研究目的
To design and validate a measurement architecture for automatically estimating intervals of pain over time based on the analysis of facial expressions, addressing the metrological challenges posed by the subjective nature of pain.
研究成果
The VBM system effectively estimates pain intervals from facial expressions by managing uncertainty at the ground-truth level. Results show good accuracy and discrimination capability, with performance improving at higher coverage probabilities. Future work should compare with Monte Carlo methods, model uncertainty in landmark positions, and extend to chronic pain scenarios.
研究不足
The system is calibrated for acute shoulder pain and may not apply to chronic pain due to potential dissociation between pain perception and facial expressions. The uncertainty increases with coverage probability, affecting precision. The study relies on a specific database and evaluator set, limiting generalizability to other pain types or populations.
1：Experimental Design and Method Selection:
The study uses a vision-based measurement (VBM) system with a multi-label classification scheme involving cascade neural networks to estimate pain intervals from facial expressions. A reference measurement procedure is established using evaluators to label pain levels, and uncertainty is managed through coverage probabilities.
2：Sample Selection and Data Sources:
200 video sequences from the UNBC-McMaster Shoulder Pain Expression Archive Database, featuring 25 individuals with shoulder pain, are used. The database includes facial expressions recorded during active and passive range-of-motion tests.
3：List of Experimental Equipment and Materials:
Sony digital cameras for video acquisition, Face++ Research Toolkit by Megvii Inc. for facial landmark detection, and computational tools for feature extraction and neural network training.
4：Experimental Procedures and Operational Workflow:
Facial landmarks are detected using Face++. Static and dynamic feature descriptors are computed from landmark displacements. An ordered set of eight cascade neural networks is trained using a binary relevance logic with labelling vectors derived from evaluator assessments. Performance is evaluated through cross-validation with metrics like sensitivity, specificity, interval completeness, correctness, and Jaccard index.
5：Data Analysis Methods:
Statistical analysis includes computation of frequency distributions, cumulative distribution functions, and performance metrics. Cross-validation with 100 repetitions is used to assess generalization, with results reported as averages and standard deviations.
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