Older people and stroke: a machine learning approach to personalize the rehabilitation of gait

You still DID NOTHING TO CREATE EXACT REHAB PROTOCOLS! But you did have great word salad meaning nothing, I'd have you all fired for incompetence. Stroke research is to get survivors recovered; THIS DID NOTHING TOWARDS THAT!

 Older people and stroke: a machine learning approach to personalize the rehabilitation of gait

Elvira Maranesi¹Federico Barbarossa¹*Leonardo Biscetti²Marco Benadduci¹Elisa Casoni³Ilaria Barboni³Fabrizia Lattanzio¹Lorenzo Fantechi⁴Daniela Fornarelli⁴Enrico Paci⁵Sara Mecozzi⁶Manuela Sallei⁶Mirko Giannoni⁶Giuseppe Pelliccioni²Giovanni Renato Riccardi³Valentina Di Donna⁷Roberta Bevilacqua¹

• ¹Scientific Direction, IRCCS INRCA, Ancona, Italy
• ²Unit of Neurology, IRCCS INRCA, Ancona, Italy
• ³Clinical Unit of Physical Rehabilitation, IRCCS INRCA, Ancona, Italy
• ⁴Unit of Nuclear Medicine, IRCCS INRCA, Ancona, Italy
• ⁵Unit of Radiology, IRCCS INRCA, Ancona, Italy
• ⁶Unit of Neuroradiology, IRCCS INRCA, Ancona, Italy
• ⁷Clinical Unit of Physical Rehabilitation, IRCCS INRCA, Fermo, Italy

Introduction: Stroke is a significant global public health challenge, ranking as the second leading cause of death after heart disease. One of the most debilitating consequences for stroke survivors is the restriction of mobility and walking, which greatly impacts their quality of life. The scientific literature extensively details the characteristics of post-stroke gait, which differs markedly from physiological walking in terms of speed, symmetry, balance control, and biomechanical parameters. This study aims to analyze the gait parameters of stroke survivors, considering the type of stroke and the affected cerebral regions, with the goal of identifying specific gait biomarkers to facilitate the design of personalized and effective rehabilitation programs.

Methods: The research focuses on 45 post-stroke patients who experienced either hemorrhagic or ischemic strokes, categorizing them based on the location of brain damage (cortical-subcortical, corona radiata, and basal ganglia). Gait analysis was conducted using the GaitRite system, measuring 39 spatio-temporal parameters.

Results: Statistical tests revealed no significant differences, but Principal Component Analysis identified a dominant structure. Machine learning (ML) algorithms—Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN)—were employed for classification, with RF demonstrating superior performance in accuracy, precision, recall (all exceeding 85%), and F1 score compared to SVM and KNN. Results indicated ML models could identify stroke types based on gait variables when traditional tests could not. Notably, RF outperformed others, suggesting its efficacy in handling complex and nonlinear data relationships.(This is why you're fired; you produced NOTHING THAT GETS SURVIVORS RECOVERED!)

Discussion: The clinical implication emphasized a connection between gait parameters and cerebral lesion location, notably linking basal ganglia lesions to prolonged double support time. This underscores the basal ganglia’s role in motor control, sensory processing, and postural control, highlighting the importance of sensory input in post-stroke rehabilitation.

1 Introduction

Stroke is a global public health issue, representing the second leading cause of death after heart attack (Khalid et al., 2023) and the sixth highest cause of burden of disease worldwide in terms of disability adjusted life years (Feigin et al., 2025; Johnson et al., 2016). The burden of stroke is projected to increase, with deaths expected to rise by 50% between 2020 and 2050, from 6.6 million to 9.7 million annually (Feigin et al., 2023). Restriction of mobility and walking is a major limitation that stroke survivors typically experience. About 80% of stroke patients are estimated to have ambulatory disability 3 months after the acute event (Govori et al., 2024; Teodoro et al., 2024). Recent studies (Roelofs et al., 2023; Blennerhassett et al., 2012) highlight that despite improvements in gait recovery, about 70% of community-dwelling stroke survivors experience falls within a year, often due to balance loss while walking.

Scientific literature extensively describes the features of post-stroke gait, which differs from physiological walking in terms of speed, symmetry, balance control and biomechanical aspects. Decreased walking speed is a typical sign of post-stroke gait and recent assessments confirm that gait velocity for individuals with post-stroke impairment ranges from approximately 0.18 to 1.03 m/s, whereas healthy age-matched adults average 1.4 m/s (Mohan et al., 2021; Darcy et al., 2024). This substantial difference in walking speed alone accounts for a significant proportion of the variance between post-stroke and physiological gait patterns. Current research confirms that self-selected walking speeds for stroke survivors remain below the 0.80 m/s threshold considered necessary for effective community ambulation (Middleton et al., 2015). Walking speed has been validated as a critical outcome measure for motor recovery, with improvements typically observed from 3 months up to 12–18 months post-stroke, while other functional measures may plateau earlier (Selves et al., 2020; Lee et al., 2015). Temporal and spatial inter-limb asymmetries significantly contribute to the variance in post-stroke gait compared to physiological walking (Lee et al., 2025). Recent literature confirm that spatiotemporal characteristics of post-stroke gait typically include reduced step or stride length and increased step length asymmetry between affected and unaffected sides (Patterson et al., 2010; Wonsetler and Bowden, 2017). A significant negative association is reported between the asymmetry ratios (affected side/unaffected side) of stance time, swing time and stride length with self-selected walking speed (Patterson et al., 2010; Hulleck et al., 2022), as well as an association between greater reduction in stride length and slower walking at patient’s highest-comfortable speed is also described (Beaman et al., 2010). While general gait parameters may improve over time, asymmetrical patterns often persist, presenting a challenge for rehabilitation strategies. Inter-limb spatio-temporal asymmetries of post-stroke gait also correlate with impaired standing balance control (Teodoro et al., 2024; Lewek et al., 2014), which is a further feature of gait in stroke outcomes. Traditional clinical assessments remain valuable but have limitations in capturing subtle gait abnormalities (Kokkotis et al., 2023). For this reason, instrumented gait analysis has become the gold standard for research settings, providing accurate and reliable biomechanical evaluation of key parameters including spatiotemporal, kinematic, and kinetic measures (Hulleck et al., 2022). These laboratory-based assessments typically employ motion capture systems, force platforms and sensor-embedded walkways.

A significant emerging trend is the application of artificial intelligence in predicting functional outcomes and personalizing rehabilitation programs. Machine learning techniques are being developed to identify relationships between stroke characteristics and gait parameters, supporting more tailored and effective rehabilitation strategies (Kokkotis et al., 2023; Jeon et al., 2024; Harari et al., 2020).

The main objective of this paper is to analyze the gait parameters that characterized the stroke survivors, on the basis of type of stroke and interested cerebral area, with the aim of identifying peculiar gait biomarkers for the implementation of personalized and effective rehabilitation programmes. In addition, the secondary aim is to build, tune and test specific machine learning techniques to identify an accurate stratification of the area of stroke damage based on spatio-temporal gait parameters. In this clinical scenario, Artificial Intelligence (AI) could play a crucial role for underpinning the relationship between stroke and gait parameters and thus support a better management of the post-stroke patients by predicting functional outcomes (Kokkotis et al., 2023; Jeon et al., 2024).