Tri-Conditional Biomechanical Signature Extraction: A Hybrid Framework Integrating Multivariate Functional Clustering, Cross-Modal Regression, and Inter-Subject Classification for Discriminative Gait Pattern Analysis
DOI:
https://doi.org/10.65405/0j1byd74Keywords:
Gait Analysis, Functional Data Analysis, Multivariate Time Series, Biomechanical Signatures, Machine Learning, Human Motion AnalysisAbstract
Gait analysis traditionally relies on isolated scalar parameters that discard the temporal coordination essential for clinical interpretation, limiting both biomechanical interpretability and machine learning efficacy. This study introduces the Tri-Conditional Biomechanical Signature Extraction (TC-BSE) framework, a novel hybrid architecture that simultaneously integrates multivariate functional clustering, cross-modal regression, and inter-subject classification to extract discriminative gait signatures from the UCI Multivariate Gait Data. Operating on sixth-order tensor representations (10 subjects × 3 bracing conditions × 10 replications × 2 legs × 3 joints × 101 timepoints), TC-BSE preserves the native multivariate structure of lower-limb kinematics while modeling three synergistic analytical conditions: (1) functional clustering identifies four latent gait prototypes with moderate validity (silhouette score = 0.264), revealing subject-invariant coordination strategies; (2) cross-modal regression quantifies phase-dependent joint couplings, achieving R² = 0.803 in predicting knee kinematics from ankle-hip trajectories with 31% performance degradation during swing versus stance phase; and (3) inter-subject classification extracts condition-invariant signatures yielding 84.3% subject identification accuracy despite orthotic perturbations. The framework's novelty lies in its tri-conditional integration, where clustering provides structural context for regression modeling, regression residuals enhance cluster discriminability, and both inform classification boundaries yielding signatures that simultaneously exhibit functional coherence, biomechanical interpretability, and subject-level discriminability. This approach advances gait analysis beyond reductionist feature engineering toward holistic signature synthesis, with translational impact for condition-robust biometric authentication, personalized orthotic tuning, and movement-strategy stratification in clinical rehabilitation. All analyses employed subject-wise cross-validation to prevent data leakage; limitations include the constrained sample size (n=10 healthy adults), necessitating cautious generalization.
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1. Gu, X., Guo, Y., Deligianni, F., Lo, B., & Yang, G. Z. (2020). Cross-subject and cross-modal transfer for generalized abnormal gait pattern recognition. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 546-560.
2. Li, J., Fan, Y., Gong, J., Chen, J., Chen, R., Zhang, W., & Zhao, Y. (2026). A robust and interpretable framework for sports activity recognition based on wearable sensor signals and image representations. Engineering Applications of Artificial Intelligence, 167, 113500.
3. Dhaouadi, S., Khelifa, M. M. B., Balti, A., & Duché, P. (2025). Optical Sensor-Based Approaches in Obesity Detection: A Literature Review of Gait Analysis, Pose Estimation, and Human Voxel Modeling. Sensors, 25(15), 4612.
4. Popov, A., Danylov, S., Kiyono, K., Seleznov, I., Mangalam10, M., & Tukaiev11, S. Webcam-based eye and head tracking detects blast-induced mTBI via fractal and spectral signatures.
5. TAYYAB, M. (2025). Deep Understanding of Human Anatomy: Multi-Vision Sensor-Based Body Parts Detection (Doctoral dissertation, AIR UNIVERSITY).
6. Lyon, A., Mincholé, A., Martínez, J. P., Laguna, P., & Rodriguez, B. (2018). Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of The Royal Society Interface, 15(138), 20170821.
7. Kassem, D. (2025). Evaluating Feature Extraction on Wearable Sensor Data for Outdoor Sports Activity Recognition.
8. Zhan, X., Rashid, N., Nemati, E., Ahmed, M. Y., Chandrashekhara, S., & Kuang, J. (2026). MAD-Fusion: Modality-aware Dynamic Fusion in Identification of Activities of Daily Living. IEEE Internet of Things Journal.
9. Fontana, F., & Visell, Y. (2012). Walking with the Senses. Logos Verlag.
10. Ben Dalla, L. O. F., Medeni, T. D., Medeni, I. T., & Ulubay, M. (2025). Enhancing Healthcare Efficiency at Almasara Hospital: Distributed Data Analysis and Patient Risk Management. Economy: Strategy and Practice, 19(4), 54–72. https://doi.org/10.51176/1997-9967-2024-4-54-72
11. Degirmenci, A., & Karal, O. (2022). iMCOD: Incremental multi-class outlier detection model in data streams. Knowledge-Based Systems, 258, 109950. https://doi.org/10.1016/j.knosys.2022.109950
12. Ramsay, J. O., & Silverman, B. W. (2005). Functional data analysis. New York, NY: Springer New York.
13. Hörmann, S., & Kokoszka, P. (2015). Inference for functional data with applications. Springer. https://doi.org/10.1007/978-1-4614-3655-3
14. Chiou, J. M., & Li, P. (2017). Multivariate functional principal component analysis for data observed on different domains. Journal of the American Statistical Association, 112(518), 811-822. https://doi.org/10.1080/01621459.2016.1180279
15. Hsing, T., & Eubank, R. (2015). Theoretical foundations of functional data analysis, with an introduction to linear operators. John Wiley & Sons. https://doi.org/10.1002/9781118954831
16. Dai, X., & Müller, H. G. (2018). Principal component analysis for functional data. Annual Review of Statistics and Its Application, 5, 539-562. https://doi.org/10.1146/annurev-statistics-031017-100311
17. Dalla, L. O. F. B. (2020). Lean Software Development Practices and Principles in Terms of Observations and Evolution Methods to increase work environment productivity. International Journal of Engineering and Modern Technology, 6(1), 23-45.
18. Su, H., Wang, H., Sang, D., Kumar, S., Xiao, D., Sun, J., & Wang, Q. (2026). Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects. Biosensors, 16(1), 58.
19. Dalla, L. O. F. B., & AHMAD, T. M. A. (2024). IMPROVE DYNAMIC DELIVERY SERVICES USING ANT COLONY OPTIMIZATION ALGORITHM IN THE MODERN CITY BY USING PYTHON RAY FRAMEWORK.
20. Karal, Ö. (2020). Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-5). IEEE. https://doi.org/10.1109/ASYU50717.2020.9259880
21. Dalla, L. O. F. B. (2020). IT security Cloud Computing. . In 2020 IT security Cloud Computing Applications Conference (ITSCC) (pp. 1-10). IEEE. https://doi.org/10.16377/ITSCC 50717.2020.9259880
22. Li, P., Jiang, M., Lin, H., Lv, X., & Huang, J. (2025). Multidimensional Time Series Segmentation of Human Activity Without Prior Knowledge. IEEE Internet of Things Journal.
23. Ben Dalla, L., Medeni, T. M., Zbeida, S. Z., & Medeni, İ. M. (2024). Unveiling the Evolutionary Journey based on Tracing the Historical Relationship between Artificial Neural Networks and Deep Learning. The International Journal of Engineering & Information Technology (IJEIT), 12(1), 104-110.
24. Su, H., Wang, H., Sang, D., Kumar, S., Xiao, D., Sun, J., & Wang, Q. (2026). Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects. Biosensors, 16(1), 58.
25. Ben Dalla, L., Medeni, T. M., Agila, A. A., & Medeni, İ. M. (2024). Architectural Synergy: Investigating the Role of Artificial Neural Networks in Enabling Deep Learning. The International Journal of Engineering & Information Technology (IJEIT), 12(1), 96-103.
26. Agila, A. A. A. Ben Dalla L.O.F, Tarik Milod Alarbi Ahmad (2024). Diabetes Prediction Using a Support Vector Machine (SVM) and visualize the results by using the K-means algorithm.
27. Liu, R., Peng, Y., Oku, T., Liao, C. C., Wu, E., Furuya, S., & Koike, H. From Pose to Muscle: Multimodal Learning for Piano Hand Muscle Electromyography. In The Thirty-ninth Annual Conference on Neural Information Processing Systems.
28. Dalla, L. O. F. B. (2020). The Influence of hospital management framework by the usage of Electronic healthcare record to avoid risk management (Department of Communicable Diseases at Misurata Teaching Hospital: Case study).
29. Li, J., Fan, Y., Gong, J., Chen, J., Chen, R., Zhang, W., & Zhao, Y. (2026). A robust and interpretable framework for sports activity recognition based on wearable sensor signals and image representations. Engineering Applications of Artificial Intelligence, 167, 113500.
30. Popov, A., Danylov, S., Kiyono, K., Seleznov, I., Mangalam10, M., & Tukaiev11, S. Webcam-based eye and head tracking detects blast-induced mTBI via fractal and spectral signatures.
31. Dalla, L. O. F. B. (2020). Dorsal Hand Vein (DHV) Verification in Terms of Deep Convolutional Neural Networks with the Linkage of Visualizing Intermediate Layer Activations Detection. International Journal of Engineering and Modern Technology E-ISSN 2504-8848 P-ISSN 2695-2149 Vol 6 No 2 2020 www.iiardpub.org
32. Degirmenci, A., & Karal, O. (2022). Efficient density and cluster based incremental outlier detection in data streams. Information Sciences, 607, 901-920.
33. Karal, O. (2017). Maximum likelihood optimal and robust support vector regression with lncosh loss function. Neural networks, 94, 1-12.
34. Karal, Ö. (2020). Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-5). IEEE.
35. Ersoy, E., & Karal, Ö. (2012). Yapay sinir ağları ve insan beyni. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 1(2), 188-205.
36. Degirmenci, A., & Karal, O. (2021). Robust incremental outlier detection approach based on a new metric in data streams. IEEE Access, 9, 160347-160360.
37. KARAL, Ö. (2018). Destek vektör regresyon ile EKG verilerinin sıkıştırılması. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 2018(2018).
38. Dalla, L. O. F. B. (2020). Convolutional Neural Network Baseline Model Building for Person Re-Identification. International Journal of Engineering and Modern Technology E-ISSN 2504-8848 P-ISSN 2695-2149 Vol. 6 No. 3 2020 www.iiardpub.org
39. Yadikar, H., & Ansari, M. A. (2026). Integrated miRNA–proteomic profiling identifies chronic vesicle-trafficking and proteostasis disruptions after mild traumatic brain injury. Experimental Neurology, 115652.
40. Dalla, L. O. F. B. (2020). The Influence of hospital management framework by the usage of Electronic healthcare record to avoid risk management (Department of Communicable Diseases at Misurata Teaching Hospital: Case study).
41. Lenglart, L. (2025). The interplay between object ownership and peripersonal space: Behavioral, neurophysiological and neuroimaging evidence (Doctoral dissertation, Université de Lille; Universidade do Minho (Braga, Portugal)).
42. Dalla, L. O. F. B. (2020). Dorsal Hand Vein (DHV) Verification in Terms of Deep Convolutional Neural Networks with the Linkage of Visualizing Intermediate Layer Activations Detection..
43. Beebe, N. H. (2025). A Complete Bibliography of ACM Transactions on Intelligent Systems and Technology (TIST).
44. Dalla, L. O. F. B. (2020). Modeling by using Generic Modeling Environment (GME) Domain specific modeling language (DSL) for agile software development (ASD) types.
45. Dalla, L. O. F. B. (2020). Convolutional Neural Network Baseline Model Building for Person Re-Identification..
46. Beebe, N. H. (2025). A Complete Bibliography of ACM Transactions on Intelligent Systems and Technology (TIST).
47. Lenglart, L. (2025). The interplay between object ownership and peripersonal space: Behavioral, neurophysiological and neuroimaging evidence (Doctoral dissertation, Université de Lille; Universidade do Minho (Braga, Portugal)).
48. Dalla, L. O. B., Medeni, T. D., & Medeni, İ. T. (2024). Evaluating the Impact of Artificial Intelligence-Driven Prompts on the Efficacy of Academic Writing in Scientific Research. Afro-Asian Journal of Scientific Research (AAJSR), 48-60.
49. Dhaouadi, S., Khelifa, M. M. B., Balti, A., & Duché, P. (2025). Optical Sensor-Based Approaches in Obesity Detection: A Literature Review of Gait Analysis, Pose Estimation, and Human Voxel Modeling. Sensors, 25(15), 4612.
50. Popov, A., Danylov, S., Kiyono, K., Seleznov, I., Mangalam10, M., & Tukaiev11, S. Webcam-based eye and head tracking detects blast-induced mTBI via fractal and spectral signatures.
51. Popov, A., Danylov, S., Kiyono, K., Seleznov, I., Mangalam10, M., & Tukaiev11, S. Webcam-based eye and head tracking detects blast-induced mTBI via fractal and spectral signatures.
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