Vehicle Operator Gait Detection for Transparent Impairment Screening
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Solution Overview
Problem
Existing impairment detection systems in vehicles are not transparent to the operator, making them susceptible to circumvention and lacking comprehensive detection of impairment, particularly medical episodes that the operator may not be aware of.
Innovation Solution
A system that uses gait sensors and machine learning algorithms, such as LSTM recurrent neural networks, to analyze the operator's gait parameters and provide transparent impairment detection by engaging the operator with notifications or disabling vehicle functions when impairment is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional impairment detection systems are implemented, then impairment detection capability is provided, but the system becomes non-transparent to the operator allowing circumvention
Solution Approach 1:
The system performs automatic gait analysis without requiring operator participation or awareness. Sensors continuously monitor gait parameters and the LSTM model automatically processes the data to detect impairment, eliminating the need for operator interaction and preventing circumvention while maintaining transparency.
Solution Approach 2:
The patent replaces traditional mechanical or manual impairment detection methods with an automated sensor-based system. Gait sensors, cameras, and LIDAR replace manual assessment, while machine learning algorithms replace rule-based detection, creating a transparent system that operates autonomously without operator knowledge.
2Reliability
If operator awareness of the detection system is increased, then circumvention attempts may be detected, but the transparency of the system decreases
Solution Approach 1:
The system performs impairment detection before the operator can attempt circumvention. By continuously monitoring gait parameters from the moment the operator approaches the vehicle, the system establishes a baseline and detects impairment early, preventing any potential circumvention attempts before they can occur.
Solution Approach 2:
The system provides continuous feedback through gait analysis, comparing real-time gait parameters against learned patterns of impairment. The LSTM model processes sequential gait data and provides ongoing assessment, creating a closed-loop system that maintains high detection accuracy while operating transparently without operator awareness.
3Reliability
If multiple gait parameters are analyzed, then detection comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The LSTM recurrent neural network serves multiple functions simultaneously: it processes sequential gait data, extracts features from multiple parameters, learns impairment patterns, and generates detection results. This single multi-functional component handles all aspects of gait analysis, reducing overall system complexity while maintaining comprehensive detection across multiple gait parameters.
Solution Approach 2:
The patent combines multiple gait sensors, cameras, and LIDAR into an integrated sensing system that collectively monitors comprehensive gait parameters. The sensor data streams are merged and processed together by the LSTM model, which integrates information from multiple sources to achieve comprehensive impairment detection while managing system complexity through unified processing.
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
Enables transparent and comprehensive impairment detection, including medical conditions, by allowing operators to continue operations without awareness of the detection process while ensuring safe vehicle operation through alerts or system disablement.
Implementation Method 1
the at least one ranging sensor is a light detection and ranging (LIDAR) sensor
Data Source
AI summary
An impairment detection process includes detecting an approach of an operator to a machine. The approach is monitored to determine a set of gait parameters of the operator based on an output of a set of gait sensors. The set of gait parameters is provided to a long short term memory (LSTM) recurrent neural network which determines a gait score by regressing the set of gait parameters. The gait score is compared to an impairment threshold, and the operator is engaged in response to the gait score exceeding the impairment threshold.