Description:DETAILED DESCRIPTION
In a preferred embodiment of the present invention, the hardware architecture is bifurcated into mobile end-nodes and stationary infrastructure. The mobile end-nodes include wearable sensors, such as smart bands or specialized ID badges, equipped with tri-axial accelerometers, gyroscopes, and photoplethysmogram (PPG) sensors to capture micro-movements, heart rate variability (HRV), and postural alignment. Simultaneously, student-owned personal devices, including smartphones and laptops, execute a secure background application that monitors device interaction metrics, such as screen-on duration, application focus, and typing cadences, which serve as proxies for digital engagement. The stationary infrastructure comprises a plurality of environmental sensor nodes distributed throughout the classroom to monitor carbon dioxide levels, ambient noise, illumination, and thermal conditions.
The data processing pipeline commences when a student enters the classroom, triggering an automatic attendance event via short-range wireless authentication, such as Bluetooth Low Energy (BLE) proximity detection or NFC validation. Once a session is established, the classroom edge gateway—a dedicated local server—aggregates the various sensor streams via high-speed local wireless protocols. This gateway performs intensive pre-processing, including noise filtering of inertial data and the extraction of HRV indices from PPG signals. The heart of the embodiment lies in the multi-modal AI inference engine, which employs advanced machine learning architectures, such as temporal convolutional networks or transformers, to analyze 30-to-60-second windows of fused data. For instance, the system may identify a "high fatigue" state by correlating a specific increase in postural deviation and micro-movement entropy with rising CO2 concentrations and declining HRV.
Following the inference of a state, the edge gateway executes a real-time feedback logic. For the instructor, this manifests as an anonymized dashboard display indicating group-level engagement trends and recommending pedagogical shifts, such as moving from a lecture format to a collaborative activity when engagement thresholds are breached. For the building infrastructure, the gateway transmits control signals via the institutional network to adjust ventilation rates or lighting intensity to counteract environmental factors contributing to student lethargy. At the campus-wide level, the system transmits pseudonymized session summaries to a central cloud server, enabling administrators to conduct longitudinal studies on the correlation between physical environment, course scheduling, and academic performance, thereby facilitating evidence-based institutional management.
Advantages of the Invention
The present invention provides a superior technical solution by enabling a holistic and multi-dimensional view of the classroom environment that single-modal systems cannot achieve. By utilizing multi-modal data fusion, the AI inference is significantly more robust against individual variability and environmental noise. The edge-centric architecture ensures that high-frequency physiological data is processed with minimal latency while simultaneously addressing the stringent privacy requirements of educational institutions. Furthermore, the transition from passive data logging to active, real-time environmental and pedagogical adaptation directly enhances the immediate learning outcomes and long-term health of students.
, Claims:CLAIMS:
We Claim:
1. A smart classroom system comprising a plurality of wearable and personal devices associated with students, a plurality of environmental sensor nodes within a classroom, and at least one edge gateway configured to receive sensor data and determine student attendance, engagement level, and fatigue state.
2. The system of claim 1, wherein the edge gateway executes a multi-modal artificial intelligence model that fuses physiological indicators, movement patterns, device interaction behavior, and environmental parameters to infer engagement and fatigue for individual students and for the classroom collectively.
3. The system of claim 1, wherein the edge gateway generates real-time recommendations to an instructor or building management system, including suggestions for micro-breaks, activity changes, or environmental adjustments when engagement falls below or fatigue rises above a threshold.
4. The system of claim 1, wherein attendance is automatically recorded based on authenticated presence of wearable or personal devices for a configurable minimum duration, and stored together with session-level engagement and environmental summaries.
5. The system of claim 1, wherein privacy is preserved by performing feature extraction and inference on the edge gateway, anonymizing identifiers before transmission to a central server, and optionally employing federated learning to aggregate model parameters instead of raw data.
6. A smart campus framework comprising a plurality of smart classroom systems according to any of claims 1 to 5, and a central analytics server configured to compute campus-wide trends in attendance, engagement, fatigue, and environmental quality.