbiMI UBICOMP Sept

14  About same as CC-3  not sending results saves little power  Perfectly scalable: no server  No access to data  Good security/privacy: nothing leaves device  Can only view results on the device  Not accessible from other places and small screens  Cannot even crowdsource results UbiMI UBICOMP Sept

15  Resource usage: unclear  Scalability: smart client best  Access to data: dumb client best  Security/Privacy: smart client best  User Interface:smart client worst  Centralized Data:dumb client best  One approach: support multiple architectures  approach taken by our research group UbiMI UBICOMP Sept

16  Go to  Actitracker should be ready for beta in 1 month   Papers available from:   My contact info:  UbiMI UBICOMP Sept

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