Wearable Sensor Data Validation for Driver Fatigue Analysis

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Solution Overview

Problem

Current systems fail to effectively analyze and mitigate driver forearm muscle fatigue during car racing, particularly in IndyCar where power steering is not allowed, leading to performance deterioration due to lack of consideration for heterogeneous data analysis.

Innovation Solution

A vehicle data analytics system utilizing wearable sensors and telemetry data to generate actionable insights, employing data validation techniques and multi-modal analysis to identify locations on the race circuit where drivers can relax and reduce fatigue, with a 99.5% accuracy in data reliability classification.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If drivers manually steer without power steering in IndyCar, then the racing regulation is maintained, but driver forearm muscle fatigue increases and performance deteriorates

Engineering Contradiction:

Improveregulation complianceVSAvoiddriver performance

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary analysis of race data to identify optimal relaxation zones before the driver needs to exert force. By pre-identifying straight sections and low-stress periods, the system enables drivers to conserve muscle energy during critical high-stress sections of the race.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors driver forearm muscle activity through wearable sensors and provides real-time feedback about fatigue levels. This feedback loop allows the system to adapt to changing driver conditions and provide personalized fatigue reduction insights throughout the race.

Inventive Principle:

Principle #23Feedback

2Loss of information

If heterogeneous data from wearable sensors and telemetry is analyzed, then actionable fatigue reduction insights are generated, but data validation complexity increases

Engineering Contradiction:

Improveinsight accuracyVSAvoiddata validation process

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The data validation process is segmented into distinct modules: wearable sensor data validation, telemetry data validation, and integrated heterogeneous data validation. Each module processes and validates specific data types independently before integration, making the complex validation process more manageable and systematic.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces intermediate processing layers that bridge wearable sensor data and telemetry data. These intermediate layers standardize and harmonize the heterogeneous data formats, enabling reliable integration while reducing the complexity of direct data validation between incompatible systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10566084B2System and method for racing data analysis using telemetry data and wearable sensor data

Publication Date: 2020.02.18 NTT INNOVATION INST

AI summary

A system and method for racing data analysis using telemetry data and wearable sensor data may be used, in one implementation, to analyze muscle use in extreme racing conditions to find actionable insights for the race car driver. An example of the actionable insights may be how to minimize the driver's muscle fatigue during a race. The system and method may perform data validation of the data from the wearable sensor(s) and then generate the actionable insights from the validated data.