Predictive Modelling of Sports Car Seat Comfort: Data-Driven Human–Seat Interaction Analysis

Institution: University of Salerno; Laboratory Human Centred Design and Vehicle Design by Simulation

Website: https://www.unisa.it/

Country: Italy

Duration: 3–6 months (recommended minimum: 3 months)

Contact information at the placement: Rosaria Califano, rocalifano@unisa.it

Contact information at the higher education institution: bbarone@unisa.it

Description:

This traineeship represents the continuation of the research activities on automotive seat ergonomics and focuses on the development and validation of predictive comfort models for sports car seats.
The project adopts a data-driven empirical–analytical approach, combining:

• Objective pressure distribution data acquired through high-resolution pressure mats
• Anthropometric percentile clustering
• Subjective comfort ratings collected through structured questionnaires
• Statistical and regression-based modelling

Trainees will:

• Analyze large datasets derived from driving simulator sessions (pressure maps, contact areas, peak pressures, anthropometric data, subjective comfort scores)
• Perform correlation analysis and statistical validation
• Develop and refine predictive models of perceived comfort
• Evaluate model robustness using training/validation data splitting
• Contribute to the development of data-driven design guidelines for sports car seats
• The traineeship aims to strengthen the link between objective measurements (pressure distribution) and subjective comfort perception, enabling more personalized seat design strategies tailored to different user percentiles.

By the end of the traineeship, participants will deliver:

• A validated predictive comfort model
• Statistical analysis reports
• Data interpretation and engineering recommendations
• A final technical presentation of results

Requirements:

• Undergraduate or postgraduate students in Mechanical Engineering, Industrial Engineering, Automotive Engineering, or related fields
• Strong interest in data analysis, statistical modelling, and human-centered engineering
• Ability to analyze and interpret large volumes of heterogeneous data
• Basic knowledge of statistics and Machine learning analysis
• Familiarity with tools such as MATLAB, SPSS, Python, or similar software is an advantage
• Interest in ergonomics and human–machine interaction
• English communication skills at B1 level or higher
• Analytical mindset and problem-solving attitude

Language of communication: English

(Internship/Position ID: NSMT_IT0020)