Data Processor for Care Support System Event Prediction
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Solution Overview
Problem
Existing care support systems struggle to accurately predict events such as bed-leaving or excretion in real-time, and fail to detect associated abnormal states like falls or tumbles, which are critical for providing timely and appropriate care.
Innovation Solution
A data processor is designed to acquire data from multiple sensors, including body movement sensors and excretion sensors, to predict the occurrence of events like bed-leaving or excretion, and to output event prediction information and state prediction information regarding potential abnormal states that may not be detected by single sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is obtained from a single sensor to detect events, then the system is simple, but the prediction accuracy and detection of abnormal states is insufficient
Solution Approach 1:
The patent combines data from multiple sensors (first sensor for event detection, second sensor for abnormal state detection, third sensor for additional event confirmation) to achieve comprehensive prediction. The processor integrates information from all sensors to generate unified prediction results, resolving the contradiction by merging multiple data sources to improve accuracy while managing system complexity through coordinated sensor operation.
Solution Approach 2:
The prediction system is designed to perform multiple functions: detecting events, predicting abnormal states, and providing timely warnings. The processor universally handles various sensor inputs and outputs comprehensive prediction information, enabling the system to address multiple care needs simultaneously without proportionally increasing complexity.
2Reliability
If multiple sensors are used to detect events and abnormal states, then the prediction accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent segments the detection system into specialized sensors with distinct functions: first sensors for event detection, second sensors for abnormal state detection, and third sensors for additional confirmation. Each sensor type is optimized for specific detection tasks, improving reliability through functional specialization while managing complexity through modular sensor design.
Solution Approach 2:
The processor acts as an intermediary that coordinates multiple sensors and integrates their data. It receives inputs from first, second, and third sensors, processes the information, and generates unified prediction results. This intermediary approach improves detection reliability by synthesizing multiple data streams while containing complexity within the processing layer rather than the sensor layer.
3Speed
If real-time prediction of events and abnormal states is implemented, then the responsiveness of care is improved, but the data processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary detection and prediction actions continuously, monitoring sensor data in advance of actual events. By maintaining constant readiness to detect patterns and predict outcomes, the system achieves real-time responsiveness without requiring complex on-demand processing, thus improving speed while managing complexity through continuous low-level monitoring.
Solution Approach 2:
The prediction system incorporates feedback mechanisms where detection results from first, second, and third sensors continuously inform and refine subsequent predictions. This feedback loop enables real-time adaptation to changing conditions, improving responsiveness through dynamic adjustment while managing processing complexity through iterative refinement rather than complex one-time calculations.
Data Source
AI summary
According to an embodiment, a data processor includes an acquisitor to acquire a first data; and a processor to conduct a first operation. At least a part of the first data is obtained from a first sensor to detect a state of a human subject. The processor outputs, if the first data includes a first data information, a first event prediction information related to an occurrence of a first event related to the human subject, and a state prediction information related to a state to be predicted of the human subject at the occurrence of the first event, in the first operation. The occurrence of the first event is detected by a second sensor to detect the state of the human subject. The state to be predicted of the human subject at the occurrence of the first event is not detected by the second sensor.