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Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System

Foad Namjoo, Neng Wan, Devan Mallory, Yuyi Chang, Nithin Sugavanam, Long Yin Lee, Ning Xiong, Emre Ertin, Jeff M. Phillips

TL;DR

MotionPI tackles the problem of secure, scalable mobile health data collection under real-world constraints by integrating passive smartphone sensing with EMA surveys through a secure, offline-first architecture. The method combines encrypted local storage with cloud uploads to a MongoDB backend, authenticated via JWTs and reinforced by edge-based event triggers from BLE wristbands using ENMO-based activity detection. Key contributions include offline-triggered EMA, dual storage (local + cloud), BLE-based data collection, and a robust cloud backend with end-to-end data integrity checks. The work demonstrates practical deployment feasibility in NIH studies, achieving sustained battery life, high data throughput, and strong privacy protections for large-scale cyber-physical health research.

Abstract

Real-world health studies require continuous and secure data collection from mobile and wearable devices. We introduce MotionPI, a smartphone-based system designed to collect behavioral and health data through sensors and surveys with minimal interaction from participants. The system integrates passive data collection (such as GPS and wristband motion data) with Ecological Momentary Assessment (EMA) surveys, which can be triggered randomly or based on physical activity. MotionPI is designed to work under real-life constraints, including limited battery life, weak or intermittent cellular connection, and minimal user supervision. It stores data both locally and on a secure cloud server, with encrypted transmission and storage. It integrates through Bluetooth Low Energy (BLE) into wristband devices that store raw data and communicate motion summaries and trigger events. MotionPI demonstrates a practical solution for secure and scalable mobile data collection in cyber-physical health studies.

Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System

TL;DR

MotionPI tackles the problem of secure, scalable mobile health data collection under real-world constraints by integrating passive smartphone sensing with EMA surveys through a secure, offline-first architecture. The method combines encrypted local storage with cloud uploads to a MongoDB backend, authenticated via JWTs and reinforced by edge-based event triggers from BLE wristbands using ENMO-based activity detection. Key contributions include offline-triggered EMA, dual storage (local + cloud), BLE-based data collection, and a robust cloud backend with end-to-end data integrity checks. The work demonstrates practical deployment feasibility in NIH studies, achieving sustained battery life, high data throughput, and strong privacy protections for large-scale cyber-physical health research.

Abstract

Real-world health studies require continuous and secure data collection from mobile and wearable devices. We introduce MotionPI, a smartphone-based system designed to collect behavioral and health data through sensors and surveys with minimal interaction from participants. The system integrates passive data collection (such as GPS and wristband motion data) with Ecological Momentary Assessment (EMA) surveys, which can be triggered randomly or based on physical activity. MotionPI is designed to work under real-life constraints, including limited battery life, weak or intermittent cellular connection, and minimal user supervision. It stores data both locally and on a secure cloud server, with encrypted transmission and storage. It integrates through Bluetooth Low Energy (BLE) into wristband devices that store raw data and communicate motion summaries and trigger events. MotionPI demonstrates a practical solution for secure and scalable mobile data collection in cyber-physical health studies.
Paper Structure (10 sections, 3 figures, 1 table)

This paper contains 10 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Our solution.
  3. Related Work
  4. The Smartphone App
  5. Access Patterns for User Interface and Interaction
  6. Wristbands
  7. Physical Activity Triggers
  8. File-System Implementation
  9. Secure, Robust Interaction with Cloud
  10. Ongoing Deployment and Conclusion

Figures (3)

  • Figure 1: Diagram of the MotionPI Ecosystem. The App plays a central role in coordinating the data collection from peripherals, high-frequency sensing, and EMAs, and its secure and distributed storage.
  • Figure 2: Screenshots from the MotionPI App. The first (red) shows before the wristbands are connected, then after they are connected (yellow) but not activated, and in the middle, all are activated (green). The right two show when an EMA survey is triggered and available, and then within the EMA survey.
  • Figure 3: Package of items prepared for each study participant.