Oslo Standstill Database

The Oslo Standstill Database (OSDB) collects 15 years of recordings of people standing still across various settings and conditions. Standing still is never still: the body keeps producing small involuntary motion — micromotion — driven by balance, breathing and the heartbeat, and that residual motion turns out to be measurable, individual, and responsive to music. In the spirit of Open Research and FAIR data management, this database gathers all the recordings in one place, in open formats, with the measurement problems documented rather than hidden.

The database as a whole is citable at 10.5281/zenodo.21653072. That record contains no recordings; it carries the database-level documentation and a table of the collections with their DOIs, sizes, modalities, and sampling rates, so that what is here can be read without downloading seven records. Cite the collection you used; also cite the umbrella if you used several.

The material spans laboratory studies, public championships, a year-long daily self-experiment, and a set of technical comparisons establishing what the instruments can and cannot resolve. It was collected at the fourMs Lab, RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Musicology, University of Oslo.

Datasets

Each dataset is self-contained: recordings at their native sampling rates, a data dictionary generated from the data itself, per-file provenance, and checksums. Data is released under CC BY 4.0 and code under GPL-3.0 unless a record states otherwise.

Technology comparisons

Micromotion sits close to the noise floor of the instruments that measure it, so knowing what a sensor can resolve is part of the measurement rather than a preliminary to it. These records directly compare measurement systems, usually by recording the same body with two or more systems simultaneously.

Publications

The standstill studies

• Jensenius, A. R., Bjerkestrand, K. A. V., & Johnson, V. (2014). How still is still? Exploring human standstill for artistic applications. International Journal of Arts and Technology, 7(2/3), 207–222. 10.1504/IJART.2014.060943
• Jensenius, A. R., Zelechowska, A., & Gonzalez-Sanchez, V. E. (2017). The musical influence on people's micromotion when standing still in groups. In Proceedings of the 14th Sound and Music Computing Conference (pp. 195–200). Aalto University. hdl.handle.net/10852/56047
• Gonzalez-Sanchez, V. E., Zelechowska, A., & Jensenius, A. R. (2018). Correspondences between music and involuntary human micromotion during standstill. Frontiers in Psychology, 9, 1382. 10.3389/fpsyg.2018.01382
• Gonzalez-Sanchez, V. E., Zelechowska, A., & Jensenius, A. R. (2019). Analysis of the movement-inducing effects of music through the fractality of head sway during standstill. Journal of Motor Behavior, 52(5), 555–570. 10.1080/00222895.2019.1689909
• Zelechowska, A., Gonzalez-Sanchez, V. E., Laeng, B., Vuoskoski, J. K., & Jensenius, A. R. (2020). Headphones or speakers? An exploratory study of their effects on spontaneous body movement to rhythmic music. Frontiers in Psychology, 11, 698. 10.3389/fpsyg.2020.00698
• Zelechowska, A., Gonzalez-Sanchez, V. E., Laeng, B., Vuoskoski, J. K., & Jensenius, A. R. (2020). Who moves to music? Empathic concern predicts spontaneous movement responses to rhythm and music. Music & Science, 3. 10.1177/2059204320974216
• Zelechowska, A., Gonzalez-Sanchez, V. E., & Jensenius, A. R. (2020). Standstill to the 'beat': Differences in involuntary movement responses to simple and complex rhythms. In Proceedings of Audio Mostly 2020. ACM. 10.1145/3411109.3411139
• Zelechowska, A. (2020). Irresistible Movement: The Role of Musical Sound, Individual Differences and Listening Context in Movement Responses to Music. PhD thesis, University of Oslo. hdl.handle.net/10852/82884
• Jensenius, A. R., & Vadstensvik Bjerkestrand, K. A. (2012). Exploring micromovements with motion capture and sonification. Lecture Notes of the Institute for Computer Sciences, 101, 100–107. 10.1007/978-3-642-33329-3_12
• Jensenius, A. R. (2023). Standing still together. In C. Wöllner & J. London (Eds.), Performing Time: Synchrony and Temporal Flow in Music and Dance. Oxford University Press. 10.1093/oso/9780192896254.001.0001
• Swarbrick, D. (2023). Being in Concert: Audience Emotion, Motion, and Social Connectedness. PhD thesis, University of Oslo. hdl.handle.net/10852/115183
• Al-Ghawanmeh, F. (2025). Ṭarab and AI: Maqām music generation through machine translation and motion capture. PhD thesis, University of Oslo and University of Lorraine. 10.70675/72d2800cza59ez4e80z9ba6z5146799f7850

Measurement and technology

• Riaz, M., Upham, F., Burnim, K., Bishop, L., & Jensenius, A. R. (2023). Comparing inertial motion sensors for capturing human micromotion. In Proceedings of the 20th Sound and Music Computing Conference. 10.5281/zenodo.8316052
• Riaz, M., et al. (2025). Where to put that microphone. In Proceedings of CMMR. 10.5281/zenodo.17178139
• Solberg, R. T., & Jensenius, A. R. (2016). Optical or inertial? Evaluation of two motion capture systems for studies of dancing to electronic dance music. In Proceedings of the 13th Sound and Music Computing Conference. 10.5281/zenodo.851315
• Jensenius, A. R., Nymoen, K., Skogstad, S. A., & Voldsund, A. (2012). A study of the noise-level in two infrared marker-based motion capture systems. In Proceedings of the 9th Sound and Music Computing Conference. 10.5281/zenodo.850038
• Bishop, L., & Jensenius, A. R. (2020). Reliability of two IR motion capture systems in a music performance setting. hdl.handle.net/10852/78536
• Guo, J., Riaz, M., & Jensenius, A. R. (2024). Comparing four 360-degree cameras for spatial video recording and analysis. In Proceedings of the 21st Sound and Music Computing Conference (pp. 309–315). 10.5281/zenodo.14339096

Related work using the same recordings

• Danielsen, A., Haugen, M. R., & Jensenius, A. R. (2015). Moving to the beat: Studying entrainment to micro-rhythmic changes in pulse by motion capture. Timing & Time Perception, 3(1–2), 133–154. 10.1163/22134468-00002043
• Bishop, L., Jensenius, A. R., & Laeng, B. (2021). Musical and bodily predictors of mental effort in string quartet music: An ecological pupillometry study of performers and listeners. Frontiers in Psychology, 12, 653021. 10.3389/fpsyg.2021.653021
• Solberg, R. T., & Jensenius, A. R. (2016). Pleasurable and intersubjectively embodied experiences of electronic dance music. Empirical Musicology Review, 11(3–4), 301–318. 10.18061/emr.v11i3-4
• Solberg, R. T., & Jensenius, A. R. (2017). Group behaviour and interpersonal synchronization to electronic dance music. Musicae Scientiae, 23(1), 111–134. 10.1177/1029864917712345
• Solberg, R. T. (2018). Experiences with electronic dance music: a multi-method study on associations between musical characteristics, embodiment and peak-pleasurable responses. PhD thesis, University of Agder. The work both dance records were recorded for. urn.nb.no/URN:NBN:no-nb_digibok_2025070206093

Software

Micromotion is the Python toolbox developed alongside the database. It carries the quantity-of-motion measure used throughout, the sampling rate the material requires, and a validation module whose every check exists because the failure it catches happened here and went unnoticed. Documentation at fourms.github.io/micromotion; install with pip install micromotion.

Mirrors and earlier releases

Parts of the championship material were released earlier through other repositories and remain available there: the 2012 and 2015 editions on PhysioNet and PhysioNet, and several editions through Sikt (formerly NSD). The versions in this database are re-curated from the original captures with corrected metadata, and should be preferred for new work.

Using the data

Five things are worth knowing before starting.

From phone sensor recordings, use the raw accelerometer and not the fused channel. Some of the phone logs carry two accelerations. gF* is the accelerometer, in g, with gravity included. ax/ay/az is a fusion of accelerometer, gyroscope and magnetometer, already in m/s², and it updates no faster than the gyroscope, about 15 Hz. Below 5 Hz, the fused channel therefore reports mostly its own noise floor, which differs between handsets. Everything in this database is computed from gF*. Several published-looking figures that came from the fused channel have been withdrawn, including a device factor between two handsets that turned out to point the other way.

Measure the sampling rate; do not read it. Documented rates in this material are wrong by up to 4.4%, and in one record by a factor of 37. Some devices also write a row whenever any sensor updates, so a file's row rate is not its sensor rate.

A dropped frame is not a position. Optical systems here write missing samples as three exact zeros, which is a point on the laboratory floor rather than a gap. Convert them to missing values before filtering or differentiating.

A marker's noise depends on where it stood. Optical accuracy falls off toward the edge of the calibrated volume: the 2012 noise-floor record places a marker outside it at about 10 mm/s compared with 1.3 at the centre, which is larger than the human signal being measured. Across the championship reference markers, distance from the middle of the room predicts each marker's measured speed, with rho = 0.39. Participants all stood well inside the volume and show no such effect, but a static reference marker at the edge is not the same instrument as one in the middle, and pooling them is not safe.

A band average is right for a level and wrong for a contrast. The corpus band, 0.2–5 Hz, spans more than four octaves. A difference between two conditions can be real in one octave and invisible in the average of all of them, which is how one effect in this material went unreported for a decade. If you are comparing conditions rather than describing a level, run the octave ladder and check that your analysis window is long enough to carry the lowest band you test, because below about 0.5 Hz a short window reports the window rather than the signal.

Say which statistic you used. The mean and the median of the same speed series answer different questions: the mean follows occasional large actions, the median the baseline sway. Windowed to each day's own standstill, they agree closely. The mean sits about 1.11 times the median and the two rank a year at a Spearman correlation of 0.99, but that agreement is a property of the windowing, not of the statistics, and it disappears when the window is chosen carelessly.

Each record's README documents its own known limitations. Where a measurement problem was found but could not be fixed, it is documented rather than omitted.

Credits

Many people contributed to this database:

Alexander Refsum Jensenius, Agata Zelechowska, Alicja Terelak, Anders Tveit, Anne Danielsen, Arve Voldsund, Ashane da Silva, Augusto Dias, Bálint Laczkó, Bilge Serdar, Bruno Laeng, Dana Swarbrick, Diana Kayser, Elias Sukken Andersen, Elisabeth Nilsson, Eyyüb Güven, Fadi Al-Ghawanmeh, Ferdinand Schwarz, Finn Upham, Frida Furmyr, Habibur Rahman, Henrik Haraldsen Sveen, Jinyue Guo, Joachim Perceval Laurent Erwin Poutaraud, Jonna Katariina Vuoskoski, Juliet Merchant, Karenina Natalia Juarez, Kari Anne Bjerkestrand, Katerina Teleli, Kayla Burnim, Kristian Nymoen, Laura Bishop, Lisa Dillan, Maham Riaz, Maja Roel, Marcus Widmer, Mari Lesteberg, Mari Romarheim Haugen, Nikos Baskozos, Nino Jakeli, Pedro Lucas Bravo, Ragnhild Torvanger Solberg, Rolf Inge Godøy, Ruby Topping, Shabari Rao, Ståle A. Skogstad, Thomas Eric Oldfield, Victor Gonzalez Sanches, Victoria Johnson.

Contact

Alexander Refsum Jensenius