SisFall: A Fall and Movement Dataset
Open accessPublished version of a preprint
Abstract
Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Unlike datasets such as SisFall that rely on invasive wearable sensors (IMUs), which may alter motor behaviour, the study employed a non-intrusive computer vision approach.
“unlike datasets such as SisFall [10] that rely on invasive wearable sensors (IMUs), which may alter motor behaviour, we employed a non-intrusive computer vision approach.”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [10]
The Experimental Set-Up section (no page provided) confirms that SisFall used a waist-mounted device containing accelerometers and a gyroscope. The paper does not say that this device was invasive or that it might alter motor behaviour. Its Introduction instead describes cameras as intrusive; the citing authors’ choice of computer vision is their own, not a finding attributed to SisFall. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe Cluster 1 profile aligns with uncontrolled descent patterns in public datasets, where untrained subjects exhibit sharp acceleration spikes and erratic limb flailing.
“This profile aligns with the “uncontrolled descent” patterns frequently observed in the unfiltered raw data of public datasets, where untrained subjects exhibit sharp acceleration spikes and erratic limb flailing [10].”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [10]
The Results (§4.2; page not supplied) report that young adults simulated falls faster and with greater acceleration than elderly participants, and the Introduction says SisFall makes fall data publicly available. The paper does not describe these patterns as frequent in unfiltered raw data, identify the subjects as untrained, or report erratic limb flailing. (LLM, conf=0.86)
Experimental — accuracy not yet measuredTraditional binary classifications (Fall vs. ADL) found in public datasets like SisFall or MobiAct focus solely on detection.
“Unlike traditional binary classifications (Fall vs. ADL) found in public datasets like SisFall or MobiAct [10], which focus solely on detection, this approach reveals the qualitative spectrum of the motor response.”
From Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality (2026) · cites this paper as [10]
The Introduction and §3.4.3 describe classifying activities as falls or ADLs to evaluate fall detection. But §3.4.4 and the Discussion also describe activity-by-activity analysis, so “solely on detection” overstates what the SisFall paper does. The paper discusses MobiFall, not MobiAct, and does not establish the claim about MobiAct. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,003 · see citing works
Fear of falling and restriction of mobility in elderly fallers
Vellas, B. J., Wayne, S. J., Romero, L. J., Baumgartner, R., & Garry, P. J. (1997) · Age and Ageing · doi:10.1093/ageing/26.3.189 · Cited by 889 · see citing works
Delbaere, K. (2004) · Age and Ageing · doi:10.1093/ageing/afh106 · Cited by 687 · see citing works
Challenges, issues and trends in fall detection systems
Igual, R., Medrano, C., & Plaza, I. (2013) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-12-66 · Cited by 659 · see citing works
Fall detection - Principles and Methods
Noury, N., Fleury, A., Rumeau, P., et al. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4352627 · Cited by 616 · see citing works
Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls
Bagalà, F., Becker, C., Cappello, A., Chiari, L., Aminian, K., Hausdorff, J. M., Zijlstra, W., & Klenk, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037062 · Cited by 464 · see citing works
Lord, S. R., Ward, J. A., Williams, P., & Anstey, K. J. (1993) · Australian Journal of Public Health · doi:10.1111/j.1753-6405.1993.tb00143.x · Cited by 456 · see citing works
Optimal Placement of Accelerometers for the Detection of Everyday Activities
Cleland, I., Kikhia, B., Nugent, C., Boytsov, A., Hallberg, J., Synnes, K., McClean, S. I., & Finlay, D. (2013) · Sensors · doi:10.3390/s130709183 · Cited by 403 · see citing works
A proposal for the classification and evaluation of fall detectors
Noury, N., Rumeau, P., Bourke, A. K., OLaighin, G. M., & Lundy, J. E. (2008) · IRBM · doi:10.1016/j.irbm.2008.08.002 · Cited by 286 · see citing works
Sensors-Based Wearable Systems for Monitoring of Human Movement and Falls
Shany, T., Redmond, S. J., Narayanan, M. R., & Lovell, N. H. (2011) · IEEE Sensors Journal · doi:10.1109/jsen.2011.2146246 · Cited by 286 · see citing works
Automatic Fall Monitoring: A Review
Pannurat, N., Thiemjarus, S., & Nantajeewarawat, E. (2014) · Sensors · doi:10.3390/s140712900 · Cited by 238 · see citing works
Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues
Habib, M. A., Mohktar, M. S., Kamaruzzaman, S. B., Lim, K. S., Pin, T., & Ibrahim, F. (2014) · Sensors · doi:10.3390/s140407181 · Cited by 219 · see citing works
Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones
Medrano, C., Igual, R., Plaza, I., & Castro, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094811 · Cited by 176 · see citing works
Comparison of acceleration signals of simulated and real-world backward falls
Klenk, J., Becker, C., Lieken, F., Nicolai, S., Maetzler, W., Alt, W., Zijlstra, W., Hausdorff, J. M., van Lummel, R. C., Chiari, L., et al. (2010) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2010.11.003 · Cited by 157 · see citing works
Age and sex influences on fall characteristics.
O’Neill, T. W., Varlow, J., Silman, A. J., et al. (1994) · Annals of the Rheumatic Diseases · doi:10.1136/ard.53.11.773 · Cited by 134 · see citing works
A comparison of public datasets for acceleration-based fall detection
Igual, R., Medrano, C., & Plaza, I. (2015) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2015.06.009 · Cited by 98 · see citing works
A Combined Smartphone and Smartwatch Fall Detection System
Vilarinho, T., Farshchian, B. A., Bajer, D. G., Dahl, O. H., Egge, I., Hegdal, S. S., Lones, A., Slettevold, J. N., & Weggersen, S. M. (2015) · 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing · doi:10.1109/cit/iucc/dasc/picom.2015.216 · Cited by 98 · see citing works
Bayesian recognition of motion related activities with inertial sensors
Frank, K., Nadales, M. J. V., Robertson, P., & Pfeifer, T. A. (2010) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/1864431.1864480 · Cited by 88 · see citing works
Vavoulas, G., Pediaditis, M., Chatzaki, C., Spanakis, E. G., & Tsiknakis, M. (2014) · International Journal of Monitoring and Surveillance Technologies Research · doi:10.4018/ijmstr.2014010103 · Cited by 82 · see citing works
Analysis of Android Device-Based Solutions for Fall Detection
Casilari, E., Luque, R., & Morón, M. J. (2015) · Sensors · doi:10.3390/s150817827 · Cited by 81 · see citing works
Do community alarm users want telecare?
Brownsell, S., Bradley, D. A., Bragg, R., Catlin, P., & Carlier, J. P. (2000) · Journal of Telemedicine and Telecare · doi:10.1258/1357633001935356 · Cited by 64 · see citing works
Yuan, J., Tan, K. K., Lee, T. H., & Koh, G. C. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2357035 · Cited by 63 · see citing works
Evaluation of the android-based fall detection system with physiological data monitoring
Koshmak, G. A., Lindén, M., & Loutfi, A. (2013) · doi:10.1109/embc.2013.6609713 · Cited by 49 · see citing works
International Journal of Monitoring and Surveillance Technologies Research
Bourbakis, N., & Nikita, K. S. (2022) · International Journal of Monitoring and Surveillance Technologies Research · doi:10.4018/ijmstr · Cited by 9 · see citing works
Analyzing Multiple Accelerometer Configurations to Detect Falls and Motion
López, J. D., Ocampo, C., Sucerquia, A., et al. (2017) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-4086-3_43 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [2]Lord S. Sherrington C. Menz H. Falls in Older People: Risk Factors and Strategies for Prevention 1st ed. Cambridge University Press Cambridge, UK 2001
- [23]Xue Y. Jin L. A Naturalistic 3D Acceleration-based Activity Dataset and Benchmark Evaluations Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC) Istanbul, Turkey 10–13 October 2010 4081 4085
Cited by
Sorted by citations
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · Applied Sciences · doi:10.3390/app7101101 · Cited by 596 · see citing works
Atitallah, S. B., Driss, M., Boulila, W., & Ghézala, H. B. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100303 · Cited by 375 · see citing works
Machine Learning for Healthcare Wearable Devices: The Big Picture
Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321 · see citing works
Online Fall Detection Using Recurrent Neural Networks on Smart Wearable Devices
Musci, M., De Martini, D., Blago, N., Facchinetti, T., & Piastra, M. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2020.3027454 · Cited by 128 · see citing works
Multi-sensor-based Action Monitoring and Recognition via Hybrid Descriptors and Logistic Regression
Hafeez, S., Alotaibi, S. S., Alazeb, A., Mudawi, N. A., & Kim, W. (2023) · IEEE Access · doi:10.1109/access.2023.3275733 · Cited by 24 · see citing works
A spatio-temporal graph neural network for fall prediction with inertial sensors
Wang, S., Li, X., Liao, G., Liu, J., Liao, C., Liu, M., Liao, J., & Liu, L. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111709 · Cited by 18 · see citing works
Ray, S. J., Cherian, J., Liberty, A. M., Hammond, T. A., & Shireman, P. K. (2024) · doi:10.2196/preprints.67373 · Cited by 18 · see citing works
Zobi, M., Bolzani, L., Tabii, Y., & Oulad Haj Thami, R. (2025) · Sensors · doi:10.3390/s25216783 · Cited by 6 · see citing works
DelCastillo-Andrés, O., Fernández-García, R., Pastor-Vicedo, J. C., Lira, M. A., Campos-Mesa, M. C., Castañeda-Vázquez, C., Genovesi, E., Krstulović, S., Kuvačić, G., Morvay-Sey, K., et al. (2026) · Sensors · doi:10.3390/s26082491 · Cited by 1 · see citing works
“Furthermore, unlike datasets such as SisFall [10] that rely on invasive wearable sensors (IMUs), which may alter motor behaviour, we employed a non-intrusive computer vision approach.”
InfluentialUniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · doi:10.20944/preprints201706.0033.v1 · Cited by 596 · see citing works
“This set includes, sorted by year of creation from the oldest the most recent, the following datasets: DLR v2 [32], Ugulino [33], USC HAD [34], DaLiAc [10], EvAAL [35], MHEALTH [36], UCI ARSA [27], BaSA [37], UR Fall Detection [38], MMsys [9], SisFall [39], UMA Fall 4 [18], and REALDISP [40].”
Background
- Authors
- Sucerquia, A., López, J., & Vargas-Bonilla, J.
- Affiliations
- Universidad de Antioquia (CO)
- SISTEMIC, Facultad de Ingeniería, Universidad de Antiquia UDEA, Calle 70 No. 52-21, 1226 Medellín, Colombia
- Corresponding author
- López, José
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-01-20
- Volume
- 17
- Issue
- 1
- Pages
- 198
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5298771
- OpenAlex
- W2543632018
- Cited by
- 530
- References
- 25
- Citations by year
- 2026: 58 · 2025: 64 · 2024: 73 · 2023: 70 · 2022: 69 · 2021: 68 · 2020: 55 · 2019: 38 · 2018: 26 · 2017: 8 · 2016: 1
- FWCI
- 14.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Repository University of Antioquia (University of Antioquia) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universidad de Antioquia UdeA (20139080)
- Ruta N (512C-2013)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- fall detection, activities of daily living, wearable sensors, accelerometer, gyroscope, older adults, threshold-based classification, feature extraction, benchmark dataset
- MeSH terms
- Smartphone, Activities of Daily Living, Algorithms, Humans, Middle Aged, Movement, Monitoring, Ambulatory
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
