Fall Detection System Using Wearable Posture and Positioning Sensors
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Solution Overview
Problem
Fall accidents among the elderly often result in serious injuries and increased mobility aids, placing a significant burden on care attendants and social resources, as existing technologies lack effective detection systems to promptly alert for falls or potential falls.
Innovation Solution
A fall detection system comprising wearable devices with posture detection and communication capabilities, positioning devices for 3D position tracking, and a data server that computes skeleton information to determine if a user has fallen, with a display device issuing warning messages upon detection, and optional shoe-mounted sensors for distance measurement data to enhance fall detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearables with multiple sensors are used to improve detection accuracy, then fall detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the detection function across multiple independent sensors (accelerometer, gyroscope, magnetometer) distributed on different body parts. Each sensor performs a specific measurement function, and the results are aggregated at the server to achieve comprehensive fall detection accuracy without requiring each individual device to be overly complex.
Solution Approach 2:
The wearable devices serve multiple functions: they act as both fall detection sensors and positioning markers. The same accelerometer and gyroscope that detect fall patterns also provide data for posture estimation, while the positioning function is achieved through the coordinated use of multiple wearables without requiring dedicated positioning hardware on each device.
2Reliability
If multiple wearables and positioning devices are deployed to enhance detection capability, then detection reliability is improved, but cost increases
Solution Approach 1:
The system merges the functions of fall detection and positioning into a unified architecture where the same wearable devices serve dual purposes. The acceleration and orientation data from wearables are used both for detecting fall events and for estimating user position and posture, eliminating the need for separate dedicated systems and reducing overall cost.
Solution Approach 2:
The wearables perform self-positioning by utilizing their own sensor data (accelerometer and gyroscope readings) to estimate orientation and position changes. The system leverages the natural movement data captured by the wearables during normal activities to infer positioning information, eliminating the need for additional positioning infrastructure.
3Speed
If real-time processing is implemented to provide immediate fall detection, then response time is improved, but computational load increases
Solution Approach 1:
The system extracts and pre-processes only the most critical fall detection features locally on the wearables (such as sudden acceleration changes and extreme orientation angles), while filtering out normal movement patterns. This extraction of salient features reduces the amount of data that needs to be transmitted and processed in real-time at the server, lowering computational load while maintaining fast response.
Solution Approach 2:
The system performs preliminary posture estimation and fall risk assessment in advance using machine learning models trained on normal activity patterns. By pre-learning what normal movement looks like and establishing baseline thresholds, the system can quickly identify abnormal fall events without requiring complex real-time computation, thus reducing instantaneous computational demand.
Data Source
AI summary
A fall detection system includes first sensing devices, second sensing devices, positioning modules, a data server and a display device. The first sensing device is configured to detect a posture of a body part of a user for obtaining body part posture data. The positioning modules are configured to detect positions of the first and second sensing devices, so as to obtain corresponding body part position data. Each of the second sensing devices is disposed on a shoe to detect a posture of a user's feet and to measure a distance from an ambient object for obtaining feet posture data and distance measurement data. The data server is configured to receive the body part posture data, the body part position data, the feet posture data and the distance measurement data to determine if the user falls down.