Machine Learning in Sensor-Enabled Environments for Wellness Alerts
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Solution Overview
Problem
Existing monitoring systems for health and wellness of individuals, particularly the elderly, face challenges in scalability and quality of life due to the generation of large data volumes and the impracticality of human oversight, leading to potential maltreatment and inadequate attention.
Innovation Solution
A machine learning model is trained to recognize patterns in data from sensor-enabled environments, aligning them with pattern frameworks to detect health or wellness events, and send alerts, minimizing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor-enabled environments collect comprehensive health and wellness data, then monitoring accuracy is improved, but data volume increases making human oversight impractical
Solution Approach 1:
The patent extracts and filters only the most critical information from large volumes of sensor data by aligning data with pattern frameworks that identify health and wellness events. This extraction process separates meaningful patterns from redundant data, enabling human oversight of summarized insights rather than raw data streams.
Solution Approach 2:
The patent introduces machine learning models as intermediary systems between sensor data collection and human decision-making. These models process and interpret complex data patterns, translating them into actionable alerts and recommendations that humans can easily understand and respond to.
2Reliability
If comprehensive monitoring is implemented, then health and wellness detection is improved, but human oversight becomes impractical leading to potential maltreatment
Solution Approach 1:
The patent implements self-service monitoring where the system automatically detects, evaluates, and responds to health and wellness events without requiring continuous human intervention. The machine learning models autonomously process data, identify patterns, and generate appropriate responses, freeing humans from mandatory oversight while maintaining reliable detection.
Solution Approach 2:
The patent establishes feedback loops where the system continuously monitors health and wellness data, compares it against pattern frameworks, and automatically adjusts monitoring intensity and response actions. This feedback mechanism ensures reliable detection while adapting to individual needs without requiring constant human direction.
3Extent of automation
If machine learning models process and evaluate sensor data, then automation of monitoring is improved, but system complexity increases
Solution Approach 1:
The patent segments the monitoring system into distinct functional modules: data collection from sensors, data alignment with pattern frameworks, machine learning evaluation, and alert generation. This segmentation allows each component to be developed, tested, and maintained independently, managing overall system complexity while achieving high automation.
Solution Approach 2:
The patent creates a universal pattern framework that can be applied across multiple health and wellness monitoring scenarios. This multi-functional framework allows the same basic system architecture to handle diverse data types and monitoring objectives, reducing the need for separate specialized systems and thereby managing complexity.
4Speed
If automated alert systems are implemented, then response time to health events is improved, but false alerts may cause alarm fatigue
Solution Approach 1:
The patent applies partial action by filtering and prioritizing alerts based on confidence levels and pattern match strength. Rather than alerting on every possible anomaly, the system selectively notifies only when patterns strongly indicate actual health and wellness events, reducing false alerts while maintaining fast response to genuine issues.
Data Source
AI summary
Machine learning for aggregating and evaluating data from a sensor enabled environment (SEE) may be provided by receiving training data from at least one SEE related to behavioral, health, wellness, and safety (BHWS) events affecting at least one person under monitoring (PUM); developing a language of wellness (LoW) syntax for a linguistic artificial intelligence or machine learning (AI/ML) model by aligning the BHWS events with a pattern framework indicative of behaviors of the PUM; training the linguistic AI/ML model based on occurrences of the BHWS events in the training data and the LoW syntax such that the linguistic AI/ML model is configured to: generate a predicted BHWS event based on a series of behaviors observed for a particular PUM in a particular SEE; and generate a predictive alert in response to identifying that the predicted BHWS event disobeys the LoW syntax.