Movement-Aware Abnormal Situation Detection for Worker Sensors
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Solution Overview
Problem
Existing techniques for detecting abnormal situations such as tumbling or falling in work environments suffer from inaccurate detection, particularly in situations where the risk of falling is low, leading to excessive detection and erroneous alerts.
Innovation Solution
An information processing device and method that identifies the type of work being performed and adjusts detection sensitivity based on a determination criterion using sensors, including acceleration, inclination, and heart rate, to accurately detect abnormal situations like tumbling, falling, impact, lying immobile, fatigue, and high heart rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the sensitivity of the sensor is increased to detect abnormal situations without fail, then the detection capability is improved, but excessive detection occurs leading to erroneous alerts
Solution Approach 1:
The patent applies dynamics by making the detection sensitivity adjustable rather than fixed. The sensitivity is dynamically changed based on the identified movement type (e.g., walking, running, working), allowing the system to adapt to different activity contexts and reduce erroneous detections while maintaining reliable abnormal situation detection.
Solution Approach 2:
The patent changes the detection parameter (sensitivity threshold) based on the movement type identified by the identification unit. Different movement types have different determination criteria, which adjust the sensitivity parameter accordingly, resolving the contradiction between high detection capability and avoidance of erroneous alerts.
2Reliability
If the sensitivity of the sensor is increased to detect abnormal situations, then fewer abnormal situations are missed, but detection accuracy decreases in low-risk environments
Solution Approach 1:
The patent applies local quality by tailoring the detection sensitivity to specific local contexts (movement types). Instead of using a uniform high sensitivity everywhere, the system adjusts sensitivity locally based on whether the user is walking, running, or working, thereby maintaining accuracy in low-risk environments while preserving detection completeness in high-risk situations.
Solution Approach 2:
The detection parameter is changed according to the movement type context. In low-risk movement types (e.g., working at flat low position), the sensitivity is reduced to avoid erroneous detections, while in high-risk movement types, sensitivity is increased to ensure detection completeness.
3Ease of operation
If a fixed determination criterion is used for all movement types, then the system is simple to operate, but detection accuracy varies across different work situations
Solution Approach 1:
The system dynamically adjusts the determination criterion based on the movement type identified by the identification unit. The detection unit automatically selects appropriate criteria (e.g., acceleration thresholds, inclination angles) corresponding to the current movement type, maintaining simplicity for the user while achieving high accuracy across different situations.
Solution Approach 2:
The system performs self-adjustment by automatically identifying the movement type and selecting the appropriate determination criterion without user intervention. The identification unit and detection unit work together to autonomously adapt the detection parameters, maintaining ease of operation while improving detection accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of detecting abnormal situations by reducing erroneous detections in low-risk environments and ensuring critical situations are not missed, thereby enhancing worker safety.
Implementation Method 1
an acceleration sensor is attached to a required position of a person and tumbling is detected when acceleration obtained from the sensor exceeds a reference acceleration set in advance
Implementation Method 2
detection unit configured to detect an abnormal situation of the user using a determination criterion based on the movement type identified by the identification unit via a sensor provided to the user or provided in periphery of the user
Implementation Method 3
sensors, including acceleration, inclination, and heart rate
Data Source
AI summary
An object is to contribute to improvement in accuracy of detection for an abnormal situation.An identification unit configured to identify a movement type of a user, and a detection unit configured to detect an abnormal situation of the user using a determination criterion based on the movement type identified by the identification unit via a sensor provided to the user or provided in periphery of the user are provided.