Studies

Managed, End-to-End Data Crowd Sourcing

Oftentimes, you want to collect smartphone sensor measurements from multiple phones and participants. Sensor Logger’s built-in Studies is an out-of-the-box solution for crowd-sourcing recordings in a streamlined way. You can control how data should be collected from the participants, ensuring data consistency. Participants can send their recordings back to you seamlessly via Sensor Logger’s secure cloud in a single tap, all whilst being privacy compliant. You can even set up custom questionnaires for participants to fill out to accompany their contributions.

Comprehensive Data Collection Management

Studies address critical challenges in collecting sensor data from phones and research participants:

  • Configuration Consistency: Ensure all devices are set up correctly, regardless of phone model and platform, including sensor types, sampling frequency, smart rules, and export formats.

  • Effortless Data Transfer: Simplify the data submission process, eliminating the need for cumbersome uploads and reminders.

  • Built for Scale: Manage data collection from a few participants to thousands, with infrastructure designed to handle large-scale studies efficiently.

  • Privacy Compliance: Data stays private throughout, and participants can stay anonymous.

  • Contextual Data: Beyond just sensor measurements — also surveys, consent forms, and more.

Sensor Logger’s Studies feature offers a seamless, end-to-end workflow. As an investigator, you define every aspect of your study, from data collection parameters to participant instructions and privacy statements. Participants join via a single link or QR code, ensuring correct configuration and consistent data collection. After recording, they can upload data securely with a single tap. You can even set up automated upload reminders, freeing you from the hassle of follow-ups.

    • Create Studies to collect data from other Sensor Logger users in a streamlined way.

    • When a Study is activated, the exact sensor configuration, as prescribed by the investigator, is used to ensure data collection consistency.

    • Study participants can seamlessly upload their recordings to a secure cloud when contributing to a study.

    • Join, pause and switch Studies anytime.

    • Easily filter and organise recordings made for different Studies.

    • Creating and managing Studies.

    • Create custom questionnaires accompanying each recording, filled out by the participants.

    • Sensor Logger helps you create Studies with clear data usage description, privacy statement and contact details to ensure privacy compliance, and that your participants understand how their data will be used.

    • Customise Study size, duration, sensor configuration, export formats and more.

    • Invite others to your Study with a short code or a QR code.

    • Keep track of participant count, recording number, and other statistics about your Study.

Beyond Sensor Data

With Sensor Logger, your studies go beyond simple sensor data collection. You can gather rich, detailed information from participants through fully customizable questionnaires. These questionnaires can include a wide range of input types, such as text responses, numerical fields, multiple-choice selections, and even participant signatures, giving you the flexibility to capture diverse data points. Questionnaires can be presented to participants at key stages of the study—when they first join, after each recording session, or at any other critical point in the process. This comprehensive approach ensures that you collect not only objective sensor data but also meaningful contextual information, enhancing the depth and value of your study.

Seamless Cross-Platform Compatibility

Sensor Logger’s studies are fully compatible with both iOS and Android devices, ensuring broad participation regardless of the platform. With built-in cross-platform functionality, Sensor Logger automatically resolves any data discrepancies between devices, allowing you to focus on analysis without worrying about format inconsistencies.

Customise For Your Research Needs

Sensor Logger's studies are fully customizable, giving you complete control over what data to collect, how to collect it, and how to communicate with participants about the use of their data. You also determine how participants should share their data with you. All of this can be managed directly from your phone, making it convenient for study investigators. For data management, you can easily access all uploaded recordings and surveys through a user-friendly web portal, accessible from both your phone and computer.

Out of the Box Privacy & Compliance

Privacy and compliance are at the core of Sensor Logger’s Studies. Every study includes an explicit privacy policy, ensuring transparency for participants. Participants must opt-in and explicitly upload their data, which is encrypted during transit and accessible only to the study investigator. Our infrastructure, powered by Cloudflare, ensures robust data protection.

Trusted by Researchers

Sensor Logger’s Studies are trusted by a wide range of industries, from research groups to startups. Users of Studies include organizations in healthcare, mobility, academia, sports, and more. Whether you’re conducting clinical trials, researching urban mobility, studying academic phenomena, or analyzing athletic performance, Sensor Logger provides the tools you need for reliable and efficient data collection.

Here is a curated list of research work citing Sensor Logger as part of their methodology. Visit https://github.com/tszheichoi/awesome-sensor-logger to see more.

  • Rodi Laanen, Maedeh Nasri, Richard van Dijk, Mitra Baratchi, Alexander Koutamanis, & Carolien Rieffe. (2023). Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology.

  • Shin, J. I. and Kim, J. O.: Possibility of Crowdsourcing-based Method for Surveying the Flatness of Pedestrian Spaces, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W10-2024, 163–168, https://doi.org/10.5194/isprs-archives-XLVIII-4-W10-2024-163-2024, 2024.

  • N. Loecher, S. King, J. Cabo, T. Neal and K. Kosyluk, "Assessing the Efficacy of a Self-Stigma Reduction Mental Health Program with Mobile Biometrics: Work-in-Progress," 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), Waikoloa Beach, HI, USA, 2023, pp. 1-6, doi: 10.1109/FG57933.2023.10042655.

  • Etienne, A. J., Field, W. E., Ehlers, S. G., Tormoehlen, R., & Haslett, N. J. (2024). Testing the feasibility of selected, commercially available wearable devices in detecting agricultural-related incidents. Journal of Agricultural Safety and Health, 30(4), 181–204. https://doi.org/10.13031/jash.15985

  • Degambur, L.-N. (2024). Replay attack prevention in decentralised contact tracing: A blockchain-based approach. OALib, 11(02), 1–17. https://doi.org/10.4236/oalib.1111179

  • Xiping Sun, Jing Chen, Cong Wu, Kun He, Haozhe Xu, Yebo Feng, Ruiying Du, & Xianhao Chen. (2024). MagLive: Near-Field Magnetic Sensing-Based Voice Liveness Detection on Smartphones.

  • Gadelho, J., & Guedes Soares, C. (2024). Experimental Motion Measurements of a Floating Dual Chamber OWC Using the Smartphone Sensors as a Low Budget Solution. In Innovations in Renewable Energies Offshore Proceedings of the 6th International Conference on Renewable Energies Offshore.

  • Zhang, J., Lau, M. C., & Zhu, Z. (2024). Hybrid CNN-GRU model for exercise classification using IMU Time-series data. Journal of Machine Intelligence and Data Science, 5. https://doi.org/10.11159/jmids.2024.007

  • Zhang, J., Lau, M. C., & Zhu, Z. (2024). Advanced Exercise Classification with a hybrid CNN-GRU model: Utilising IMU data from cell phones. International Conference of Control, Dynamic Systems, and Robotics. https://doi.org/10.11159/cdsr24.115

  • Lee, M. J., Lin, J., & Hsu, L. T. (2024). Exploring the Feasibility of Automated Data Standardization using Large Language Models for Seamless Positioning. arXiv preprint arXiv:2408.12080.

  • Vallivaara, I., Dong, Y., & Arslan, T. (2024). Saying goodbyes to rotating your phone: Magnetometer calibration during SLAM. arXiv preprint arXiv:2409.01242.

  • Ray, L. S. S., Geißler, D., Liu, M., Zhou, B., Suh, S., & Lukowicz, P. (2024). ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based HAR. arXiv preprint arXiv:2408.09527.

  • Yonetani, R., Baba, J., & Furukawa, Y. (2024, October). RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information. In Proceedings of the 2024 ACM International Symposium on Wearable Computers (pp. 125-132).

If you require custom Study requirements, or would like to white label the solution, feel free to reach out and we can discuss!

If you are running into issues with Studies or Stories, please check the current operational status of Sensor Logger Cloud: https://stats.uptimerobot.com/tA1F7mCPao