marco_at_work

( marco.ajovalasit@brunel.ac.uk )

Marco Ajovalasit is a Lecturer in Human Factors & Ergonomics within the School of Engineering and Design, Brunel University, West London, UK. He holds a Ph.D. degree obtained from The University of Sheffield, UK which focused on the effects of fuel content on the human perception of steering wheel hand-arm vibration as perceived by the driver in automobiles at idle. His research interests include multisensory driver perception in transport system with particular emphasis placed to the human psychophysical response to vibro-tactile and acoustic stimuli in automobiles. He performs research with the Perception Enhancement Systems (PES) Research Group at Brunel Design. The goal is to define the parameters of environmental stimuli which control the human detection of the associated real world scenario. He is an associate member of the Ergonomics Society and a member of the Human Centred Design Institute (HCDI).

Research interests

  • Multisensory driver perception (visual, sound, tactile perception in motor vehicles)
  • Psychophysics (Human subjective response to sound and vibration stimuli)
  • Occupational Ergonomics (workforces, human strength, visual strains and physical workload)
  • Product experience (measurement of attitudes, human behaviour and emotional experience in product design)

 

An example of design research performed to optimise the perceptual characteristics of a product was an activity performed for Shell Research Ltd. which developed a test method for quantifying driver response to engine idle vibration and sound. By understanding how a driver’s feelings, of engine roughness or power, change with changes in the chemical properties of the fuel it is possible to choose chemical compounds that meet and exceed customer expectations. In this case, knowledge of the human perceptual characteristics was deployed to design a fuel that considered the driver as much as it did the car.

The research acvity has involved developing of several signal processing techniques for the detection and quantification of variation in diesel engine idle vibration. The techniques include time-frequency analysis using the wavelet transform and time-varying covariance method.

Psychophysical scaling methods including paired-comparison and the Borg CR10 scale have been used for quantifying and modelling the human subjective response to the sound heard, and the vibration felt through the steering wheel, by the driver.