This project seeks to enhance the occupational health and safety of construction workers by significantly advancing current wearable sensor-based ergonomic monitoring methods.
Development of wearable sensors for the occupational health and safety improvement of construction workers
The central innovation is a deep-learning algorithm-based correlation between a state-of-the-art XSens motion-capture suit (ideal for research and laboratory use), and a fewer-sensor based wearable device (ideal for practical in-field use).
The support from the Global Challenges seed fund enables the team to develop a holistic solution that goes beyond engineering. Collaborating across UOW's four faculties, the team will convert biomechanical data into potential injury insights, co-design user-friendly industry-specific wearables, and analyse the advantages of technology adoption for improving corporate image and corporate social responsibility. The project also encompasses relevant policy analysis to ensure device adoption aligns with workers' rights and privacy protection.
The findings could be extrapolated to scenarios involving professionals engaged in strenuous physical tasks, including athletes, mining workers, firefighters, airport baggage handlers, and more.
The team
- Aziz Ahmed (EIS)
- Manish Narsipura Sreenivasa (EIS)
- Gursel Alici (EIS)
- Hassan Hosseinzadeh (ASSH)
- Zubair Ratan (ASSH)
- Abdullah Al Mamun (BAL)
- Afroza Begum (BAL)
- James Forsyth (SMAH)
- Yeong Lee (Community Partner)
- David Varcoe (Community Partner)
SDG's
