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Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research:

Michael Single1, Lena C Bruhin1, Narayan Schütz1,2

  • 1Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.

JMIR Formative Research
|February 17, 2023
PubMed
Summary

This study introduces a novel, cost-effective sensor recording software for biomedical research, enabling reliable, long-term data collection on consumer hardware. The system reduces technical barriers, allowing researchers to focus on data analysis and digital health advancements.

Keywords:
biomedical researchdigital measureson-demand deploymentsensor platformsensor recording software

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Area of Science:

  • Biomedical Engineering
  • Digital Health
  • Wearable Technology

Background:

  • Digital sensing devices offer objective insights into motor and non-motor symptoms in biomedical research.
  • High costs and technical expertise are significant barriers to adopting sensor-enhanced solutions.
  • Real-time sensor data can lower healthcare costs and improve access to health assessments.

Purpose of the Study:

  • To develop a novel sensor recording software system.
  • To support integration of heterogeneous sensor technologies.
  • To enable reliable, longitudinal sensor measurements on consumer-grade hardware.

Main Methods:

  • Developed a server-client architecture using microservices and Docker containers.
  • Utilized open-source technologies (Node.js, MongoDB) for cost-effectiveness.
  • Tested the system in 3 studies (114 participants, up to 225 days) evaluating reliability, error rates, throughput, latency, and usability.

Main Results:

  • The software operates reliably on consumer hardware for extended periods (>420 days).
  • Achieved high throughput (2000 requests/sec) with low latency and error rates (<0.002%).
  • Usability tests showed high user satisfaction (SUS: 89.5, PSSUQ: 1.62).

Conclusions:

  • The software reduces barriers in sensor-enhanced biomedical research.
  • It facilitates testing sensor devices and developing algorithms for digital measures (e.g., gait, actigraphy).
  • Enables researchers to prioritize research questions over technology development.