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MOBIUS: Smart Mobility Tracking with Smartphone Sensors

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MOBIUS: Smart Mobility Tracking with Smartphone Sensors

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Presentation for the 11th EAI International Conference Sensor System and Software (S-Cube) taking place on the 3rd December 2020.

This paper received the *Best Paper Award* at the S-Cube conference https://www.dimstudio.org/best-paper-award-at-eai-s-cube-conference/

Abstract
In this paper, we introduce MOBIUS, a smartphone-based system for remote tracking of citizens' movements. By collecting smartphone's sensor data such as accelerometer and gyroscope, along with self-report data, the MOBIUS system allows to classify the users' mode of transportation. With the MOBIUS app the users can also activate GPS tracking to visualise their journeys and travelling speed on a map. The MOBIUS app is an example of a tracing app which can provide more insights into how people move around in an urban area. In this paper, we introduce the motivation, the architectural design and development of the MOBIUS app. To further test its validity, we run a user study collecting data from multiple users. The collected data are used to train a deep convolutional neural network architecture which classifies the transportation modes using with a mean accuracy of 89%.

Video of the presentation
https://youtu.be/tBXtxcHFyMs

To appear in the Springer proceedings Science and Technologies for Smart Cities 6th EAI International Summit, SmartCity360, online December 2-4 December 2020, Proceedings

Presentation for the 11th EAI International Conference Sensor System and Software (S-Cube) taking place on the 3rd December 2020.

This paper received the *Best Paper Award* at the S-Cube conference https://www.dimstudio.org/best-paper-award-at-eai-s-cube-conference/

Abstract
In this paper, we introduce MOBIUS, a smartphone-based system for remote tracking of citizens' movements. By collecting smartphone's sensor data such as accelerometer and gyroscope, along with self-report data, the MOBIUS system allows to classify the users' mode of transportation. With the MOBIUS app the users can also activate GPS tracking to visualise their journeys and travelling speed on a map. The MOBIUS app is an example of a tracing app which can provide more insights into how people move around in an urban area. In this paper, we introduce the motivation, the architectural design and development of the MOBIUS app. To further test its validity, we run a user study collecting data from multiple users. The collected data are used to train a deep convolutional neural network architecture which classifies the transportation modes using with a mean accuracy of 89%.

Video of the presentation
https://youtu.be/tBXtxcHFyMs

To appear in the Springer proceedings Science and Technologies for Smart Cities 6th EAI International Summit, SmartCity360, online December 2-4 December 2020, Proceedings

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MOBIUS: Smart Mobility Tracking with Smartphone Sensors

  1. 1. MOBIUS: Smart Mobility Tracking with Smartphone Sensors 1Open University of The Netherlands 2Maastricht University 3rd December 2020 Daniele DI MITRI1 Khaleel ASYRAAF1 Kevin TREBING2 Stefano BROMURI1 daniele.dimitri@dipf.de - @dimstudio
  2. 2. The authors Daniele DI MITRI dimitri@dipf.de Khaleel ASYRAAF khaleel.asyraaf@ou.nl Stefano BROMURI stefano.bromuri@ou.nl Kevin TREBING kevin.trebing@ student.maastrichtuniversity.nl Center for Actionable Research of the Open University MOBIUS: Mobility Tracking w. Smartphone Sensors 2
  3. 3. Outline presentation • The authors • Background • Related Work • Functional requirements • System Architecture 1. Data Collection 2. Data Storing • Collected dataset 3. Data annotation 4. Data Analysis • Results • Discussion • Conclusions MOBIUS: Mobility Tracking w. Smartphone Sensors 3
  4. 4. Background • MOBIlity for Urban Sustainability (Mobius) is a project part of the SafeCity programme of the Open University of The Netherlands • The municipality of Heerlen monitors citizens journeys throughout the city using paper based questionnaires • MOBIUS idea: • replace questionnaire with a mobile application (android) • classify the transportation mode using Neural Networks (NN) • collected labels asking user to self-report using the mobile apps when the journey happens MOBIUS: Mobility Tracking w. Smartphone Sensors 4
  5. 5. Related Work • cell phone signal coverage and classified "stationary", "walking", and "driving” • 80%-85% accuracy (Anderson, 2006; Sohn, 2006) • proximity to bus-stops and rail-lines as well as GPS data • accuracy of 93% (Stenneth, 2011) • Only accelerometer of the cell phone (Hemminki, 2013; Liang, 2019) classify "stationary", "walk", "bus", "train", "metro", and "tram" • achieve 80% and 95% accuracy, respectively. • Using a Bi-LSTM (Zhao, 2019) with a MLP using acclerometer and gyroscope readings on the classes "stationary", "walk", "run", "bike", "bus", and "subway” • achieve 92% accuracy MOBIUS: Mobility Tracking w. Smartphone Sensors 5 Our classifier uses a similar CNN architecture as (Liang, 2019).
  6. 6. Functional requirements (FR1) Data collection • Long periods data collection of accelerometer, gyroscope, and GPS sensor data. • Data collection should be a feature in the background • Accelerometer and gyroscope data sampled at 60Hz (60 updates per seconds) • GPS coordinates sampled every 10 seconds • Data gathering should not drain the battery (FR2) Data storing • Sessions to be stored into compressed format (FR3) Data annotation • Self-report the mode of transportation • Activate a toggle for each mode of transportation • The user-annotations should be editable (FR4) Data analysis • suitable representation for machine learning (FR5) Privacy • User should be able to deactivate the GPS coordinate tracking • Classification should rely on Acceleroemter and Gyroscope MOBIUS: Mobility Tracking w. Smartphone Sensors 6
  7. 7. MOBIUS: Mobility Tracking w. Smartphone Sensors 7 System Architecture
  8. 8. (1) Data Collection: MOBIUS client Android mobile application with 2 flows of data collection: • Sensor data • Accelerometer gyroscope - 60Hz (60 updates per second) • Global Positioning System (GPS) - every 10s. Can be deactivated • User starts/stop the collection which runs in as deamon service in the background • Self-reported annotations: • “Walking”, “Running”, “Biking”, “Train/bus”, “Car”. • User toggles the annotations at start and ends of their journeys • At the end of the day, the user uploads the compressed data in the server MOBIUS: Mobility Tracking w. Smartphone Sensors 8 Source code: https://github.com/khaleelasyraaf/Mobius_Client
  9. 9. (2) Data Storing: Mobius Server MOBIUS: Mobility Tracking w. Smartphone Sensors 9 • Each session is stored in a zip folder • When the user hits the ‘Upload’ button, the zip folder is transfered from the Cliebt to the Mobius Server • The Mobius server is a RESTful web service implemented with Python Flask • Each session folder is composed by three CSV files contaning the data from: (1) acc & gyroscope, (2) GPS, (3) annotations • The files are later transformed into MLT-JSON format to be compatible with the annoation tool (next slide) MLT-JSON data format CSV data format Source code: https://github.com/HansBambel/Mobius_server
  10. 10. (3) Data Annotation: Visual Inspection Tool MOBIUS: Mobility Tracking w. Smartphone Sensors 10 Source code: https://github.com/dimstudio/visual-inspection-tool
  11. 11. Collected dataset & pre-processing MOBIUS: Mobility Tracking w. Smartphone Sensors 11 • The research team (4 users) collected ~47 hours recordings • The dataset was pre-processed and used as input for training the Neural Networks • We only consider Acc. and Gyr. (x,y,z) • A sliding window approach was used (size 512 = 8.7s) • It resulted into 168476 samples • We removed gravity from the Accelerometer • Applied smoothing at each sensor stream • We also tried using only the Acc. magnitude
  12. 12. (4) Data Analysis: SharpFlow Compared approaches: 1. Convolutional Neural Network (CNN) 2. Bi-directional Long Short Term Memory (Bi-LSTM) 3. Dummy classifiers 1. Most frequent class 2. Dependent on class distribution MOBIUS: Mobility Tracking w. Smartphone Sensors 12 Source code: https://github.com/dimstudio/SharpFlow Grafic Representation of the CNN
  13. 13. Results MOBIUS: Mobility Tracking w. Smartphone Sensors 13
  14. 14. Discussion • Gyroscope sensor seems to have a minor performance impact, thus excluding it will extend battery's life and storage space • Bi-LSTM did not achieve over 90% as in the literature, due to smaller time window (2.56s) with a sampling rate of 50Hz unlike our longer time (8.7s) window was sampled at 60Hz • The CNN outperforming the LSTM approach due to cyclic motions are easy to spot by the spatially invariant kernels of the convolutions MOBIUS: Mobility Tracking w. Smartphone Sensors 14
  15. 15. Conclusions • We introduced Mobius, a system for Smart Mobility Tracking with Smartphone Sensors • Mobius can automatically classify transportation mode using only Accelerometer and Gyroscope data • We tested Mobius using 47 hours of recorded data • We achieved an overall 89% using a CNN • Scalable client-server architecture which can accommodate data from multiple clients • User in the loop can self-report mode of transport • We released all our code Open Source on GitHub • We are aware that tracking apps pose privacy concerns due to possible surveillance MOBIUS: Mobility Tracking w. Smartphone Sensors 15
  16. 16. Future applications • We plan to develop an Android Wear (smartwatch) version of the Mobius client app • Add more modalities such as the heart-rate • The approach proposed by Mobius can be used for Human Activity Recognition: • Daily Life Activities • Sports performance monitoring • Physical rehabilitation Tasks • The trained models can be embedded in the android app to avoid uploading in the server MOBIUS: Mobility Tracking w. Smartphone Sensors 16
  17. 17. Thank you! Any question? Code: github.com/dimstudio Connect: dimitri@dipf.de @dimstudio

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