Personalized Emotion-Adaptive Automation

• Personalized emotion-adaptive automation is an affective computing paradigm that analyzes real-time physiological and behavioral signals to customize system responses.
• The methodology employs hierarchical clustering and per-cluster AI models such as KNN to accurately classify emotional states and trigger appropriate adaptations.
• This approach improves system accuracy and responsiveness in safety, health, and human-computer interaction applications by continuously updating user-specific models.

Personalized emotion-adaptive automation is an affective computing paradigm that operationalizes individual differences in emotional reactions for system-level adaptation and intervention. Rather than relying on generic, user-independent predictive models, this approach leverages clustering, per-user or per-typology predictive modeling, and continuous updating to drive real-time decision-making in safety, health, and human-computer interaction contexts. Central to its technical design is a continuous intake of physiological and behavioral signals, their transformation into statistically and/or semantically meaningful features, and the subsequent tailoring of response or adaptation policies to clusters of subjects sharing similar affective profiles or to individual users. The concept is especially pertinent in domains such as wearable safety devices, mental health support, and welfare applications, where inter-individual variability in emotional response carries direct functional implications (Gutierrez-Martin et al., 2024).

1. Signal Acquisition, Feature Engineering, and Labeling

Personalized emotion-adaptive systems begin with multimodal signal collection, typically via discrete, unobtrusive sensors integrated into wearables or real-world environments. The canonical workflow includes:

• Physiological Sensing: Galvanic Skin Response (GSR), Skin Temperature (SKT), and Blood-Volume Pulse (BVP) via systems such as BioSignalsPlux. Heart rate (HR) and heart-rate variability (HRV) are derived from BVP.
• Temporal Segmentation: Sliding windows of 20 s with 50 % overlap; each window yields 57 features spanning statistical, time- and frequency-domain analyses (e.g., mean, standard deviation, peaks, slopes, spectral bands).
• Feature Normalization: For each subject, features are z-normalized: $x' = (x – \mu_{\text{subj}})/\sigma_{\text{subj}}$.
• Label Collection: Binary emotional labels (“fear” vs. “non-fear”) are acquired immediately post-stimulus (e.g., after 14 VR events per the WEMAC protocol).

This granular feature-based representation, coupled with detailed self-reporting, is a prerequisite for effective personalization and cluster formation.

2. Optimal Clustering of Affective Responses

Automated, emotion-adaptive personalization proceeds by partitioning the user population into typological clusters with homogeneous affective responsivity:

• Hierarchical Clustering: Euclidean distance with Ward’s linkage is applied across feature vectors. The algorithm searches $k=2..10$ clusters and selects $k^*$ that maximizes the Dunn index:

$$\text{Dunn} = \frac{\min_{i \neq j} d(C_i, C_j)}{\max_{k} \Delta(C_k)}$$

where $d(C_i, C_j)$ is centroid distance, and $\Delta(C_k)$ is maximum intra-cluster distance.

• Cluster Size Constraint: Each cluster must maintain ≥15 % of the subject pool; undersized clusters are merged.
• Objective Function (Variance Minimization):

$$J = \sum_{i=1}^K \sum_{x \in C_i} \lVert x - \mu_i \rVert^2$$

with $\mu_i = \frac{1}{|C_i|}\sum_{x \in C_i} x$.

By enforcing both quality and balance constraints, the system prevents overfitting and maintains robustness across demographic subpopulations.

3. Per-Cluster Personalized AI Modeling

Once clusters are established, emotion recognition proceeds via simple yet powerful AI models individualized to each cluster:

• K-Nearest Neighbors (KNN): The classifier utilizes Euclidean distance and includes cost-sensitive weighting to reflect application-specific priorities (e.g., $cost(\text{fear} \rightarrow \text{non-fear})=1.6$, $cost(\text{non-fear} \rightarrow \text{fear})=1$).
• Hyperparameter Optimization: Number of neighbors $k$ and weighting schemes $w_n=1/\text{distance}(x,x_n)$ or uniform weight are tuned via Bayesian optimization with 5-fold cross-validation under leave-one-subject-out (LOSO) splitting.
• Loss for Hyperparameter Tuning:

$L = \frac{1}{N} \sum_{i=1}^N cost_{y_i, \hat{y}_i}$

aligning misclassification penalties with application requirements.

This per-cluster modeling yields significant gains in both accuracy (+4%, from 60.8% to 64.1%) and F1-score (+3%), while reducing variability (std dev) in performance by 14% (Gutierrez-Martin et al., 2024).

4. Dynamic Model Updating and Subject Enrollment

Adaptation extends beyond initial modeling to continual real-world updating:

• New Subject Assignment (Labeled and Unlabeled):
  • Labeled Data: Subject S*’s profile vector $p$ is matched to cluster centroids by $\arg\min_{i} \lVert p - \mu_i \rVert$.
  • Unlabeled Data: For each subject cluster TC$_k$, internal clusters IC$_{kp}$ are compiled; S*’s observations $o_j$ are matched by summing minimum distances to internal centroids:

  $$D_k = \sum_{j=1}^M \min_p \lVert o_j - c_{kp} \rVert$$

  S* is assigned to $\arg\min_k D_k$.

• Centroid Update:

$$\mu_i^{(\text{new})} = \frac{n_i \cdot \mu_i^{(\text{old})} + p^*}{n_i + 1}$$

• Model Retraining: Either via periodic full retraining or online KNN augmentation by integrating new labeled neighbors directly.

These protocols support real-time personalization and typology evolution in multiuser protection contexts.

5. Real-Time Automation Workflow and Adaptation Policies

The system operationalizes emotion detection for dynamic automation control via an efficient, continuous loop:

initialize ClusterModels = { Model₁,…,Model_K } initialize Centroids = { TC₁, …, TC_K } loop every T seconds: raw ← read_sensors() feats ← extract_features(raw) # Profile assignment for k in 1…K: D_k ← sum(minₚ || o – c_{k,p} || for o in feats) cluster_id = argmin_k D_k # Emotion inference norm_feats ← z_normalize(feats, subject_stats) ŷ ← ClusterModels[cluster_id].predict(norm_feats) emotion ← majority_vote(ŷ) # Automation adaptation params ← adaptation_map[cluster_id][emotion] apply_system_changes(params) # Optional update if user_confirms_label(y_true): update ClusterModels[cluster_id] with (feats, y_true) update centroid Centroids[cluster_id] end loop

Example adaptation mappings specify differential actuation parameters for high GSR responders (fear → alert+30%, haptic-vibration) vs. low-reactivity users (fear → alert+15%, visual warning only). The actions could include task throttling, UI simplification, or security alarms (Gutierrez-Martin et al., 2024).

6. Quantitative Validation and Robustness Analysis

The empirical evaluation demonstrates:

• Consistent performance improvement over general models: +4% accuracy, +3% F1, −14% variability.
• Stability across validation folds and robustness tests, with ≥5% accuracy drop for out-of-cluster subjects, confirming the necessity of cluster-driven personalization.
• Subject cohort: 44 women (after discarding 3 subjects for anomalous signals).
• Cross-validation: both random 20-fold splits and LOSO protocols recompute clusters and models iteratively.

A direct implication is that clustering delivers actionable affective typologies yielding superior safety, engagement, and reduced false negatives in critical applications (Gutierrez-Martin et al., 2024).

7. Applications and Implications for Wearable, Real-Time Affective Computing

Personalized emotion-adaptive automation instantiated via this cluster-driven AI pipeline is:

• Directly implementable in low-power, wearable devices (battery-powered, wireless).
• Highly relevant to safety domains—violence, abuse, mental health—where group-level generalizations fail and user-level customization enables effective intervention.
• Extensible to multiuser systems via typological clustering, supporting health, law enforcement, and welfare professionals.
• Capable of fast inference, discrete operation, and continual model evolution.

The integration of multimodal signal analysis, unsupervised typology clustering, cost-sensitive classification, and adaptive control forms an end-to-end architecture suitable for real-world deployment in scalable, personalized, emotion-adaptive systems (Gutierrez-Martin et al., 2024).