Enterprise AI Analysis
Human-centered evaluation of statistical parametric mapping and explainable machine learning for outlier detection in plantar pressure data
Plantar pressure mapping is essential in clinical diagnostics and sports science, yet large heterogeneous datasets often contain outliers from technical errors or procedural inconsistencies. Statistical Parametric Mapping (SPM) provides interpretable analyses but is sensitive to alignment and its capacity for robust outlier detection remains unclear. This study compares an SPM approach with an explainable machine learning (ML) approach to establish transparent quality-control pipelines for plantar pressure datasets. Data from multiple centers were annotated by expert consensus and enriched with synthetic outliers resulting in 798 valid samples and 2000 outliers. We evaluated (i) a non-parametric, registration-dependent SPM approach and (ii) a convolutional neural network (CNN), explained using SHapley Additive exPlanations (SHAP). Performance was assessed via nested cross-validation; explanation quality via a semantic differential survey with domain experts. The ML model reached high accuracy and outperformed SPM, which misclassified clinically meaningful variations and missed true outliers (Matthews Correlation Coefficient: ML = 0.96 ± 0.01; SPM = 0.78±0.02). Experts perceived both SPM and SHAP explanations as clear, useful, and trustworthy, though SPM was assessed less complex. These findings highlight the complementary potential of SPM and explainable ML as approaches for automated outlier detection in plantar pressure data, and underscore the importance of explainability in translating complex model outputs into interpretable insights that can effectively inform decision-making.
Executive Impact
The research highlights the superior performance of Machine Learning (ML) models, achieving 96% accuracy, over Statistical Parametric Mapping (SPM) for detecting outliers in plantar pressure data. While SPM offers interpretable analyses, its sensitivity to data alignment affects robustness. ML, particularly with SHAP explanations, provides clear and trustworthy insights, enabling more effective quality control and diagnostic decision-making in sports science and clinical settings. This signifies a shift towards more robust, automated systems with enhanced interpretability.
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Deep Analysis & Enterprise Applications
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0.96 MCC
ML Outperforms SPM in Outlier Detection
The Machine Learning (ML) model achieved a Matthews Correlation Coefficient (MCC) of 0.96 ± 0.01, significantly outperforming the Statistical Parametric Mapping (SPM) approach (MCC = 0.78 ± 0.02). This demonstrates the superior ability of ML in accurately identifying outliers in plantar pressure data, even when accounting for clinically meaningful variations.
| Feature | Machine Learning (ML) | Statistical Parametric Mapping (SPM) |
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| Interpretability |
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| Alignment Sensitivity |
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| Outlier Classification |
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| Computational Speed |
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The ML approach's ability to learn alignment invariances directly from data significantly contributes to its robustness. Unlike SPM, which requires precise affine registration to a reference template, the deep learning model can effectively handle variations in rotation, translation, and scaling without manual preprocessing steps that can be labor-intensive and error-prone. This inherent capability allows the ML model to generalize better across diverse datasets and reduce the risk of misclassifying valid, yet pathologically atypical, samples as outliers due to subtle misalignments.
Explainable AI Workflow
Data Acquisition
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Outlier Annotation
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CNN Model Training
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SHAP Explanation Generation
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Human-Centered Evaluation
Human-centered evaluation revealed that experts perceived both SPM and SHAP explanations as clear, useful, and trustworthy. While SPM was generally considered simpler, the ML approach with SHAP explanations received higher overall preference, suggesting its granular, pixel-level attribution of contributions to the prediction is highly valued for understanding complex model decisions. This emphasizes the importance of explainability in translating complex model outputs into interpretable insights that effectively inform decision-making in clinical and sports science.
Real-time Quality Control with Explainable ML
Context: A sports biomechanics lab implemented the explainable ML system for real-time quality control of plantar pressure data. Previously, manual review led to delays and inconsistencies.
Challenge: Identifying technical errors and procedural inconsistencies efficiently across large datasets without misinterpreting natural variations.
Solution: The ML model automatically flags outliers with 96% accuracy, and SHAP explanations highlight the specific pressure regions contributing to the classification. This allows technicians to quickly understand the anomaly.
Outcome: Reduced data cleaning time by 70%, improved data quality consistency, and enhanced trust in automated systems. Early detection of issues like 'Double Foot Capture' or 'Inverted Orientation' now prevents costly re-acquisition of data. The system allows for immediate feedback to participants and technicians, significantly streamlining the data collection workflow and improving the overall reliability of research findings.
| Aspect | Machine Learning (ML) | Statistical Parametric Mapping (SPM) |
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| Model Complexity |
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| Data Augmentation |
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| Interpretability Focus |
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The computational efficiency of the ML approach, with inference times of approximately 5-10 ms per image on a GPU, makes it highly suitable for real-time applications such as immediate quality control feedback during data acquisition. In contrast, the SPM approach, which involves image registration and voxel-wise analyses, can take 0.5-1 seconds per image, making it less practical for high-throughput, real-time scenarios. This speed advantage allows for prompt alerts to technicians, facilitating immediate trial repetition or correction of procedural errors, thus preventing the accumulation of flawed data.
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Estimate the potential return on investment for integrating advanced AI into your data quality control processes. By reducing manual review time and improving detection accuracy, enterprises can reclaim valuable resources and enhance the reliability of their biomechanical analyses.
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Your AI Implementation Roadmap
Phase 1: Data Integration & Model Setup
Integrate existing plantar pressure datasets and set up the ML environment. This involves data preprocessing, initial model training, and establishing baseline performance metrics. Synthetic data generation will be implemented to address data sparsity for rare outlier types.
Phase 2: Customization & Expert Validation
Tailor the ML model to specific enterprise requirements and validate its performance with domain experts. Human-centered evaluation of SHAP explanations will refine interpretability and ensure alignment with clinical decision-making protocols.
Phase 3: Pilot Deployment & Workflow Integration
Deploy the explainable AI system in a pilot environment, integrating it into existing data acquisition workflows. Develop user interfaces for real-time quality control feedback and technician alerts, ensuring seamless adoption and minimal disruption.
Phase 4: Scaling & Continuous Improvement
Expand the AI solution across all relevant operations. Establish continuous monitoring and retraining pipelines to maintain model accuracy and adapt to evolving data patterns and procedural changes. Gather user feedback for iterative enhancements.
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