Israel Goytom

Yisak Debele, Israel Goytom, Anwar Misbah

Artificial intelligence systems that model and support human cognition require reliable measures of cognitive state. We present the Focus Performance Score (FPS) from the Pulse Focus mobile Stroop application and evaluate whether it measures attentional control during color-word conflict resolution. We conduct behavioral, neural, and formula validation analyses. Behavioral results (N=466, 111,133 trials) show that FPS captures the Stroop interference effect, tracks individual differences in attentional control, and demonstrates strong test-retest reliability. Neural validation using the DMCC55B fMRI dataset (N=55) shows that the primary FPS component, mean incongruent reaction time, is significantly associated with anterior cingulate cortex activation, a key neural substrate of conflict monitoring. Formula validation identifies and resolves structural redundancy within the scoring framework and provides convergent support for the weighting design. Together, these findings establish FPS as a behaviorally valid, reliable, and neurally grounded measure of attentional control. FPS provides a defensible behavioral signal for evaluating human attentional state and supports future work on attention-aware human-AI interaction and physiological state modeling.

Henok Ademtew, Israel Goytom

Human-computer interaction increasingly demands systems that recognize not only explicit user inputs but also implicit emotional states. While substantial progress has been made in affective computing, most emotion recognition systems rely on cloud-based inference, introducing privacy vulnerabilities and latency constraints unsuitable for real-time applications. This work presents a comprehensive evaluation of machine learning architectures for on-device emotion recognition from wrist-based photoplethysmography (PPG), systematically comparing different models spanning classical ensemble methods, deep neural networks, and transformers on the WESAD stress detection dataset. Results demonstrate that classical ensemble methods substantially outperform deep learning on small physiological datasets, with ExtraTrees achieving F1 = 0.826 on combined features and F1 = 0.623 on wrist-only features, compared to transformers achieving only F1 = 0.509-0.577. We deploy the wrist-only ExtraTrees model optimized via ONNX conversion, achieving a 4.08 MB footprint, 0.05 ms inference latency, and 152x speedup over the original implementation. Furthermore, ONNX optimization yields a 30.5% average storage reduction and 40.1x inference speedup, highlighting the feasibility of privacy-preserving on-device emotion recognition for real-world wearables.