Wearable Sensor System for Pre-Meltdown Detection and Auditory Intervention
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Solution Overview
Problem
Individuals with autism spectrum disorder (ASD) often experience meltdowns triggered by stress, social demands, and sensory overload, which are challenging to predict and manage due to variability in pre-meltdown behaviors and the need for caregiver recognition.
Innovation Solution
A method utilizing wearable sensors to acquire motion, sound, and physiological data, comparing it to target data, and delivering audible sound therapy with a familiar audio track to prevent or reduce meltdown incidence and severity, employing machine learning algorithms for real-time prediction and intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional interventions (removing from stressful environment, redirecting to routine activity) are used, then meltdown intensity is reduced, but the intervention requires caretaker recognition of rumbling stage behaviors which are often minor and inadvertently missed
Solution Approach 1:
The patent replaces the mechanical/cognitive system of caretaker observation and recognition with an automated sensor-based detection system. Wearable sensors continuously monitor physiological parameters (heart rate, skin conductance, temperature) and motion patterns to objectively detect pre-meltdown states, eliminating reliance on caretaker recognition of subtle behavioral cues.
Solution Approach 2:
The patent introduces wearable sensors and a processing system as intermediaries between the subject's physiological state and the intervention delivery. These intermediaries continuously monitor and analyze data, providing early detection of rumbling stage behaviors before they become apparent to caretakers, enabling timely intervention.
2Loss of time
If continuous monitoring of sensor data is implemented to detect pre-meltdown stages, then early intervention is enabled, but system complexity and data processing requirements increase
Solution Approach 1:
The patent establishes predetermined thresholds for physiological parameters and motion patterns that indicate pre-meltdown states. The system is pre-configured with these criteria, allowing it to automatically compare real-time sensor data against these thresholds and trigger interventions without requiring complex real-time analysis or decision-making processes.
Solution Approach 2:
The system performs automated self-monitoring and self-intervention. The wearable device continuously collects sensor data, compares it against predetermined thresholds, and automatically delivers the familiar audio sound track when thresholds are exceeded, eliminating the need for external monitoring and manual intervention initiation.
3Reliability
If familiar audio sound track is repeated continuously until target data is exceeded, then meltdown severity is decreased, but energy consumption and intervention duration increase
Solution Approach 1:
The system continuously monitors sensor data during audio track delivery and compares it against target thresholds. The familiar audio sound track is automatically adjusted or stopped when the subject's physiological parameters return to target ranges, creating a closed-loop feedback system that optimizes intervention duration and energy consumption while maintaining effectiveness.
Data Source
AI summary
A method comprising (a) acquiring sensor data from wearable sensors worn by a subject, wherein the sensor data comprise motion, sound, and/or physiological data; (b) comparing the sensor data with target data; (c) determining: that the motion data of the subject is equal to or exceed the target motion data; that the sound data of the subject is equal to or exceed the target sound data; and/or that the physiological data of the subject is equal to or exceed the target physiological data; and (d) responsive to step (c), delivering audible sound therapy to the subject, wherein the audible sound therapy comprises a familiar audio sound track which is repeated at least until it is determined: that the target motion data exceed the motion data; that the target sound data exceed the sound data; and/or that the target physiological data exceed the physiological data. Steps (b)-(c) can encompass machine learning.