Postgraduate Diploma in Workload and Attention Assessment (DMS/OMS)
Online 800-hour Postgraduate Diploma in Workload and Attention Assessment (DMS/OMS). 8 modules covering Regulation (EU) 2019/2144 (General Vehicle Safety), Delegated Regulation (EU) 2023/2590 (Advanced Driver Distraction Warning, ADDW) and Python (OpenCV). Dedicated tutor and open enrolment.
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Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Explain driver attention and mental workload models.
Describe the architecture of DMS and OMS systems.
Measure mental workload using physiological signals.
Analyse behavioural indicators such as gaze and posture.
Develop algorithms for estimating distraction and workload.
Define adaptive alert and intervention strategies.
Validate the system against regulations and Euro NCAP protocols.
Who it's for
- DMS/OMS systems engineer looking to specialise in Workload and Attention Assessment (DMS/OMS).
- In-cabin computer vision engineer looking to specialise in Workload and Attention Assessment (DMS/OMS).
- Driver attention researcher looking to specialise in Workload and Attention Assessment (DMS/OMS).
- Safety systems validation engineer looking to specialise in Workload and Attention Assessment (DMS/OMS).
- Automotive human factors specialist looking to specialise in Workload and Attention Assessment (DMS/OMS).
- Behavioural data scientist looking to specialise in Workload and Attention Assessment (DMS/OMS).
- Engineering or science graduates looking to enter the automotive sector.
What you'll work with
- Regulation (EU) 2019/2144 (General Vehicle Safety)
- Delegated Regulation (EU) 2023/2590 (Advanced Driver Distraction Warning, ADDW)
- Euro NCAP driver monitoring protocols
- ISO 15007 (driver visual behaviour)
- ISO 15005 (dialogue management in on-board systems)
- ISO 10075 (mental workload)
8 modules · 800 h
1.1Attention models in driving1.2Mental workload and performance: the Yerkes-Dodson curve1.3Visual, manual and cognitive distraction1.4Inattention and mind wandering1.5Fatigue and drowsiness versus workload1.6Individual factors and age1.7Workload in automated driving1.8Indicators of performance degradation1.9Accident data on distraction1.10Case study: distraction analysis in naturalistic studies
2.1Architecture of a driver monitoring system2.2In-cabin occupant monitoring2.3Infrared cameras and positioning2.4Face, eye and head detection2.5Gaze direction estimation2.6Occupant detection and forgotten children2.7Occupant classification for airbags2.8ADDW requirements of Regulation (EU) 2023/25902.9Euro NCAP occupant protocols2.10Laboratory: gaze estimation with an open model
3.1Pupillometry and cognitive load3.2Blink rate and PERCLOS3.3Heart rate variability3.4Electrodermal activity3.5EEG in driving3.6fNIRS and prefrontal activity3.7Steering wheel and seat sensors3.8In-vehicle signal quality3.9Fusion of physiological measures3.10Practical: processing HRV recorded in a simulator
4.1Gaze patterns and AOI4.2Time off the road4.3ISO 15007 gaze measurement4.4Lateral deviation and steering control4.5Speed and distance variability4.6Event reaction time4.7Secondary tasks: DRT from ISO 174884.8Subjective scales: NASA-TLX4.9Integration of measures4.10Laboratory: calculating ISO 15007 gaze metrics
5.1Feature extraction5.2Distraction classifiers5.3Deep learning with video5.4Temporal models: LSTM and transformers5.5Continuous workload estimation5.6Labelling and ground truth5.7Cross-validation by subject5.8Robustness to glasses, lighting and occlusion5.9Fairness across demographic groups5.10Practical: distraction classifier with labelled data
6.1Tiered distraction warnings6.2Adapting ADAS to the driver's state6.3Managing infotainment workload6.4Locking functions while driving6.5Interaction with the voice assistant6.6Drowsiness interventions6.7Acceptance and annoyance of warnings6.8Feedback design6.9Effectiveness assessment6.10Workshop: designing a distraction warning strategy
7.1Simulator and track trials7.2Naturalistic studies and FOT7.3System performance indicators7.4False positives and negatives7.5Euro NCAP assessment protocols7.6Type-approval requirements7.7Privacy and in-vehicle processing7.8ISO 26262 applied to DMS7.9Technical documentation7.10Case study: DMS validation plan
8.1Requirements definition8.2Sensor selection8.3Experimental design8.4Data collection8.5Algorithm development8.6Performance evaluation8.7Intervention strategy design8.8Validation plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (OpenCV), MATLAB, iMotions, Tobii Pro Lab, in free, academic or evaluation versions. Trials are studied using real driving simulator procedures with secondary tasks, in-cabin eye-tracking recording and physiological measurement of mental workload. Assessment is continuous across modules, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Explain driver attention and mental workload models.
Describe the architecture of DMS and OMS systems.
Measure mental workload using physiological signals.
Analyse behavioural indicators such as gaze and posture.
Develop algorithms for estimating distraction and workload.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You will work with Python (OpenCV), MATLAB, iMotions and Tobii Pro Lab. Free, academic or evaluation versions are used, and the tutor will point out a free alternative wherever one exists.
An SEIUM Institute diploma on passing the continuous assessment and the integrating project. This is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Please review the recognition policy when you enrol.
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