Sascha H. Fink

Carinthia University of Applied Sciences, Austria

Abstract Title:

Smartphone-Based Nonlinear Gait Metrics: Novel Instrumentation for Physiotherapy Assessment

Biography:

Sascha Fink, PhD, is a senior scientist and head of the Institute for Human Movement Analysis at Carinthia University of Applied Sciences. His research integrates nonlinear gait analysis, smartphone-based digital biomarkers, and teletherapeutic frameworks to investigate functional movement and sensorimotor control. He studies the relationships between gait variability, proprioception, functional movement, and pain in individuals with musculoskeletal disorders. His work contributes to the development of scalable, ecologically valid assessment and rehabilitation methods. He is actively engaged in translating research findings into clinical practice, designing training programs, and advancing innovative, technology-enabled physiotherapy and digital rehabilitation approaches

Research Interests:

In clinical practice, gait assessment typically relies on linear spatiotemporal parameters and pressure-based metrics obtained from pressure-distribution plates (PDP). Although these systems provide high-resolution information on temporospatial performance and plantar loading, they offer limited insight into the underlying structure and adaptability of motor control—factors increasingly recognised as central to functional capacity, rehabilitation progress, and long-term movement health. Alongside laboratory-based analysis, smartphonederived nonlinear gait metrics gathered semi-supervised over 250 strides on a pavement— including the Coefficient of Variation (CV), Largest Lyapunov Exponent (LLE), and Hurst Exponent (HE)—represent a promising and easy-to-use extension for clinical practice. However, their biomechanical relevance, added value beyond PDP-parameters, and clinical applicability remain insufficiently understood. This study therefore aimed to determine the multiscale information contributed by these nonlinear features and to evaluate how they relate to, complement, or extend conventional gait parameters routinely used in physiotherapy. Across 100 spatiotemporal, kinetic, and plantar-pressure variables collected with a PDP from 74 individuals with and without musculoskeletal disorders, principal component analysis revealed three components representing temporal stability, spatial coordination, and pressure-loading dynamics, together explaining 52% of total variance. LLE demonstrated strong cross-loadings across all three components, indicating that it captures a multiscale stability construct spanning temporal, spatial, and kinetic domains. CV loaded primarily onto temporal stability, consistent with its sensitivity to short-term stride-to-stride timing fluctuations. HE showed no consistent loading, suggesting that it reflects long-range temporal organization largely independent of linear gait dimensions and may therefore capture higherorder control processes not accessible through plate-based systems. Because smartphone-based stride extraction is feasible during continuous walking, this analytic framework was developed to be directly implementable within the 6-Minute Walking Test, enabling clinicians to obtain nonlinear gait signatures without additional equipment. This supports practical, scalable integration into physiotherapy, rehabilitation, and sports medicine and establishes a foundation for future efforts.