Spatiotemporal gait parameters (SGP) derived from inertial measurement units (IMUs) are well-established in gait analysis and fall risk assessment. These interpretable features (such as step time, symmetry, double support, etc.) require accurate gait event (GE) detection and are commonly used in clinical and research settings. In contrast, topological data analysis (TDA) is an emerging approach that captures the global geometric and temporal structure of time series without requiring step segmentation. TDA maps signals into higher-dimensional phase spaces via time-delay embedding and quantifies their topological structure using persistent homology.In this study, we directly compare SGP and TDA features for classifying fall risk in a cohort of 78 older adults (41 non-fallers, 37 fallers) recruited at the University Hospital (UZ) Brussels. Each participant completed six IMU-recorded walking trials. SGP features were computed from GE using wavelet-based detection, while TDA features were extracted from vertical-axis acceleration. We also evaluate the impact of the time-delay embedding parameter (Ï„) on TDA classification performance.Our results show that both TDA features and SGP achieved comparable classification performance, with an AUC of 0.81. While the overall performance remained stable across different Ï„ values, subject-level analysis revealed that fallers are more affected by Ï„ variations.These results show that TDA can equal the discriminative power of handcrafted SGP features. To our knowledge, this is the very first direct comparison of SGP and TDA features in gait analysis - across any population - not just in elderly fall-risk assessment. This positions TDA as a promising alternative for future research in wearable sensors and human activity recognition tasks.
Brahimetaj, R, Botti, E, Bautmans, I, Swinnen, E & Jansen, B 2026, Topological Versus Spatiotemporal Gait Parameters for Fall Risk Detection with IMU Sensors. in Ö Durmaz Incel, J Qin, G Bieber & A Kuijper (eds), Sensor-Based Activity Recognition and Artificial Intelligence - 10th International Workshop, iWOAR 2025, Proceedings. vol. 16292, Lecture Notes in Computer Science, vol. 16292 LNCS, Springer Nature, Sensor-Based Activity Recognition and Artificial Intelligence, pp. 156-171, iWOAR 2025, Enschede, Netherlands, 18/09/25. https://doi.org/10.1007/978-3-032-13312-0_9
Brahimetaj, R., Botti, E., Bautmans, I., Swinnen, E., & Jansen, B. (2026). Topological Versus Spatiotemporal Gait Parameters for Fall Risk Detection with IMU Sensors. In Ö. Durmaz Incel, J. Qin, G. Bieber, & A. Kuijper (Eds.), Sensor-Based Activity Recognition and Artificial Intelligence - 10th International Workshop, iWOAR 2025, Proceedings (Vol. 16292, pp. 156-171). (Lecture Notes in Computer Science; Vol. 16292 LNCS). Springer Nature. https://doi.org/10.1007/978-3-032-13312-0_9
@inproceedings{8ee485a3bd8647d2a0b56548688a0aeb,
title = "Topological Versus Spatiotemporal Gait Parameters for Fall Risk Detection with IMU Sensors",
abstract = "Spatiotemporal gait parameters (SGP) derived from inertial measurement units (IMUs) are well-established in gait analysis and fall risk assessment. These interpretable features (such as step time, symmetry, double support, etc.) require accurate gait event (GE) detection and are commonly used in clinical and research settings. In contrast, topological data analysis (TDA) is an emerging approach that captures the global geometric and temporal structure of time series without requiring step segmentation. TDA maps signals into higher-dimensional phase spaces via time-delay embedding and quantifies their topological structure using persistent homology.In this study, we directly compare SGP and TDA features for classifying fall risk in a cohort of 78 older adults (41 non-fallers, 37 fallers) recruited at the University Hospital (UZ) Brussels. Each participant completed six IMU-recorded walking trials. SGP features were computed from GE using wavelet-based detection, while TDA features were extracted from vertical-axis acceleration. We also evaluate the impact of the time-delay embedding parameter (Ï„) on TDA classification performance.Our results show that both TDA features and SGP achieved comparable classification performance, with an AUC of 0.81. While the overall performance remained stable across different Ï„ values, subject-level analysis revealed that fallers are more affected by Ï„ variations.These results show that TDA can equal the discriminative power of handcrafted SGP features. To our knowledge, this is the very first direct comparison of SGP and TDA features in gait analysis - across any population - not just in elderly fall-risk assessment. This positions TDA as a promising alternative for future research in wearable sensors and human activity recognition tasks.",
keywords = "Gait analysis, Spatiotemporal Gait Parameters, Topological Data Analysis, Inertial Measurement Units, Fall Risk Assessment, Wearable Sensors",
author = "Redona Brahimetaj and Elena Botti and Ivan Bautmans and Eva Swinnen and Bart Jansen",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; iWOAR 2025 ; Conference date: 18-09-2025 Through 19-09-2025",
year = "2026",
month = jan,
day = "2",
doi = "10.1007/978-3-032-13312-0_9",
language = "English",
isbn = "978-3-032-13311-3",
volume = "16292",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "156--171",
editor = "{Durmaz Incel}, {\"O}zlem and Jingwen Qin and Gerald Bieber and Arjan Kuijper",
booktitle = "Sensor-Based Activity Recognition and Artificial Intelligence - 10th International Workshop, iWOAR 2025, Proceedings",
url = "https://www.iwoar.org/2025/index.html",
}