Intelligent Seating Wellness Monitoring with Sensor Fusion
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Solution Overview
Problem
Current monitoring systems lack the ability to effectively assess and provide real-time feedback on mobility and wellness metrics, such as sitting and standing times, which are crucial for predicting falls and diagnosing issues like lower back pain, especially in elderly individuals, and often require costly and inconvenient therapy visits.
Innovation Solution
A system that integrates sensors into seating apparatuses, such as chairs, to collect and analyze data on posture, balance, and movement patterns, using machine learning algorithms to detect abnormal conditions and provide personalized feedback and exercises to improve mobility and reduce discomfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are integrated into seating apparatus to monitor wellness metrics, then measurement precision of mobility and wellness attributes is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (weight sensors, pressure sensors, motion sensors) into a single seating apparatus to comprehensively monitor wellness metrics. This merging approach enables precise measurement of multiple parameters (weight, pressure distribution, motion patterns) simultaneously, improving measurement precision while consolidating monitoring functions into one device rather than requiring separate monitoring systems.
Solution Approach 2:
The seating apparatus is designed to perform multiple functions: it serves as both a conventional seat and a comprehensive wellness monitoring station. The same seating structure integrates weight measurement, pressure distribution analysis, motion detection, and posture assessment capabilities, allowing a single device to provide diverse wellness metrics without requiring multiple separate devices.
2Reliability
If real-time sensor data analysis is implemented to detect abnormal conditions, then reliability of wellness monitoring is improved, but use of energy increases
Solution Approach 1:
The system pre-establishes thresholds and criteria for abnormal conditions based on baseline wellness data. By preparing detection rules and decision algorithms in advance, the system can quickly compare real-time sensor readings against predetermined standards without requiring complex real-time analysis, thereby improving detection reliability while minimizing processing energy consumption during actual monitoring.
Solution Approach 2:
The seating apparatus autonomously analyzes sensor data and detects abnormal conditions without requiring external intervention or complex centralized processing. The embedded system performs self-diagnosis and generates alerts independently, reducing the need for high-energy cloud-based analysis while maintaining reliable detection capabilities through localized processing.
3Loss of information
If multiple sensor types are integrated to provide comprehensive wellness data, then quantity of information obtained is improved, but ease of operation deteriorates
Solution Approach 1:
The system provides automated feedback through notifications and alerts when abnormal conditions are detected, guiding users on necessary actions without requiring them to interpret complex sensor data. This feedback mechanism ensures comprehensive wellness information is captured and analyzed while simplifying user interaction to simple alert-response patterns, maintaining data completeness without compromising ease of operation.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates complex multi-sensor data into simple, actionable insights. The system acts as a mediator between the multiple sensors and the user, converting raw data from weight sensors, pressure sensors, and motion sensors into comprehensible wellness assessments and clear guidance, thereby preserving information completeness while simplifying the user interface.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described for implementing intelligent seating for wellness monitoring. A system obtains data from a first sensor integrated in an intelligent seating apparatus at a property. The first data indicates a potential abnormal condition of a person at the property. The system determines that the person has an abnormal condition based on the first data corresponding to the person having used the seating apparatus. Based on the abnormal condition, the system provides an indication to a client device of the person to prompt the person to adjust their use of the seating apparatus. The system also obtains visual indications of the abnormal condition, determines the type of abnormal condition afflicting the person, and determines a wellness command with instructions for alleviating the abnormal condition. The wellness command is provided for display on the client device.