Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System

• URL: http://arxiv.org/abs/2510.19938v1
• Date: Wed, 22 Oct 2025 18:07:49 GMT
• Title: Designing a Secure and Resilient Distributed Smartphone Participant Data Collection System
• Authors: Foad Namjoo, Neng Wan, Devan Mallory, Yuyi Chang, Nithin Sugavanam, Long Yin Lee, Ning Xiong, Emre Ertin, Jeff M. Phillips,
• Abstract summary: MotionPI is a smartphone-based system designed to collect behavioral and health data through sensors and surveys.<n>It stores data both locally and on a secure cloud server, with encrypted transmission and storage.
• Score: 5.708820498491868
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• REMONI: An Autonomous System Integrating Wearables and Multimodal Large Language Models for Enhanced Remote Health Monitoring [4.85305127684971]
  This paper proposes REMONI, an autonomous REmote health MONItoring system that integrates multimodal large language models (MLLMs), the Internet of Things (IoT), and wearable devices.<n>The system automatically and continuously collects vital signs, accelerometer data from a special wearable (such as a smartwatch), and visual data in patient video clips collected from cameras.<n>A distinctive feature of our proposed system is the natural language processing component, developed with MLLMs capable of detecting and recognizing a patient's activity and emotion while responding to healthcare worker's inquiries.
  arXiv  Detail & Related papers  (2025-10-24T13:23:38Z)
• Colepp: uma ferramenta multiplataforma para coleta de dados de dispositivos vestiveis [0.0]
  Colepp is an open-source tool designed to collect and synchronize data from multiple wearable devices.<n>The system integrates a smartphone as a central hub, receiving data from a Polar H10 chest strap and a Wear OS smartwatch, and exporting synchronized datasets in CSV format.<n>A use case shows the effectiveness of the tool in producing consistent and synchronized signals.
  arXiv  Detail & Related papers  (2025-10-17T11:50:43Z)
• Collecting Human Motion Data in Large and Occlusion-Prone Environments using Ultra-Wideband Localization [1.3852370777848657]
  We investigate the possibility to apply the novel Ultra-Wideband (UWB) localization technology as a scalable alternative for human motion capture in crowded environments.<n>We include additional sensing modalities such as eye-tracking, onboard robot LiDAR and radar sensors, and record motion capture data as ground truth for evaluation and comparison.
  arXiv  Detail & Related papers  (2025-05-09T07:44:57Z)
• Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment [6.9604565273682955]
  This work introduces a wrist-worn smart band designed to address challenges through a novel combination of on-device TinyML-driven computing and cloud-enabled auto-deployment. Users can tailor activity classes to their unique movement styles with minimal calibration. System achieves a 37% increase in accuracy over generalized models in personalized settings.
  arXiv  Detail & Related papers  (2024-08-26T13:28:41Z)
• Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing [5.434366992553875]
  We propose FedMobile, an incremental semi-supervised federated learning algorithm. We evaluate FedMobile using a real-world mobile sensing dataset for influenza-like symptom recognition.
  arXiv  Detail & Related papers  (2023-12-19T23:39:33Z)
• Reconfigurable Data Glove for Reconstructing Physical and Virtual Grasps [100.72245315180433]
  We present a reconfigurable data glove design to capture different modes of human hand-object interactions. The glove operates in three modes for various downstream tasks with distinct features. We evaluate the system's three modes by (i) recording hand gestures and associated forces, (ii) improving manipulation fluency in VR, and (iii) producing realistic simulation effects of various tool uses.
  arXiv  Detail & Related papers  (2023-01-14T05:35:50Z)
• PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation [54.839600943189915]
  Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions. Despite a rich imputation literature, existing techniques are ineffective for the pulsative signals which comprise many mHealth applications. We address this gap with PulseImpute, the first large-scale pulsative signal imputation challenge which includes realistic mHealth missingness models, an extensive set of baselines, and clinically-relevant downstream tasks.
  arXiv  Detail & Related papers  (2022-12-14T21:39:15Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• A Computer Vision System to Help Prevent the Transmission of COVID-19 [79.62140902232628]
  The COVID-19 pandemic affects every area of daily life globally. Health organizations advise social distancing, wearing face mask, and avoiding touching face. We developed a deep learning-based computer vision system to help prevent the transmission of COVID-19.
  arXiv  Detail & Related papers  (2021-03-16T00:00:04Z)
• Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions [10.59409233835301]
  We develop for the first time a system consisting of computational models for synchronously skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data.
  arXiv  Detail & Related papers  (2021-02-08T21:56:15Z)
• BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing [40.44797233933835]
  coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Despite intensive research on digital contact tracing, existing solutions can hardly meet users' requirements on privacy and convenience. We propose BU-Trace, a permissionless mobile system for privacy-preserving intelligent contact tracing based on QR code and NFC technologies.
  arXiv  Detail & Related papers  (2021-01-24T06:11:09Z)
• Reconciling Security and Utility in Next-Generation Epidemic Risk Mitigation Systems [49.05741109401773]
  We present Silmarillion, a system that reconciles user's privacy with rich data collection for higher utility. In Silmarillion, user devices record Bluetooth encounters with beacons installed in strategic locations. We describe the design of Silmarillion and its communication protocols that ensure user privacy and data security.
  arXiv  Detail & Related papers  (2020-11-16T16:19:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.