Prakriti Assessment Questionnaire

• Prakriti Assessment Questionnaire is a structured tool that quantifies an individual's Ayurvedic constitution using standardized survey items across physical, physiological, and psychological domains.
• It employs automated, rule-based scoring and machine learning algorithms to classify dosha categories accurately by analyzing weighted response vectors.
• Integrated digital deployment and advanced statistical models facilitate personalized health analytics, supporting clinical research and future multimodal phenotyping.

A Prakriti Assessment Questionnaire is a systematically designed instrument to quantify the constitutional makeup (“prakriti”) of individuals according to classical Ayurvedic principles. These questionnaires typically consist of multiple-choice items covering a comprehensive set of physical, physiological, and psychological domains. They operationalize expert-driven traditional constructs using standardized, structured survey techniques and are paired with automated or algorithmic scoring systems to objectively classify individuals into one or more “dosha” categories (Vata, Pitta, and Kapha, including their overlaps). Adaptations of such questionnaires serve as fundamental tools for clinical research, computational modeling, and personalized health analytics.

1. Design and Standardization of the Prakriti Assessment Questionnaire

Prakriti Assessment Questionnaires are constructed to evaluate multifaceted traits as defined in Ayurveda. A representative instrument, as described in (Singh et al., 5 Oct 2025), comprises 24 multiple-choice questions, each mapped rigorously to classical constitutional features. Every question is mandatory, guaranteeing complete and non-missing response sets.

The questionnaire domains encompass:

• Physical: Body size, stature, bone structure, skin complexion
• Physiological: Appetite, sleep, energy, bowel habits, metabolic markers
• Psychological: Temperament, concentration, patience, focus

Development aligns closely with AYUSH/CCRAS guidelines. Expert review and validation refine question content and phrasing, and neutral wording eliminates bias—dosha labels are hidden from participants to suppress expectancy effects. The questionnaire is bilingual (English-Hindi), maximizing accessibility and preserving semantic clarity across languages.

2. Dosha Scoring and Algorithmic Classification

All responses are mapped to dosha scores using rule-based algorithms. Typically, a response vector for an individual ($\textbf{r}$) is processed as:

• $Vata\_Score = \sum_i w_i^{(V)} \cdot r_i$
• $Pitta\_Score = \sum_j w_j^{(P)} \cdot r_j$
• $Kapha\_Score = \sum_k w_k^{(K)} \cdot r_k$

where $w_i^{(V)}, w_j^{(P)}, w_k^{(K)}$ are predefined weights based on Ayurvedic principles and expert consensus. The dominant dosha is assigned according to the highest cumulative score.

Automated scoring pipelines, deployed as backend scripts integrated with the survey platform (typically Google Forms and Google Sheets for digital data capture), produce reproducible and bias-minimized dosha assignments (Singh et al., 5 Oct 2025).

3. Data Collection, Digital Deployment, and Dataset Construction

Contemporary administrations of these assessments utilize digital survey tools (e.g., Google Forms), ensuring scalability and uniformity in data collection. All items are mandatory, guaranteeing structural completeness.

Upon completion, responses are compiled in a structured output (e.g., Prakriti_Dataset.xlsx), with columns for demographic data, individual items, computed dosha scores, and final classifications. This digital-first architecture supports high-throughput data gathering, minimizes manual processing, and facilitates computational analysis pipelines.

A quintessential example is the Prakriti200 dataset (Singh et al., 5 Oct 2025), comprising 200 standardized assessments, each mapped to dosha labels via an automated backend.

4. Statistical Modeling and Latent Structure Inference

For advanced statistical analysis, latent factor models provide high-dimensional representation learning from questionnaire data, accommodating mixed categorical and continuous item types, along with missingness (Mclaughlin et al., 2021). Each response vector $y_n$ for subject $n$ is modeled as arising from latent variables $x_n$:

• For categorical: $$P(y_{nj}|x_n) \sim \operatorname{Bernoulli}(P_{nj})$$, with $logit(P_{nj}) = A_j \cdot x_n + b_j$
• For continuous: $Vata\_Score = \sum_i w_i^{(V)} \cdot r_i$0

Missing data is handled directly in the likelihood, with $Vata\_Score = \sum_i w_i^{(V)} \cdot r_i$1 for unobserved entries. Expectation-Maximization algorithms infer the latent projections ($Vata\_Score = \sum_i w_i^{(V)} \cdot r_i$2), bases ($Vata\_Score = \sum_i w_i^{(V)} \cdot r_i$3), and intercepts ($Vata\_Score = \sum_i w_i^{(V)} \cdot r_i$4). This produces a lower-dimensional, interpretable latent space summarizing each subject's constitution, supporting clustering and population structure analyses.

5. Machine Learning for Enhanced Prakriti Classification

High-dimensional categorical assessments motivate the application of machine learning from feature selection to classification. A comprehensive pipeline includes:

1. Feature Engineering: Categorical responses are subjected to the Chi-Square test ($Vata\_Score = \sum_i w_i^{(V)} \cdot r_i$5) for relevance ranking. SelectKBest is commonly used for optimal feature set construction (Bidve et al., 2023).
2. Cluster Formation: K-modes clustering is applied to categorical features to partition individuals into discrete or overlapping dosha groups (seven utilized: three pure, three dual, one tri-dosha).
3. Classification: Multinomial Naïve Bayes (MNB) classifier, which models the posterior as $Vata\_Score = \sum_i w_i^{(V)} \cdot r_i$6, demonstrates higher performance compared to decision trees. Empirical metrics: accuracy ≈ 0.90, precision ≈ 0.81, F-score ≈ 0.91, recall ≈ 0.90, with model robustness sensitive to feature selection.

These methods allow extension from traditional three-group dosha assignments to fine-grained, overlapping constitutional categories, enabling more individualized clinical recommendations (Bidve et al., 2023).

6. Integration with Physiological and Subjective Markers

Composite assessments further integrate questionnaire outputs with objective physiological markers (reaction time, HRV/PRV indices, photoplethysmography) for multi-parametric stress quantification (Apoorvagiri et al., 2015). In such protocols:

• Subjective measures (e.g., PSS questionnaire scores) establish a baseline for perceived stress.
• Objective physiological signals are acquired (reaction time, ECG/PPG for HRV/PRV), pre-processed, and features extracted (e.g., sample entropy: $Vata\_Score = \sum_i w_i^{(V)} \cdot r_i$7).
• Data-driven classifiers (neural networks) are trained for categorical stress classification.
• Pearson correlation analyses validate the alignment between subjective and physiological domains (e.g., a correlation of −0.943 between HRV entropy and PSS score).

This multimodal strategy improves the reliability and clinical utility of the traditional questionnaire methodology by mitigating exclusive reliance on self-reported data.

7. Applications, Research Implications, and Future Directions

Prakriti Assessment Questionnaires function as foundational tools for research at the intersection of Ayurveda and data science. Structured datasets such as Prakriti200 enable:

• Computationally robust validation and extension of rule-based dosha scoring using supervised, unsupervised, and latent variable models.
• Examination of trait-dosha correlations, population-scale constitutional mapping, and precision lifestyle or therapeutic stratification.
• Benchmarking for future multimodal datasets incorporating imaging and physiological modalities.

Anticipated future directions include scaling assessments to larger, demographically diverse populations; extending questionnaires with multimodal phenotyping (facial, tongue, pulse data); and integrating fairness and bias analyses in computational models (Singh et al., 5 Oct 2025). These lines of research will underpin the development of intelligent health decision-support tools and promote rigorous methodological standards in personalized Ayurvedic analytics.