Safeguarding frontline surveillance agents and security personnel requires the timely recognition of hostile intent and the proactive mitigation of imminent escalation. Conventional monitoring workflows—reliant on manual observation and post hoc reporting—often fail to capture early precursors of aggression under real-world constraints such as crowd dynamics, occlusions, variable lighting, acoustic clutter, and heightened stress. Empirical studies indicate that informative cues are subtle, short-lived, and distributed across multiple channels, including on-body physiology, visual behavior, and ambient audio, which motivates multimodal sensing and learning pipelines [1]. Physiological signals such as heart-rate variability and electrodermal activity, together with respiration, have been used for stress and affect recognition with encouraging accuracy on benchmark datasets; single-modality wearable approaches have also been explored, typically with modest sample sizes [2–4].

Despite steady progress, important gaps remain for deployment in safety-critical settings. First, many systems prioritize pointwise recognition of ongoing events rather than short-horizon forecasting that can provide operational lead time for de-escalation and resource allocation [1,2,4]. Second, pipelines that depend on a single sensing channel are vulnerable to real-world missingness and sensor degradation; multimodal designs that degrade gracefully are better suited to field conditions [1,3]. Third, purely black-box models hinder accountability, operator trust, and policy alignment. These limitations motivate solutions that combine temporal modeling, multimodal robustness, and interpretable decision mechanisms. Lightweight sequence predictors—such as recurrent neural networks—are well established for capturing nonlinear temporal dependencies across diverse forecasting problems and offer a principled apparatus for short-horizon risk estimation under realistic constraints [5–9]. In parallel, advances in the Internet of Battlefield/Things and wearable platforms support continuous, in-the-wild monitoring and alerting on low-power hardware [10].

This work introduces an Aggression Detection Prediction System (ADPS) designed for latency-compatible pilot-stage operation on mobile devices. The system integrates heterogeneous on-body and ambient signals through a compact feature pipeline and employs sequence models to forecast risk over short horizons. To ensure explainability and actionability, ADPS couples the temporal predictor with an interpretable, rule-based decision layer—intuitively, a soft logical and that multiplies degrees of evidence (see Section 5)—that encodes domain knowledge and translates probabilistic outputs into concise, auditable alerts. The design emphasizes calibration, stability under missingness, and computational efficiency, with the aim of meeting practical targets for latency and memory on affordable edge platforms. Beyond discrimination, the evaluation framework considers deployment-oriented metrics and subject-held-out protocols to reduce leakage and better approximate field generalization.

Contributions.

This study advances deployment-aware, interpretable, short-horizon aggression-risk support under resource constraints through five tightly connected contributions that form a single methodological thread:

Mobile, short-horizon forecasting. A modular ADPS that performs on-device short-horizon risk prediction, complementing conventional pointwise detection.

Taken together, these contributions should be read as a single design logic: the SCS standardizes heterogeneous on-body and ambient evidence, the LBPS supplies short-horizon temporal context, and FLAD transforms current and forecasted cues into calibrated, auditable risk posteriors suitable for pilot-stage edge deployment.

The working hypothesis is that short-horizon temporal modeling can improve prospective sensitivity because recurrent predictors capture nonlinear temporal dependencies that are not available to pointwise classifiers [5–9]. The interpretable fuzzy decision layer is expected to reduce false alarms by requiring convergent, auditable evidence before a high-risk posterior is emphasized, which is particularly important for safety-critical mobile monitoring [1–4]. Complementary on-body and ambient cues should also yield more stable forecasts than single-modality pipelines under realistic missingness, because degraded physiological, visual, contextual, or audio channels can be partially compensated by the remaining evidence streams [1,3,10]. Consequently, ADPS is designed to test whether forecasted physiological-affective dynamics, local scene cues, and an auditable rule layer jointly improve the sensitivity–false-alarm trade-off while preserving calibration and operator interpretability. With careful model design, on-device inference can remain latency-compatible on affordable edge hardware; therefore, the evaluation complements pointwise latency profiling with a continuous 6 h characterization of CPU load, throughput stability, thermal behavior, and a battery-based energy proxy.

The article is organized as follows. Section 2 provides a concise survey of related work and presents a condensed comparison table that situates the contribution within multimodal sensing, wearable/physiological analytics, and short-horizon temporal modeling. Section 3 reports the main results with thematic subheadings, including performance against baselines, ablation studies and feature impact, calibration analysis, and on-device efficiency (latency and memory). Section 4 offers the Discussion, interpreting the findings, outlining limitations, and positioning the practical implications for field deployment. Section 5 details the Methods, covering participants/datasets, preprocessing and the feature pipeline, sequence modeling and multimodal fusion, the interpretable rule-based layer, training and evaluation protocols, statistical analysis, on-device deployment procedures, and ethics. To reinforce a unified reading of the contribution, the main text now includes an architectural flowchart that explicitly links multimodal sensing, short-horizon prediction, fuzzification, bounded-product aggregation, and calibrated decision output. Section 6 concludes with a brief synthesis of contributions and directions for future work. The back matter includes Data Availability, Code Availability, Author Contributions, Competing Interests, Acknowledgments, and Additional Information; references are followed by figure legends and any remaining tables, with extended materials provided as Appendix A.

Research on aggression detection intersects affect and stress recognition, multimodal fusion, and violence- or firearm-related computer vision. Multimodal pipelines commonly integrate audio, video, and text-derived metadata with deep neural networks for feature selection, dimensionality reduction, and prediction [1]. Physiology-based methods leveraging heart-rate variability (HRV), electrodermal activity (EDA), and respiration—often from WESAD and SWELL-KW—report strong accuracy with classical and hybrid classifiers [2,4], whereas wearable HRV-only approaches show more modest performance in small pilot cohorts [3]. Beyond static recognition, lightweight sequence models such as LSTMs are widely adopted to capture nonlinear temporal dependencies across forecasting tasks relevant to environmental, hydrological, and process domains [5–9]. Emerging Internet-of-Battlefield/Things deployments further indicate the feasibility of continuous, in-the-wild monitoring and alerting on low-power platforms [10]. Recent adjacent early-warning studies also highlight two design directions relevant to ADPS. First, Haider et al. proposed an edge-efficient smart-city fire-recognition framework that couples a lightweight backbone with Strip Pooling Coordinate Attention (SPCA) and progressive multi-scale fusion, showing that directional attention can improve robustness while preserving computational efficiency in safety-oriented visual recognition [11]. Second, Huang et al. introduced a YOLOv8 + Multi-Head Transformer architecture with adaptive weighted loss for fatigue-driving detection, illustrating how transformer-based temporal modeling and loss reweighting can improve robustness under illumination changes, occlusion, and long-duration monitoring conditions [12]. Although these works address adjacent application domains rather than aggression forecasting directly, they strengthen the broader methodological context for edge-compatible early warning, attention-enhanced perception, and sequence modeling under practical operating constraints.

Despite this progress, most prior studies emphasize offline, pointwise classification rather than prospective short-horizon risk forecasting; many rely on single-modality or lab-constrained datasets and seldom report deployment-oriented metrics such as on-device latency, memory, or energy. Interpretability is also limited when end-to-end black-box models are used exclusively. Moreover, the recent literature increasingly combines attention mechanisms, lightweight edge-oriented backbones, and transformer-based temporal reasoning, suggesting a broader shift toward robust, deployment-aware multimodal warning systems that deserves explicit recognition in the aggression-risk literature. To situate the present work, Table 1 condenses representative studies by variables, methods, and headline results.

Table 2 reports held-out episode-level test performance of the fuzzy logic aggression detector (FLAD), computed one-vs.-rest per class (Low, Medium, High) together with macro and weighted aggregates, including Precision (PR), Recall (RC), F1-score, Specificity (SP), and the Brier score, each with 95% confidence intervals computed via a moving-block bootstrap (see Section 5.6). At the aggregate level, FLAD attains F1macro=98.31% (95% CI: [98.02,98.58]), PRmacro=98.31% ([98.05,98.58]), RCmacro=98.31% ([98.00,98.61]), SPmacro=99.16% ([98.99,99.30]), and a macro Brier score of 1.91% ([1.72,2.10]), with overall accuracy 98.31% ([98.02,98.56]). The reported counts correspond to temporally indexed episode windows concatenated across the held-out LOSO folds rather than to independent participants or independent experimental trials. Balanced class counts (Low =6667, Medium =6666, High =6667) therefore describe the nominal composition of the held-out episode set, whereas inferential uncertainty is quantified separately through the moving-block bootstrap and the effective sample size under temporal dependence. Class-wise estimates remain uniformly strong; for the High-risk class, for example, F1=98.44% ([98.09,98.77]) and SP=99.21% ([99.01,99.38]), indicating high sensitivity and low false-positive rates across classes. Because the study is a small pilot, these figures should be read as strong subject-held-out point estimates within the present cohort, not as conclusive evidence of broadly generalizable state-of-the-art performance. In particular, the independent test units are the 10 held-out participants in the outer LOSO folds; the much larger episode count does not remove the possibility of residual overfitting or optimistic generalization estimates at the participant level. No further participant-level test subjects were available within this pilot phase, so the reported episode count should not be read as if it upgraded the study to a large-sample validation. Instead, the present test results are intentionally framed as pilot-scale subject-held-out evidence only.

Fig. 1 summarizes the discriminative behavior of FLAD using one-vs.-rest curves with micro- and macro-aggregations. Fig. 1a shows ROC trajectories obtained by threshold-sweeping the per-class probabilities and Fig. 1b shows the corresponding PR curves. Areas are near ceiling, with AUROCmacro=0.998 and AUROCmicro=0.998, and AUPRCmacro=0.997 and AUPRCmicro=0.997, indicating uniformly low false-positive rates at high true-positive rates and high precision sustained under high recall across Low/Medium/High classes. Micro-averaging (prevalence-weighted) and macro-averaging (class-uniform) closely agree, consistent with balanced class prevalences. See Section 5.6 for formal definitions of ROC/PR construction and averaging schemes. For reporting consistency, all class-wise figures and tables in the revised manuscript follow the same class order (Low, Medium, High), the same naming of pooled summaries (micro, macro), and harmonized decimal precision; all values quoted in the running text were rechecked against the final aggregated outputs reported in Tables 2 and 3.

Table 3 contrasts the proposed ADPS (FLAD + LSTM predictors) with a set of matched calibrated comparators trained under identical feature inputs and subject-disjoint LOSO folds, with isotonic probability calibration fitted on validation and kept fixed at test time. To improve methodological comparability, all non-fuzzy baselines were assigned validation-only calibration and explicitly bounded tuning budgets; the corresponding search spaces and final settings are reported later with the baseline protocol in Section 5.7. In addition to Logistic Regression (LR), Gradient Boosting (GBM), Random Forest (RF), and a shallow MLP, the revised table now includes two broader deep-learning references: a compact Temporal Transformer and a MobileNetV3-LSTM baseline intended to represent, respectively, an attention-based sequence model and an edge-oriented deep architecture. Within this pilot evaluation, ADPS attains the strongest point estimates across all reported metrics, with F1macro=98.31%, AUROC=0.998, AUPRC=0.997, Accuracy=98.31%, Brier=1.91%, and ECE=1.20% (Table 3). Relative to the strongest non-fuzzy classical baseline (GBM), this corresponds to absolute gains of +0.70 percentage points in macro-F1 (98.31 vs. 97.61), +0.008 in AUROC (0.998 vs. 0.990), and +0.012 in AUPRC (0.997 vs. 0.985), together with lower Brier (1.91 vs. 2.31) and ECE (1.20 vs. 1.60). Relative to the added deep-learning comparators, the margin is substantially larger: compared with the Temporal Transformer, ADPS improves macro-F1 by +7.30 percentage points, AUROC by +0.073, and AUPRC by +0.083; compared with MobileNetV3-LSTM, the corresponding gains are +5.54 percentage points, +0.057, and +0.078, respectively. Although these differences support the value of the proposed architecture under matched pilot conditions, they should still be interpreted cautiously given the limited subject-held-out cohort and should not yet be read as definitive evidence of broad superiority across all modern deep sequence models.

Beyond aggregate discrimination, Table 3 also provides a direct reference point for the contribution of the fuzzy aggregation layer through the calibrated non-fuzzy MLP baseline, which uses the same inputs, subject-disjoint folds, and validation-fitted isotonic calibration. Relative to this non-fuzzy alternative, ADPS improves macro-F1 by +0.90 percentage points, AUROC by +0.009, and AUPRC by +0.015, while reducing the Brier score and ECE by 0.50 and 0.40 percentage points, respectively. This pattern suggests that the added value of FLAD is not purely conceptual: under matched data splits, calibration rules, and reporting metrics, the fuzzy decision layer is associated with both stronger ranking performance and better probability quality than a similarly calibrated non-fuzzy aggregator. At the same time, the inclusion of the Temporal Transformer and MobileNetV3-LSTM baselines broadens the interpretation of these results by showing that the advantage of ADPS is not confined to classical shallow learners alone. Within the present pilot setting, the proposed combination of short-horizon LSTM forecasting and interpretable fuzzy fusion remains more accurate and better calibrated than both the added attention-based sequence baseline and the edge-oriented deep baseline. To make the practical meaning of this improvement more concrete, Table 4 presents representative operator-facing rule traces for a true-positive and a false-positive High-risk alert. These examples show which rules dominate the alert, how cross-channel corroboration differs between stable and borderline alarms, and how the resulting explanation can guide operator response in practice.

Table 5 quantifies the marginal contribution of each input family via one-at-a-time ablations under an identical training protocol and probability calibration held fixed from validation to test. Let ΔM:=Mablated−Mfull for a metric M∈{F1macro,AUROC,AUPRC,Brier}. Negative Δ in discrimination metrics (Macro-F1, AUROC, AUPRC) indicates degradation, while positive Δ in Brier denotes poorer calibration (Brier deltas in percentage points, pp). The largest loss arises when removing the short-horizon sequential predictors (No LSTM predictors): ΔF1macro=−1.22 pp, ΔAUROC=−0.016, ΔAUPRC=−0.022, and ΔBrier=+1.00pp (Table 5). The second-largest drop is observed when suppressing the weapon-detection signal (No weapon cues): ΔF1macro=−0.74 pp, ΔAUROC=−0.010, ΔAUPRC=−0.014, and ΔBrier=+0.55pp (Table 5). Ablating affect-related predictors (No emotion-rate and No heart-rate) produces moderate but consistent degradations, whereas sector-risk prior and audio/noise yield smaller yet systematic gains primarily reflected in calibration. Overall, the monotone increase in Brier across all ablations indicates a deterioration in probabilistic calibration whenever any input family is removed, and the ranking of effects suggests prioritizing LSTM-based predictors and weapon cues in resource-constrained deployments (Table 5). For the No LSTM variant, temporal signals are replaced by leakage-free carry-forward (emotion-rate) and an exponentially weighted moving average (heart-rate) tuned on validation, preserving the evaluation protocol while isolating the value of short-term forecasting. A concordant drop is observed for the No forecasting control (L=1,H=0), confirming that the gains stem from true short-horizon look-ahead rather than static smoothing.

Per-module performance of the SCS detectors is provided in Table A1. We report Precision (PR), Recall (RC), and F1 as percentages with two-sided 95% confidence intervals computed via a moving-block bootstrap that preserves temporal dependence (see Section 5.6). The “Macro average” row is the unweighted mean across modules, i.e., Macro-M=1K∑k=1KMk for M∈{PR,RC,F1}. This table is particularly relevant for the visual branch: within the present acquisition geometry, the off-the-shelf YOLOv7 person detector achieves held-out Precision =95.7%, Recall =95.1%, and F1=95.4%, indicating that the chosen detector remains operationally adequate for pilot-stage person localization even if it is no longer the newest architecture in the literature.

At the rule level, a leave–one–rule–out (LORO) ablation with membership-sensitivity analysis indicates that no rule satisfies the pruning criteria (high overlap and negligible impact); see Table A2. For each rule r, we define the performance deltas as ΔMr:=Mablated(r)−Mfull,M∈{Macro-F1 (pp), Brier (pp)}.

We also define an overlap score Or as the maximum conditional co-activation with other rules. A rule is pruned if and only if Or≥τoverlap and |ΔMr|≤εM. The membership-sensitivity analysis perturbs the parameters of each membership function by ±10% and summarizes median(|ΔM|) to assess the local robustness of the rule base.

Together, these analyses provide a practical sufficiency check for the FLAD specification used in this pilot: the retained 12-rule base is compact enough to remain interpretable, yet no retained rule was simultaneously redundant and negligible under the stated pruning criterion.

Component-level impacts of the SCS detectors on FLAD are provided in Table A3. We evaluate counterfactuals by (i) zeroing each detector’s outputs and (ii) injecting ground-truth (GT), and summarize changes as ΔM:=Mcounterfactual−Mfull for M∈{Macro-F1 (pp), Brier (pp)}. Positive ΔBrier indicates worse calibration, while negative ΔMacro-F1 denotes degradation. Confidence intervals are paired moving-block bootstrap CIs (blocks aligned across conditions) to respect within-episode correlation.

Table 6 evaluates one-vs.-rest decisions for the High-risk class using the rule ⊮{P^(High∣x)≥τ} on the held-out test set. Any deployment threshold was selected exclusively on the calibrated validation partition within each outer fold, either by maximizing Fβ or by minimizing the linear cost C=CFN⋅FN+CFP⋅FP, and was then frozen before test scoring. For transparency, the table also reports operating characteristics at several pre-specified thresholds on the test set; these rows are descriptive and were not used to tune τ after observing test predictions. For N+=6667 High and N−=13,333 not-High episodes, the trade-offs follow the expected monotonic pattern: increasing τ from 0.40 to 0.70 raises Precision (97.56%→98.96%) and reduces the false-positive rate (1.24%→0.41%), at the expense of Recall (98.90%→97.20%) and with a slight drop in alert rate (33.8%→32.7%). A balanced setting at τ=0.50 yields Precision=98.43%, Recall=98.46%, F1=98.44%, with FPR=0.79% and an alert rate of 33.3% (Table 6). These results operationalize the ROC/PR summaries by mapping threshold-free ranking into actionable decisions; consistent with the deployment objective (CFN>CFP), high-sensitivity operating points are emphasized while maintaining low false-positive rates on the class-balanced pilot test set. However, because the held-out evaluation set is balanced by design, the resulting precision and alert-rate values do not directly transport to field settings in which High-risk events are rare. To address this limitation, Table 7 translates the selected operating point into scenario-based deployment quantities under assumed High-risk prevalences of 1%, 5%, and 10% on the deployed 0.5 Hz timeline (1800 decision points/h). Under these assumptions, the positive predictive value (PPV) decreases from 98.43% on the balanced test set to 55.73% at 1% prevalence, while the expected false-alert burden remains approximately 14.1 false alerts/h because it is driven mainly by the false-positive rate and the large volume of not-High windows. These deployment-oriented calculations should therefore be interpreted as scenario analyses rather than as empirical prevalence estimates from the pilot cohort.

The three-class confusion matrix for the held-out episode-level test set (N=20,000, aggregated across LOSO folds) is strongly diagonal, yielding an overall accuracy of 98.31%. For the Low class, recall is 98.37%, precision is 98.45%, and specificity is 99.23%, with most errors corresponding to Low→Medium (1.05% of Low) and fewer Low→High (0.58%). The Medium class exhibits recall of 98.11%, precision of 98.05%, and specificity of 99.03%, with symmetric confusions to adjacent classes (0.90% to Low and 0.99% to High). The High class attains recall of 98.46%, precision of 98.43%, and specificity of 99.21%, with minimal spillover to Low (0.64%) and Medium (0.90%). Off-diagonal entries are thus small and predominantly between adjacent risk levels, indicating balanced separability and uniformly low false-positive rates across classes.

Fig. 2 presents class-wise and pooled reliability assessments before and after isotonic probability calibration. Fig. 2a (Class Low) shows that post-calibration predictions align closely with the identity line across the full confidence range, indicating reduced over/under-confidence relative to the uncalibrated curve. Fig. 2b (Class Medium) exhibits the largest visual correction in the mid-probability region—after calibration, empirical accuracy tracks predicted confidence more tightly, reflecting lower expected and maximum calibration errors. Fig. 2c (Class High) demonstrates elimination of mild overconfidence at high scores, with improved agreement to the diagonal and a corresponding reduction in the Brier score. Finally, Fig. 2d (micro one-vs.-rest) aggregates decisions across classes (thus weighting by prevalence) and confirms that calibration gains persist at the pooled level, yielding well-calibrated probabilities suitable for threshold selection and cost-sensitive operation.

Table 8 summarizes exploratory evidence beyond the in-domain test split using 95% confidence intervals computed via a moving-block bootstrap (block length B≥τint to account for serial dependence). On the small external cohort (Next=15), the system attains macro-F1=98.60% (95% CI [97.90,99.10]), AUROC=0.993[0.989,0.996], AUPRC=0.996[0.992,0.998], Brier =2.30%[1.95,2.70], and ECE=1.10%. These values are directionally consistent with the in-domain reference (macro-F1=98.31%, AUROC=0.998, AUPRC=0.997, Brier =1.91%, ECE=1.20%), but they should be interpreted strictly as exploratory because the external sample is too small to support robust claims of transportability across sites or populations. For cross-dataset modules, the emotion-rate branch on AffectNet (N=12,480) yields macro-F1=90.40%, AUROC=0.948, AUPRC=0.952, Brier =7.10%, and ECE=2.40%. Because the heart-rate branch is regression-only with a continuous target, it is excluded from Table 8; we report its performance as RMSE=0.084 on WESAD (see Section 5). All external scores are produced with preprocessing and isotonic calibration learned on validation and kept fixed at test time to prevent leakage. See Table 8 for the complete panel and CIs.

Table A4 reports performance deltas relative to the in-domain test under four controlled shifts—low light/backlight, occlusion/pose, crowding, and elevated ambient noise—computed for macro-F1, AUROC, AUPRC, and the Brier score (in percentage points). The most adverse condition is occlusion/pose, with ΔF1macro=−1.6 pp, ΔAUROC=−0.008, ΔAUPRC=−0.009, and ΔBrier=+0.25 pp. Low light yields ΔF1macro=−1.2 pp, ΔAUROC=−0.006, ΔAUPRC=−0.007, and ΔBrier=+0.18 pp, whereas crowding produces ΔF1macro=−0.9 pp with smaller ranking/calibration effects (ΔAUROC=−0.004, ΔAUPRC=−0.005, ΔBrier=+0.12 pp). The high-noise condition exhibits the mildest degradation (ΔF1macro=−0.7 pp; ΔAUROC=−0.003; ΔAUPRC=−0.004; ΔBrier=+0.10 pp). Overall, AUROC/AUPRC decreases remain bounded (e.g., |ΔAUROC|≤0.008, |ΔAUPRC|≤0.009), indicating preserved threshold-free ranking quality, while Brier increases are modest, reflecting limited calibration drift. Confidence intervals are estimated via a moving-block bootstrap to respect serial dependence; where applicable, AUROC and paired-classification differences are assessed with DeLong’s and McNemar’s tests. These patterns collectively suggest graceful performance degradation under plausible covariate shifts, with the largest sensitivity arising from facial occlusions/pose.

On-device runtime on the Raspberry Pi 3B+ was profiled over the same N=20,000 held-out episode-level records used for test aggregation across LOSO folds. The system shows an end-to-end median of 1.25s (P95 =1.60s; range =0.70–1.80s), with latency dominated by person detection (YOLOv7; median 0.62s, P95 0.78s) and all other stages contributing ≤0.24 s at P95; see Table A5. Formally, for record i and stage j we measure Li,j (s) and define end-to-end time Ti=∑jLi,j. We report empirical quantiles p0.5(T⋅) (median) and p0.95(T⋅), and the range [miniTi,maxiTi]; stage-wise summaries are computed analogously for L⋅,j (see Section 5.6).

System sustainability and continuous-operation profiling.

To evaluate near-real-time feasibility beyond pointwise latency, we additionally profiled ADPS over a continuous 6 h sustained-load deployment on the Raspberry Pi 3B+ at the deployed fused decision rate of 0.5 Hz. Mean CPU utilization was 68.4% (±4.2%), with peaks reaching 89.2% during concurrent YOLOv7 inference and LSTM state updates. Under passive cooling, the SoC temperature stabilized at 64.2∘C with a peak of 68.5∘C, remaining below the nominal 80∘C throttling threshold and supporting stable execution without visible frequency-scaling degradation. Throughput remained consistent across the 6 h run, with a measured mean completion rate of 0.798 records/s and drift below 1.2%; the inter-arrival jitter for High-risk alerts remained bounded within ±140 ms. Using the nominal 44.4 Wh battery-pack specification as a deployment-planning proxy, the profiled workload corresponds to an estimated mean power draw of 5.18 W, a peak proxy of 6.45 W, and an expected continuous autonomy of approximately 8.57 h. These results indicate that ADPS maintains a sustainable resource footprint on affordable edge hardware while preserving positive timing slack of approximately 0.75 s per 2.0 s decision cycle, as summarized in Table 9.

The empirical distribution of T is right-skewed, with mass concentrated below 1.6s and rare long tails consistent with bursty compute and I/O; see Fig. A1. The complementary LBPS optimization traces used to assess convergence of the compact forecasters are reported in Fig. A2 and interpreted in detail in Section 5.4.2. Let F^T(t) denote the empirical CDF of {Ti}i=1N; the reported median and P95 correspond to the quantiles p0.5 and p0.95 of F^T, respectively.

This study indicates that the proposed aggression detection and prevention system (ADPS) achieves very strong episode-level discrimination within a small pilot, subject-disjoint evaluation setting while preserving probabilistic interpretability and practical deployability. To maintain a consistent reading of the paper, we interpret the results through the same five-part contribution logic introduced in Section 1 and visualized in Fig. 3: short-horizon forecasting, interpretable fuzzy aggregation, lightweight temporal modeling, multimodal robustness to missingness, and deployment-aware evaluation. On the held-out LOSO test folds, the system attains macro-F1≈98.3%, AUROC =0.998, and AUPRC =0.997, with low miscalibration (Brier ≈1.9%, ECE ≈1.2%). However, the reported 20,000 test records correspond to temporally indexed episodes aggregated across LOSO folds, not to 20,000 independent participants or independent experimental trials. Accordingly, the nominal record count should not be equated with the amount of statistically independent information when neighboring windows overlap. We therefore interpret these results jointly with the subject-held-out evaluation protocol, temporal guard gaps, fixed calibration mappings, and moving-block bootstrap confidence intervals that partially account for serial dependence.

A legitimate remaining concern is residual overfitting at the participant level. Although each outer LOSO fold evaluates FLAD on a previously unseen subject and therefore avoids direct subject leakage, only 10 independent participants were available to characterize between-subject heterogeneity. For that reason, the present evaluation should be interpreted as evidence that the model is promising under a carefully controlled pilot protocol, not as proof that the reported FLAD performance is free from overfitting or that the same error rates will hold in broader operational populations. Importantly, this limitation could not be remedied within the present revision by simply adding more subjects, because the study was completed as a pilot feasibility investigation with a fixed cohort. We therefore chose the more conservative and scientifically defensible path: to narrow the claim, make the participant-level unit of inference explicit, and state directly that broader between-subject validation must be established in a subsequent larger study rather than inferred from the current pilot alone.

The optimization behavior of the LSTM forecasters warrants the same caution. Rapid stabilization of validation loss and early stopping after relatively few effective epochs are compatible with the simple one-layer architecture and the short-horizon forecasting objective, but they do not by themselves demonstrate that the training signal is already rich enough for deployment-oriented evaluation. In a cohort of only 10 subjects, fast convergence may partly reflect limited heterogeneity in the training distribution rather than robust learning under broad operational variation. Accordingly, the present LBPS traces should be read as evidence of numerically stable pilot-scale optimization, not as evidence that data sufficiency has been achieved. The need for a larger training set is therefore scientifically clear, but it could not be addressed within the present revision because the data-collection phase had already been completed under the fixed pilot-study scope.

Beyond threshold-free ranking, calibrated probabilities are central to cost-sensitive operation. Reliability diagrams show that isotonic calibration fitted on validation and held fixed at test improves alignment to the identity line across classes and at the pooled (micro) level, reducing expected calibration error and the Brier score. This provides a sound basis for selecting operating points by minimizing linear cost C or maximizing Fβ according to deployment priorities. In concrete terms, sweeping the High-risk decision threshold from τ=0.40 to τ=0.70 tightens precision (97.56%→98.96%) and reduces false-positive rate (1.24%→0.41%) at a controlled expense in recall (98.90%→97.20%), with a balanced operating point at τ=0.50 (Precision =98.43%, Recall =98.46%, F1=98.44%, FPR=0.79%). These patterns align with applications that prioritize missed-high events (CFN>CFP), such as early-warning settings. At the same time, the balanced pilot design is optimistic with respect to deployment precision when High-risk events are rare. Using the selected operating point (τ=0.50) and transporting its sensitivity and false-positive rate to assumed High-risk prevalences of 1%, 5%, and 10%, the corresponding PPV becomes 55.73%, 86.77%, and 93.27%, respectively, while the expected false-alert burden remains approximately 14.1, 13.5, and 12.8 false alerts/h on the deployed 0.5 Hz timeline. Thus, the near-perfect discrimination observed under balanced held-out classes should not be interpreted as implying equally favorable alert precision in low-prevalence field environments. In practice, sustained deployment would likely require site-specific threshold selection, temporal alarm suppression or aggregation, and human-in-the-loop triage to keep operational burden acceptable.

Ablation analyses clarify the sources of performance. Under identical training, splits, and probability calibration, removing short-horizon sequential predictors (No LSTM) yields the largest degradation (ΔF1macro=−1.22 pp, ΔAUROC=−0.016, ΔAUPRC=−0.022, ΔBrier=+1.00 pp), indicating that look-ahead temporal structure captures predictive micro-dynamics that are not recoverable via leakage-free carry-forward or exponential smoothing. A concordant drop is observed for the No forecasting control (L=1,H=0), confirming that the gains stem from true short-horizon look-ahead rather than static smoothing. Suppressing weapon cues is the second most detrimental intervention (ΔF1macro=−0.74 pp, ΔAUROC=−0.010, ΔAUPRC=−0.014, ΔBrier=+0.55 pp), plausibly because these signals are directly tied to high-risk episodes. At the same time, this ablation should be interpreted with care: the visual branch measures only what is observable in the device’s forward field of view. Consequently, the estimated contribution of person- and weapon-related cues reflects the value of local camera-visible evidence within the present acquisition geometry, not guaranteed observability of the full crowd or threat configuration in unrestricted field settings. Emotion-rate and heart-rate branches add moderate but consistent gains, while priors and audio/noise features contribute smaller, calibration-skewed improvements. The monotone increase in Brier across all ablations indicates that each family not only improves ranking but also contributes to probability quality.

Component analyzes further support these conclusions. First, per-module performance within the sensing/computing stack (SCS) indicates high-quality detectors and estimators (module-wise PR/RC/F1 ≈95%–96%), which helps explain the separability observed at the fusion layer. Second, counterfactual tests that (i) zero out each detector or (ii) inject ground-truth (GT) into the pipeline quantify the directionality of influence: zeroing a component reduces macro-F1 and increases Brier, whereas injecting GT produces the opposite shifts, with the largest gains arising from the person detector and firearm cues. Taken together, these results provide the main empirical justification for retaining YOLOv7 in the present study despite its age: the person-detection task is comparatively mature, the detector performs strongly under the current forward-view pilot geometry, and its downstream contribution remains measurable when propagated through FLAD. Our claim is therefore not that YOLOv7 is the newest or universally best detector, but that it is sufficiently effective, reproducible, and edge-compatible for the specific proof-of-concept problem studied here. These findings are consistent with widely used, high-capacity person-detection backbones (e.g., YOLOv7) and classical, fast firearm detectors (Haar-like features) [13,14], and they motivate prioritizing these components in resource-constrained deployments. Nevertheless, the person detector should not be read as providing a panoramic or omnidirectional estimate of crowd size. In the current prototype, it summarizes only the locally visible sector covered by the camera, so its downstream influence on FLAD is best understood as view-conditioned scene evidence rather than a full situational census. Future work should benchmark newer detector families against YOLOv7 under the same field-of-view constraints, calibration protocol, and embedded-computing budget to determine whether the added architectural novelty produces a material application-level gain rather than only a nominal update in detector generation.

The interpretability contribution of FLAD should therefore be read at two complementary levels. Quantitatively, Table 3 shows that the fuzzy layer outperforms a similarly calibrated non-fuzzy MLP aggregator under matched inputs, folds, and validation-only calibration. Operationally, Table 4 shows how the same layer exposes auditable rule traces that can help an operator distinguish convergent multi-cue alarms from borderline alerts that primarily reflect transient or weakly corroborated evidence. This is important in safety-critical monitoring, where the practical value of an alert depends not only on its score but also on whether the rationale can be inspected, communicated, and triaged in real time.

An additional practical implication is that the camera-derived antecedents in FLAD are inherently view-limited. Because the forward-facing device does not observe the full surrounding crowd, rules involving People many/few or Weapons detected should be interpreted as operating on the currently visible sector only. In consequence, alerts dominated by camera evidence are best treated as decision-support prompts that should be cross-checked with temporal evolution, sector context, and, when feasible, secondary human verification, rather than as exhaustive summaries of the wider scene.

Evidence beyond the in-domain split remains preliminary. The exploratory external cohort provides only a limited signal that discrimination and calibration may transfer beyond the development setting, but with Next=15 it is not sufficient to support robust claims of generalization across sites or populations. Accordingly, the external results should be interpreted as feasibility-oriented rather than confirmatory. By contrast, the controlled distribution-shift analyses (low light/backlight, occlusion/pose, crowding, elevated ambient noise) are useful stress tests of internal robustness under predefined perturbations, but they do not substitute for large-scale external validation under organically occurring field heterogeneity. Within this limited scope, the observed OOD degradations remain modest and bounded, with occlusion/pose being the most adverse condition (e.g., ΔF1macro=−1.6 pp, ΔAUROC=−0.008, ΔAUPRC=−0.009, ΔBrier=+0.25 pp). These patterns highlight priorities for subsequent data collection and model refinement, including occlusion-aware training, broader site coverage, and prospectively acquired multi-session cohorts.

From a systems perspective, the Raspberry Pi 3B+ evaluation now supports a materially stronger pilot-stage deployment claim because it combines pointwise latency with a continuous 6 h sustained-load profile. In addition to median/P95 end-to-end latencies of approximately 1.25/1.60 s, the platform sustained mean CPU utilization of 68.4% (±4.2%) with peaks of 89.2%, mean SoC temperature of 64.2∘C (peak 68.5∘C), mean throughput of 0.798 records/s with drift below 1.2%, and bounded High-risk alert jitter within ±140 ms. Interpreted on the deployed 0.5 Hz schedule, these measurements indicate that the system remained below timing saturation and preserved positive slack throughout the sustained-load run. The nominal 44.4 Wh battery pack further implies a battery-based mean-power proxy of 5.18 W, a peak proxy of 6.45 W, and an autonomy window of approximately 8.57 h under the profiled workload. Taken together, these results substantially strengthen the near-real-time feasibility claim for pilot operation on Raspberry Pi hardware. At the same time, because the power estimate is derived from the nominal battery budget rather than from inline electrical telemetry, future systems work should still examine direct power measurement under broader ambient-temperature, duty-cycle, and multi-stream operating conditions. This connection between statistical performance and runtime feasibility is critical for embedded deployments where energy, thermal limits, and response times must be balanced.

Methodologically, we emphasize three choices that enhance internal validity and interpretability. First, the participant—not the episode window—was treated as the unit of independence for generalization assessment: each outer LOSO fold withheld one entire subject, while preprocessing, model selection, calibration, and threshold selection were all completed within the remaining subjects and then frozen before test scoring. Second, uncertainty is quantified via a moving-block bootstrap that respects temporal dependence, with block length tied to the estimated correlation time; this yields conservative, two-sided 95% confidence intervals. Third, between-model AUROC differences and paired classification outcomes were assessed with DeLong’s and McNemar’s tests, respectively; inference is reported via effect sizes and two-sided 95% confidence intervals, which were consistent with the observed ranking advantages of the proposed system over non-fuzzy baselines.

The present study is a pilot evaluation emphasizing careful protocol control and transparent reporting. External validation remains strictly exploratory (Next=15), and the OOD scenarios, though informative, are controlled perturbations rather than organically occurring shifts. Consequently, neither the small site-B cohort nor the synthetic stressors should be taken as sufficient evidence of robustness across institutions, populations, or operational contexts. The same caution applies to the target formulation itself: although the Low/Medium/High scale was defined a priori through an expert-informed operational codebook, it should presently be interpreted as a study-specific risk stratification for short-horizon security monitoring rather than as a universally standardized aggressiveness taxonomy. Broader scientific validity would benefit from future multi-expert consensus exercises and, where appropriate, alignment with formal security-escalation guidance used in comparable operational environments. Scaling to substantially larger, demographically diverse, multi-session, and multi-site cohorts would be necessary to estimate between-site variability, tighten uncertainty, and enable subgroup fairness analysis. Hardware-wise, the Raspberry Pi experiments provide a useful lower bound on embedded feasibility under the tested workload, but they should not be interpreted as a complete sustained-operation assessment. Continuous-operation stability, CPU load, and energy draw were not formally profiled and remain future work, together with additional platforms and dynamic scheduling policies (e.g., adaptive frame rates, detector gating) to optimize the latency–accuracy–power trade-off.

Prior efforts in surveillance and safety analytics typically report strong discriminative performance but rarely quantify calibration or cost-sensitive decision behavior. For instance, multimodal fusion for aggression/safety monitoring has reported accuracies in the 80%–90% range with limited treatment of probability calibration and thresholding [1]; violence detection via global motion and trajectory cues has achieved high accuracies on benchmark corpora without explicit ECE/Brier analysis [15]; and recent hybrid pipelines for physiological stress/risk modeling emphasize task-specific accuracy and throughput but only implicitly address decision costs [2]. In contrast, our system pairs near-ceiling AUROC/AUPRC with explicit calibration (Brier/ECE), threshold-sweep operating points, ablation-based attribution, and paired uncertainty quantification (moving-block bootstrap), while retaining interpretability through a fuzzy rule layer. At the component level, our SCS choices (YOLOv7 person detection and Haar-based firearm detection) align with widely adopted detectors [13,14], facilitating reproducibility and system-level transfer. Here it is important to distinguish detector recency from detector adequacy: YOLOv7 is not presented as the most current architecture, but as a mature and reproducible backbone whose held-out module performance and downstream impact are sufficient for the present proof-of-concept study. While protocol differences preclude head-to-head claims, the combination of discriminative performance, calibrated probabilities, uncertainty bands, and latency-compatible pilot-stage embedded profiling supports ADPS as a proof-of-concept early-warning prototype where both ranking and probability quality matter. Broader deployment claims should remain provisional until validated on larger, multi-session, and multi-site cohorts and complemented by formal sustained-load systems profiling.

Because this application domain is sensitive, the key ethical issue is not only how data were collected, but also how system outputs could be interpreted and potentially misused in practice. The formal approval, consent, and de-identification procedures are described in Section 5.1; here, we emphasize their implications. Even with ethics oversight, anonymized processing, and restricted data access, a pilot system such as ADPS should not be framed as an autonomous basis for punitive, disciplinary, or liberty-restricting decisions. Rather, its appropriate role is that of a human-in-the-loop early-warning aid whose outputs require contextual interpretation, proportionality, and professional oversight. This caution is particularly important because the present cohort is narrow in demographic and situational scope, and therefore does not support broad claims of contextual, demographic, or institutional generalizability. In high-stakes settings, the main ethical safeguard is not only privacy protection, but also explicit limitation of use: the system should assist human judgment under governance controls, not replace it

The limits of demographic and contextual generalizability are equally important. Our pilot cohort comprised adult male police officers from a single institutional setting; therefore, findings should not be generalized to female officers, civilians, other age groups, or different operational and cultural contexts. We make no subgroup fairness claims, and the controlled OOD scenarios do not substitute for naturally occurring heterogeneity in real deployments. A larger, prospectively governed, multi-site, mixed-sex study would be required to assess fairness, calibration stability, and policy robustness across sex, age, site, and context.

This study developed and pilot-tested the Aggression Detection–Prediction System (ADPS), a mobile expert system that integrates lightweight on-body and ambient signals through a hybrid architecture of Long Short-Term Memory (LSTM) networks and an interpretable fuzzy logic decision layer. Within subject-disjoint in-domain evaluation, the proposed model achieved macro-F1=98.3% and AUROC =0.998, showing higher pilot-scale point estimates than the calibrated non-fuzzy baselines while maintaining low miscalibration (ECE =1.20%). The primary contribution of this work lies in a unified five-part design logic: short-horizon temporal modeling, transparent fuzzy aggregation, lightweight sequence modeling, multimodal robustness to missingness, and deployment-oriented pilot evaluation. These points are intended to be read consistently across the manuscript as one coherent contribution thread rather than as isolated claims. Together, they provide auditable and computationally feasible pilot-stage aggression forecasting. In particular, the revised analysis now makes the contribution of the fuzzy layer more concrete by pairing the matched comparison against a calibrated non-fuzzy MLP with operator-facing rule traces that show how true-positive and false-positive High-risk alerts can be interpreted in practice. In addition, the revised Methods now make the FLAD construction itself more explicit by clarifying how the 12-rule base was built from inference-time variables and operational escalation patterns, how continuous cues were fuzzified into overlapping linguistic memberships, how the bounded-product t-norm was applied during antecedent aggregation, and how the final compact rule set was checked for sufficiency through leave–one–rule–out and membership-sensitivity analyses. At the same time, the present prototype should not be understood as observing the full crowd environment: the person-count and weapon cues are extracted from a forward-facing camera and therefore summarize only the locally visible sector. Their contribution to FLAD is useful, but inherently partial, and broader scene understanding would require wider-angle, multi-camera, or otherwise complementary sensing. Likewise, the choice of YOLOv7 for person detection should be interpreted as a pragmatic pilot-stage engineering decision rather than as a claim that this detector is the newest available architecture. In the present study, its role is that of a reproducible, off-the-shelf, edge-compatible backbone whose adequacy is supported by the held-out module-level results and by the downstream counterfactual analyses. Furthermore, deployment on a Raspberry Pi 3B+ now indicates latency-compatible and sustained-load pilot-stage feasibility under the tested workload, supported by a continuous 6 h profile with mean CPU utilization of 68.4%, mean throughput of 0.798 records/s, bounded thermal load, and a battery-based mean-power proxy of 5.18 W. At the same time, the external cohort remains small and strictly exploratory; therefore, those findings should be viewed only as a preliminary signal rather than as sufficient evidence of robustness across sites or populations. The class-balanced pilot design is also more favorable than realistic field prevalence for High-risk events; accordingly, deployment interpretation should rely not only on balanced-set discrimination but also on prevalence-adjusted alert precision and false-alert burden. Overall, the present results support proof-of-concept feasibility, but the performance estimates should still be interpreted cautiously given the pilot scale, and broader claims of external generalization or deployment robustness should remain provisional until confirmed in substantially larger, multi-session, and multi-site cohorts, ideally under naturally imbalanced event rates and prospectively specified alarm-management policies. Because only 10 independent participants contributed the subject-held-out test folds, residual overfitting at the participant level cannot yet be excluded and should be treated as an explicit limitation of the present FLAD evaluation. Because this article reports the completed pilot dataset, that limitation could not be eliminated within the current revision by adding new subject-level tests. The appropriate corrective action is therefore transparent claim-bounding and explicit acknowledgment of uncertainty, with confirmation deferred to a later, larger validation campaign. In addition, the revised systems section now reports a continuous-operation profile that includes CPU load, thermal behavior, throughput stability, alert jitter, and a battery-based power/autonomy proxy. These measurements substantially strengthen the deployment argument beyond latency alone, although direct inline electrical telemetry and testing under harsher ambient or multi-stream conditions remain important targets for future systems evaluation. Given the sensitivity of the application domain, future deployment-oriented studies should pair technical validation with explicit governance, privacy-preserving data handling, and subgroup fairness audits. Future revisions should also strengthen the external validity of the target definition itself by testing whether the present Low/Medium/High codebook remains stable under broader multi-expert review and across institutions, scenarios, and security cultures. In that sense, the current aggressiveness scale should be viewed as an explicit pilot operationalization of short-horizon escalation risk, not as a final universal standard. Rapid LBPS convergence in this pilot should likewise be interpreted cautiously: because the compact forecasters often stabilized after relatively few effective epochs, substantially larger and more heterogeneous training datasets will be needed to verify that this behavior reflects learnable short-horizon structure rather than limited pilot-scale variability. Because no further subject-level sequences were available within this completed pilot, the appropriate present remedy is explicit claim-bounding rather than retrospective expansion of the training set within the current revision. Future research will focus on multi-site validation and the exploration of long-term human-system interaction to further enhance the generalizability and ethical deployment of the ADPS framework.