A system for detecting, evaluating, and adaptively responding to a user's emotional and physiological states during ongoing interaction, comprising:
- a) two or more user devices operating in coordination, selected from the group consisting of a smartwatch, smartphone, tablet, and laptop, wherein at least one of the two or more user devices is a smartwatch configured to capture biometric data, and at least one other of the two or more user devices is a smartphone, tablet, or laptop configured to capture behavioral, visual, or auditory data;
- b) a multimodal sensor suite configured to capture signals across five modalities:
- I. Biometric/Physiological:
- including heart rate variability (HRV), galvanic skin response (GSR), respiratory rate, blood oxygen saturation (SpO2), skin temperature, blood glucose level, and sleep cycle data;
- II. Behavioral:
- including keystroke dynamics, typing rhythm, application usage patterns, scrolling or clicking behavior, and physical movement patterns;
- III. Visual:
- including facial expressions, micro-expressions, gaze tracking, blink rate, and facial muscular tension via integrated or peripheral cameras;
- IV. Auditory/Voice:
- including vocal tone, pitch, cadence, pauses, tremors, and breathing irregularities via one or more microphones;
- V. Contextual/Environmental:
- including time of day, geolocation, ambient light and sound levels, device usage frequency, calendar events, and interaction context;
- c) a signal processing and fusion engine configured to normalize, denoise, temporally align, reconcile redundant signals, and synthesize the captured inputs into a unified physiological state vector;
- d) an emotion and health classification engine comprising one or more machine learning models selected from convolutional neural networks (CNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs), and transformer-based architectures, the engine configured to infer user states from the synthesized vector;
- e) a contextual adaptive intervention engine configured to deploy one or more adaptive actions based on the inferred state using reinforcement learning or rule-based logic;
- f) a monitoring module configured to collect post-intervention signals, including physiological recovery indicators and behavioral patterns;
- g) a feedback evaluation engine configured to assess intervention effectiveness using recovery signals, heuristic patterns, and user-specific thresholds, and to adapt intervention strategies based on real-time physiological and behavioral feedback;
- h) a personalized recommendation and orchestration module configured to use contextual bandits, reinforcement learning models, and ranking frameworks to interpret the user's physiological and behavioral state, contextual inputs, and user embeddings, and to autonomously trigger next-best actions across the smartwatch, smartphone, tablet, and laptop with or without explicit user commands;
- i) a federated refinement engine configured to perform on-device model training and transmit encrypted model weight updates to a federated aggregation server, where updates from multiple devices are merged and redistributed as a global model, without transmitting raw sensor data;
- j) a longitudinal reporting and insight module configured to generate dashboards and natural language summaries, including trend visualizations of emotional and physiological states, intervention outcomes, and resilience metrics, optionally via large language models (LLMs);
- wherein the two or more user devices operate in coordination to simultaneously capture input signals across distinct sensing modalities during ongoing user interaction, and wherein the system operates as a continuous closed-loop architecture in which each executed adaptive action shapes subsequent input signals monitored across the two or more user devices, enabling ongoing adaptation of emotional state classifications and system responses without requiring explicit user input.