Integrated system and method for multi-modal distress verification and alert error mitigation using wearable and robotic data fusion

Abstract

An integrated cyber-physical system and method for high-fidelity distress monitoring and automated alert error mitigation. The system comprises a wearable multi-parametric sensing suite (100) for primary anomaly detection and an autonomous mobile robotic agent (200) for secondary contextual verification. Upon detection of a potential distress event (S110), the robotic agent (200) executes a priority undocking sequence (S120) from a charging station (500) to perform multi¬ modal acquisition (S130), including 3D skeletal pose estimation and acoustic sentiment analysis. A Cloud Processing Engine (300) synthesizes these data streams — including optional spatial occupancy data from an environmental sensor network (400) — to calculate a Validation Confidence Score (VCS) (S140) using a dynamic weighted fusion algorithm. This score enables the system to autonomously perform alert de-escalation (S150) for verified safe-states or execute prioritized escalation protocols (S160) to a caregiver interface (600) and an emergency services gateway (700). The invention effectively reduces alarm fatigue and improves emergency response reliability by providing contextual validation of biometric triggers.

Description

1 . TITLE OF THE INVENTIONINTEGRATED SYSTEM AND METHOD FOR MULTI-MODALDISTRESS VERIFICATION AND ALERT ERROR MITIGATION USING WEARABLE AND ROBOTIC DATA FUSION2. CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 750,797, filed on January 29, 2025, the contents of which are incorporated herein by reference in their entirety.3. TECHNICAL FIELD

[0002] The present invention relates to integrated healthcare monitoring systems, and more specifically to a cooperative architecture between wearable multi-parametric sensors and autonomous mobile robotics designed for high-fidelity distress verification and automated alert error mitigation.4. BACKGROUND ART

[0003] Conventional monitoring systems often result in erroneous alerts, including false positives or false negatives, due to reliance on isolated data sources. Such errors lead to alarm fatigue and inefficient emergency response. There is a technical need for a system that provides contextual verification of biometric data via mobile robotic observation to improve alert reliability and mitigate diagnostic errors.

[0004] The Technical Failure of Isolated Sensors: In the current landscape of geriatric care and remote patient monitoring, wearable devices (e.g., smartwatches, patches) have become ubiquitous. However, these devices suffer from a fundamental technical limitation: they operate in a contextual vacuum. An accelerometer can detect a rapid change in velocity, but it cannot determine if that change resulted from a lifethreatening fall or from the user simply dropping the device on a hard surface.

[0005] The Economic and Human Cost of False Positives: Statistical data indicates that up to 80-90% of automated alerts are "false positives." This leads to a critical phenomenon known as "Alarm Fatigue" among medical personnel and family members. When caregivers are bombarded with erroneous alerts, their response time to genuine emergencies increases significantly, creating a dangerous safety gap.

[0006] Limitations of Existing Static Solutions: Existing attempts to solve this problem have relied on static environmental sensors, such as wall-mounted cameras. These solutions are plagued by "Blind Spots" — areas where the sensor's line-of-sight is obstructed by furniture or walls. Furthermore, static systems are invasive, requiring a dense and expensive infrastructure to be installed in every room of a residence.

[0007] The Unmet Need: There is a long-standing technical requirement for a system that can provide Mobile, Proactive, and Multi-Modal Verification. A system that does not just "detect" an event, but "validates" it through an independent observation layer that can eliminate blind spots while maintaining user privacy and reducing the burden on human responders.5. SUMMARY OF THE INVENTION

[0008] Technical Innovation: The present invention addresses the aforementioned deficiencies by introducing a cooperative sensing architecture. Unlike prior art, which relies on a single point of failure, the current invention utilizes a Verification-Loop between a primary wearable sensor and a secondary mobile robotic agent.

[0009] The Core Solution: The system is characterized by its ability to generate a Validation Confidence Score (VCS). This score is not a simple binary trigger but a dynamic, weighted probability index. The innovation lies in the system's capacity for Automated Alert De-escalation. By deploying a robotic agent to perform a localized "close-up" inspection using 3D pose estimation and natural language queries, the system can autonomously cancel an alert if a "Safe-State" is verified.

[0010] Strategic Advantages: This approach provides three primary technical benefits: (1 ) it eliminates blind spots through robotic mobility; (2) it preserves privacy by activating visual sensors only upon a primary trigger; and (3) it optimizes emergency resource allocation by filtering out noise at the source.6. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG. 1 is a block diagram illustrating the distributed system architecture, detailing the bi-directional connectivity between the Sensing Suite (100), the Cloud Processing Engine (300), the Robotic Agent (200), the Charging Station (500), the Administrative / Caregiver Interface (600), and the Emergency Services Gateway (700).

[0012] FIG. 2 is a logic flow diagram illustrating the multi-stage validation process and the calculation of the Validation Confidence Score (VCS).7. DETAILED DESCRIPTION OF THE INVENTION

[0013] System Architecture and Interconnectivity (FIG. 1): The system architecture, as illustrated in FIG. 1 , comprises a distributed network of heterogeneous sensing and processing nodes designed to provide a closed-loop verification of distress events. The primary acquisition node is the Sensing Suite (100), typically embodied as a wearable device. This device is in continuous or semi- continuous communication with the Cloud Processing Engine (300) and the Robotic Agent (200) via high-speed wireless protocols such as Bluetooth Low Energy (BLE), Wi-Fi, or cellular backhaul (LTE / 5G). The Charging Station (500) acts as a local anchor for the robotic agent, ensuring power readiness and acting as a secondary communication relay.

[0014] The Sensing Suite (100) and Multi-Modal Physiological Telemetry

[0015] The wearable device (100) comprises a multi-parametric sensor array configured for the continuous or semi-continuous acquisition of physiological and kinetic data. In a preferred but non-limiting embodiment, the sensor suite includes aPhotoplethysmogram (PPG) sensor for cardiac rhythm analysis and a Pulse Oximeter for oxygen saturation (SpO2) monitoring.

[0016] Furthermore, the sensing suite (100) may incorporate, without limitation, a plurality of additional sensors such as:• Electrodermal Activity (EDA / GSR) sensors for monitoring skin conductance and stress levels.• Electrocardiogram (ECG) electrodes for high-fidelity heart rate variability (HRV) analysis.• Body temperature sensors (Thermopiles) for detecting febrile states or hypothermia.• Blood Pressure (BP) estimation modules utilizing oscillometric or PTT- based (Pulse Transit Time) techniques.• Acoustic sensors (Microphones) for detecting respiratory patterns, coughing, or vocal distress signals.

[0017] For kinetic analysis, the device (100) comprises an Inertial Measurement Unit (IMU), which may consist of a 3-axis accelerometer, a 3-axis gyroscope, and optionally a magnetometer. The IMU samples kinetic data at high frequencies (e.g., 200Hz to 500Hz) to detect complex signatures of falls, tremors, or prolonged immobility. The device further includes an integrated digital signal processor (DSP) or microcontroller unit (MCU) executing advanced filtering algorithms to isolate biometric signals from motion-induced artifacts, thereby ensuring that the data transmitted to the Cloud Processing Engine (300) is of high diagnostic and verification quality

[0018] The Robotic Agent (200) as a Mobile Sensing Node: The Robotic Agent (200) is an autonomous mobile platform capable of Simultaneous Localization and Mapping (SLAM). Upon receiving a "Verification Trigger" from the Cloud Engine (300) or directly from the wearable (100), the robot (200) calculates an optimal trajectory to the user's location. The robot's payload includes a 3D depth camera for skeletal landmark extraction and a beamforming microphone array. Crucially, the robot (200) performs Edge Computing; it processes visual data locally to determine the user'spose (e.g., lying on the floor vs. sitting) without transmitting raw video, thereby preserving the subject’s privacy while providing objective verification.

[0019] The Administrative and Caregiver Interface (600): Referring to FIG. 1 , the system includes an Administrative Interface (600). This component serves as the human-in-the-loop terminal. It is configured to display real-time telemetry, the current calculated Validation Confidence Score (VCS), and the robot's visual confirmation data. The interface (600) allows caregivers to receive pre-validated alerts, significantly reducing the cognitive load and "alarm fatigue" associated with traditional systems. Caregivers can use the interface (600) to communicate through the robot (200) via a two-way audio-video link, further humanizing the verification process.

[0020] Integration with Emergency Services (700): The system is further integrated with an Emergency Services Gateway (700). When the Cloud Processing Engine (300) determines that the VCS has exceeded a critical safety threshold (e.g., 0.85), it automatically initiates an escalation protocol. The system transmits a Comprehensive Emergency Packet to the gateway (700). This packet includes the subject’s precise location, the specific type of verified distress (e.g., "Validated Fall - Unresponsive"), and a summary of recent physiological anomalies. This ensures that first responders (700) are dispatched with actionable intelligence, rather than just a binary alarm.

[0021] Detailed Logic Flow and VCS Calculation (FIG. 2): The operational logic is detailed in FIG. 2. The process starts at S100 with continuous monitoring. At S110, an initial anomaly is detected by the wearable (100). At S120, the robotic agent (200) executes a Priority Undocking Sequence from the charging station (500) and navigates to the subject. At S130, multi-modal acquisition begins, where the robot captures visual and acoustic data.

[0022] At step S140, the system calculates the Validation Confidence Score (VCS) using the dynamic fusion formula:VCS = w1(Dp)+ w2(Dtrv])+ w3(l+ w4(D{e])Where:• Dp(Primary Data): The raw signal strength and profile from the wearable or environmental sensors.• Drv(Robotic Visual): Results from the 3D Pose Estimation algorithm (identifying skeletal landmarks).• Dra(Robotic Acoustic / NLU): Data from the voice and sound analysis.• De(Environmental): Spatial occupancy and motion data from the environmental sensor network.And: w15w2, w3, w4Dynamic weights adjusted based on sensor reliability and environmental factors. For example, in high-noise or low-light conditions, w2or w3are reduced whileand w4are increased to maintain verification fidelity.This formula is provided as a non-limiting example of how data may be synthesized; the VCS is utilized exclusively as an alert management metric for reducing false-positive triggers and does not serve as a clinical diagnostic tool.

[0023] Automated De-escalation and Escalation (S150-S160): At S145, the VCS is evaluated against a dynamic threshold. If the robot (200) verifies that the user is in a "Safe-State" (e.g., the user is observed standing and provides a coherent verbal confirmation), the system performs an Automated Alert De-escalation (S150). This cancels the alarm before it reaches emergency services, preventing unnecessary resource deployment. Conversely, if the VCS is high, the system proceeds to Escalation (S160), notifying both the Caregiver Interface (600) and Emergency Services (700).

Claims

8. CLAIMS[0024] 1. A system for mitigating errors in distress alerts, comprising:• A wearable device (100) comprising at least one physiological sensor and at least one motion sensor for initial detection of anomalies;• An autonomous mobile robotic assistant (200) for capturing contextual data; and• A Cloud Processing Engine (300) configured to calculate a Validation Confidence Score (VCS) by synthesizing data from said wearable device (100) and said robotic assistant (200) to autonomously suppress or escalate alerts.

2. The system of claim 1 , wherein the at least one physiological sensor is selected from a group comprising: a photoplethysmogram (PPG) sensor, an electrocardiogram (ECG) sensor, a pulse oximeter, a skin conductance sensor, a body temperature sensor, and a blood pressure estimation module.

3. The system of claim 1 , wherein the robotic assistant (200) executes an automated priority undocking sequence from a charging station (500) responsive to the initial detection.

4. The system of claim 1 , wherein the Cloud Processing Engine (300) performs automated alert de-escalation based on a validated "Safe-State" derived from robotic visual and acoustic verification.

5. The system of claim 1 , further comprising an environmental sensor network (400) comprising at least one of an Ultra-Wideband (UWB) radar, a pressuresensitive surface, or a static motion detector, wherein the Cloud Processing Engine (300) incorporates spatial data from said network as a variable in the VCS calculation.

6. The system of claim 1 , further comprising an emergency services gateway (700), wherein the Cloud Processing Engine (300) is configured to transmit a comprehensive emergency packet to said gateway upon a VCS validation of distress.28 7. The system of claim 1 , wherein the VCS functions exclusively as an alert29 management metric to minimize alarm fatigue and does not independently30 determine a medical state.

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