Indoor Place Prediction on Smart Phones

Pritam Sen, Xiaopeng Jiang, Qiong Wu, Manoop Talasila, Wen Ling Hsu, Cristian Borcea

Research output: Chapter in Book/Report/Conference proceedingConference contribution


High-accuracy and low-latency indoor place prediction for mobile users is crucial to enable applications for assisted living, emergency services, smart homes, and augmented reality. Previous studies on indoor place prediction use complex infrastructure with multiple visual/wireless anchors or multiple wireless access points. These localization techniques are difficult to deploy, may negatively impact user privacy through location tracking, and their data collection is not suitable for personalized place prediction. To solve these challenges, this paper proposes GoPlaces, a novel app that fuses inertial sensor data with WiFi-RTT estimated distances to predict the future indoor places visited by a user. GoPlaces does not require any infrastructure, except for one cheap off-the-shelf WiFi access point that supports ranging with RTT. In addition, it enables personalized place naming and prediction through its on-the-phone data collection and protects users' location privacy because user's data never leaves the phone. GoPlaces uses an attention-based bidirectional long short-term memory model to detect user's current trajectory, which is then used together with historical information stored in a prediction tree to infer user's future places. We implemented GoPlaces in Android and evaluated it in several indoor spaces. The experimental results demonstrate prediction accuracy as high as 92%, low latency, and low resource consumption on the phones.

Original languageEnglish (US)
Title of host publicationSenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages2
ISBN (Electronic)9781450398862
StatePublished - Nov 6 2022
Event20th ACM Conference on Embedded Networked Sensor Systems, SenSys 2022 - Boston, United States
Duration: Nov 6 2022Nov 9 2022

Publication series

NameSenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems


Conference20th ACM Conference on Embedded Networked Sensor Systems, SenSys 2022
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


  • deep learning
  • human mobility
  • indoor place prediction
  • sensor fusion
  • smart phones
  • time series analysis
  • wifi-RTT


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