Multimodal data fusion combining eye-tracking, behavioral logs, interaction patterns, and physiological signals

1. Use a unified timestamp format (Unix time) across all files. 2. Write a Python script using Pandas to align all data streams to a common time axis, using the stimulus presentation onset (e.g., a screen change recorded in all logs) as a synchronization anchor. 3. Filter and interpolate physiological data (e.g., apply a bandpass filter to ECG) to match the sampling rate of eye-tracking data. 4. Output a single merged DataFrame with columns for all modalities, ready for analysis.

1. Design a lab study with 15 participants using the checkout flow while recording eye-tracking, EDA, and UI logs. 2. Fuse the data to identify the problematic page: Look for convergence of high fixation duration on form fields, increased EDA spikes (arousal), and frequent correction/deletion keystrokes in the interaction log. 3. Calculate a composite 'stress index' by z-scoring and weighting these aligned metrics. 4. Present findings with synchronized video playback showing the user's gaze path, physiological state, and actions, pinpointing the exact moment and element causing the bottleneck.

1. Architect a system with a low-latency stream processor (e.g., Apache Kafka/Faust) ingesting real-time data from an eye-tracker, mouse/keyboard logger, and a wearable sensor. 2. Implement a lightweight classification model (e.g., a pre-trained LSTM) that fuses feature vectors from all streams to classify state (e.g., 'Focused,' 'Frustrated,' 'Confused'). 3. Create an API endpoint that the UI application can poll for the current state classification. 4. Program the UI to respond: if state='Frustrated', it could simplify the interface, highlight a help button, or offer a hint.