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Human-centered design of wearable assistive technology

Targeted study program
Masters with project
Doctorate
Postdoctoral studies
Research domains
Intelligent and Autonomous Systems
Health Technologies
Sensors, Networks and Connectivity
Financing
Financial support will be provided to doctoral candidates who have proven their abilities, motivations and time commitment. Funding will not be available for master’s students. Postdoctoral fellows must apply for their own funding from an external agency.
Other informations

Interested candidates should send a CV (please do NOT include year of birth or photo), transcripts and examples of technical writing by email to ornwipa.thamsuwan@etsmtl.ca. In the subject line, please indicate [PhD/MSc Candidate].

Our research applies a human-centered approach to the development of assistive technology. Our work currently focuses on back support exoskeletons for a diverse range of industrial users, but we are also opened for other wearable assistive technologies for the aging population as well as those with pathologies.

To create and validate solutions in both laboratory and field settings, we integrate a variety of disciplines, including but not limited to ones as follows:

  • Anthropometry
  • Human kinematics, i.e., posture, movement and balance, of which the data are collected from inertial measurement units and analyzed using advanced signal processing algorithms
  • Electromyography (EMG) in the aspect of localized muscle activity and fatigue, as well as overall muscle synergy
  • Heart rate variability (HRV) which represents the autonomic nervous system’s response to stress
  • User experience

Required knowledge

We are looking for motivated people with some of the following qualifications:

  • Education in Biomedical Engineering, Human Factors Engineering, Ergonomics, Kinesiology, Neuroscience, or other related fields
  • Aptitude in participant recruitment and data collection for research with human subjects
  • Intermediate skills in using and troubleshooting sensors
  • Proven project-based experience in the analysis of unstructured time-series data, with strong programming proficiency (MATLAB, Python or others)
  • Experience with or willingness to learn R for statistical analysis and data visualization
  • Knowledge in human-centered design principles and usability testing
  • Ability to conduct semi-structured interview and contextual analysis
  • Publication with first-authorship (for postdoctoral applicants only)