Robotic Fall Detection Using Probabilistic Object Location Analysis
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Solution Overview
Problem
Conventional object detection systems face limitations in accurately detecting out-of-place objects and often generate false positive and false negative detections, leading to reduced accuracy and increased resource usage.
Innovation Solution
A method and system that utilize a probability distribution of object locations over time to identify and correct the position of objects, employing density-based clustering and probability density analysis to distinguish between true and false object locations, allowing a robotic device to perform actions based on likelihood thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional object detection systems store each estimated object location with timestamp in a database, then object location tracking is achieved, but system complexity and resource usage increase
Solution Approach 1:
The patent extracts only the essential information needed for fall detection (object location estimates and timestamps) and stores them in a simplified database structure, removing unnecessary complexity while maintaining tracking capability. The system focuses on storing minimal viable data required for the specific application of fall detection rather than comprehensive object tracking.
Solution Approach 2:
The system pre-processes and stores object location estimates with timestamps in advance, creating a ready-to-analyze database structure before fall detection is needed. This preliminary organization of data enables faster and more efficient fall detection analysis when required, reducing computational complexity during critical detection moments.
2Productivity
If conventional object detection systems detect all objects in environment, then comprehensive monitoring is achieved, but false positive and false negative detections increase
Solution Approach 1:
The patent applies different detection strategies to different locations in the environment. It focuses computational resources on high-risk areas where falls are more likely to occur and where detection is most critical, rather than uniformly monitoring all areas with equal intensity. This localized approach improves accuracy in critical zones while maintaining overall detection coverage.
Solution Approach 2:
The system dynamically adjusts detection parameters such as probability thresholds and monitoring intensity based on location, time, and contextual factors. By changing these parameters adaptively, the system reduces false positives and false negatives while maintaining comprehensive monitoring coverage across different environmental conditions.
3Ease of operation
If robotic device performs actions upon detecting out of place objects, then response capability is improved, but resource usage increases
Solution Approach 1:
The system pre-identifies and flags objects that are out of place before taking corrective actions. By detecting and marking anomalous objects in advance, the robotic device can plan and execute responses more efficiently, reducing immediate resource consumption while maintaining rapid response capability when needed.
Solution Approach 2:
The robotic device performs partial actions upon detection, such as first verifying the anomaly, then determining the appropriate response level. Not all detections require full robotic intervention - some may only need monitoring or minor adjustments. This partial action approach reduces resource usage while maintaining adequate response capability for critical situations.
Data Source
AI summary
A method for controlling a robotic device includes observing a first object associated with an object type at one or more first locations in an environment over a period of time prior to a current time. The method also includes generating a probability distribution associated with the one or more first locations based on observing the first object over the period of time. The method further includes observing, at the current time, a second object associated with the object type at a second location in the environment. The method still further includes determining a probability of the second object being at the second location based on observing the second object at the second location. The probability is based on the probability distribution associated with the one or more first locations. The method also includes controlling the robotic device to perform an action based on the probability being less than a threshold.