Comparative Analysis of Different AI Approaches to Stroke Patients’ Gait Analysis
Abstract
1. Introduction
- Limited validation in diverse populations due to any AI models have been developed and validated in controlled settings with small sample sizes, often characterized by a lack of diversity in age, gender, and ethnicity. This limits their generalizability to broader patient populations [30].
- Lack of standardization because a lack of consensus on standardized protocols for data collection, feature extraction, or model evaluation leads to inconsistencies across studies and hinders comparison of results or their integration into clinical practice [31].
- Data privacy concerns because the use of wearable sensors and video recordings poses significant privacy issues, especially for sensitive medical data. Ensuring data security and patient consent is a priority but often insufficiently addressed.
- Interpretability of AI models since many AI systems act as “black boxes,” providing predictions without clear explanations. This lack of interpretability can hinder clinician trust and acceptance, as understanding the rationale behind decisions is crucial to decision-making [32].
- Integration with clinical processes because AI tools often operate in isolation and are not seamlessly integrated with existing clinical processes or electronic health record systems, limiting their utility in real-world settings [33].
- Practical application: most AI models are tested in controlled environments, but their effectiveness in real-world settings, such as patients’ homes or community clinics, remains understudied [34].
- Challenges with longitudinal monitoring because continuous gait monitoring over extended periods is essential to assessing rehabilitation progress, but current AI systems may lack long-term tracking capabilities or be impractical for daily use [35].
- Multimodal data integration: Integrating data from various sources, such as video, wearable sensors, and clinical assessments, poses challenges in data fusion and analysis but is essential for comprehensive gait analysis [36].
- Personalization of interventions because AI models often provide generalized conclusions without tailoring recommendations to individual patient needs, which is crucial for effective rehabilitation [37].
- Regulatory and ethical issues due to implementing AI in clinical settings raises regulatory concerns about safety, effectiveness, and ethical issues that are still being addressed by policymakers and regulators.
- Methods for rapid and easy-to-automate gait analysis;
- New gait parameters, including fuzzy and fractal logic;
- Methods for objectifying the assessment of gait rehabilitation effectiveness.
2. Materials and Methods
2.1. Dataset
- Ethics and consent: all participants are recruited only after informed consent, explaining how gait data, and health information will be collected, stored, and used, and consent forms specify data-sharing conditions;
- De-identification and anonymization: raw clinical identifiers (name, date of birth, medical record numbers) are replaced with randomized study IDs;
- Data storage and transfer security: data are stored in encrypted servers compliant with GDPR standards, Transfer between sensors and servers uses end-to-end encryption (VPN), Role-based access control ensures only authorized clinicians/researchers access sensitive data;
- Minimization and purpose limitation: only the minimum necessary dataset for analysis is collected (e.g., gait parameters instead of raw continuous video whenever possible).
2.2. Statistical Analysis
2.3. Computational Methods
3. Results
4. Discussion
- Overarching study design: technical/bench testing, prospective observational validation, clinical impact trial;
- Participant selection & sample size (practical guidance): broad range of post-stroke patients (from acute to chronic), varying severity (functional levels), assistive device users vs. independent walkers and comorbidities typical for clinic, sample size guidance (algorithm internal validation, external validation);
- Data collection;
- Outcome measures: clinically meaningful outcomes, technical performance metrics;
- Evaluation metrics;
- Extended statistical analysis (within dataset, between model comparisons, agreement and subgroup analysis;
- Blindig, bias and generalizability,
- Safety of patients and clinical workflow testing;
- Regulatory, ethical and data governance issues;
- Practical logistics;
- Reporting and transparency.
4.1. Limitations of Previous Studies and the Current Study
4.2. Technological Implications
4.3. Economical Implications
4.4. Societal Implications
4.5. Ethical and Legal Implications
4.6. Directions for Further Research
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Arificial intelligence |
CNN | Convolutional neural network |
DL | Deep learning |
ML | Machine learning |
ReLU | Rectification linear unit |
RF | Random forests |
SVM | Support vector machine |
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Parameter | Study Group (n = 120) |
---|---|
Age [years] | |
mean | 62.5 |
SD | 8.91 |
min | 40 |
Q1 | 59 |
Median | 64 |
Q3 | 71 |
Max | 81 |
Gender | |
male | 60 (50%) |
female | 60 (50%) |
Time after stroke [weeks] | |
mean | 56.7 |
SD | 7.98 |
min | 15 |
Q1 | 22 |
Median | 61 |
Q3 | 102 |
Max | 143 |
Side affected | |
Left | 51 (42.50%) |
Right | 69 (57.50%) |
Subset | Number of Patients | Percentage of Total | Class 1 [%] | Class 2 [%] |
---|---|---|---|---|
Training | 84 | 70 | 50 | 50 |
Validation | 12 | 10 | 50 | 50 |
Testing | 24 | 20 | 50 | 50 |
Algorithm | Accuracy [%] | RMSE [-] |
---|---|---|
CNN | 91.88 | 0.001 |
SVM | 89.91 | 0.01 |
kNN | 87.52 | 0.02 |
RF | 85.41 | 0.02 |
Class | Precision | Recall | F1 | 95% CI (F1) |
---|---|---|---|---|
1 | 91.12 | 91.12 | 91.88 | 0.88–0.94 |
2 | 91.02 | 91.02 | 91.56 | 0.88–0.94 |
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Rojek, I.; Mikołajewska, E.; Małolepsza, O.; Kozielski, M.; Mikołajewski, D. Comparative Analysis of Different AI Approaches to Stroke Patients’ Gait Analysis. Appl. Sci. 2025, 15, 10896. https://doi.org/10.3390/app152010896
Rojek I, Mikołajewska E, Małolepsza O, Kozielski M, Mikołajewski D. Comparative Analysis of Different AI Approaches to Stroke Patients’ Gait Analysis. Applied Sciences. 2025; 15(20):10896. https://doi.org/10.3390/app152010896
Chicago/Turabian StyleRojek, Izabela, Emilia Mikołajewska, Olga Małolepsza, Mirosław Kozielski, and Dariusz Mikołajewski. 2025. "Comparative Analysis of Different AI Approaches to Stroke Patients’ Gait Analysis" Applied Sciences 15, no. 20: 10896. https://doi.org/10.3390/app152010896
APA StyleRojek, I., Mikołajewska, E., Małolepsza, O., Kozielski, M., & Mikołajewski, D. (2025). Comparative Analysis of Different AI Approaches to Stroke Patients’ Gait Analysis. Applied Sciences, 15(20), 10896. https://doi.org/10.3390/app152010896