Towards inclusive design of assistive technologies through the uses of wearable sensors and artificial intelligence

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Exoskeletons, supporting workers' backs, arms, and shoulders in many industries, may reduce the risk of occupational injuries and, thus, improve productivity. However, current models are inappropriate for more than half of the Canadian working population because many Canadian workers have different body types and functional capabilities from those for whom exoskeletons were designed. The long-term vision of this research is to develop techniques to make assistive devices like exoskeletons suitable for a broader range of users. In the first five years, this research will incorporate the user's biomechanical and metabolic characteristics into designing a passive back support exoskeleton. There are specific objectives in this research: First, we will develop new protocols to characterize users' physical demands, i.e., body kinematics, muscle activity, and cardiac loads, using inertial measurement units, electromyography and heart rate monitors, respectively. Second, we will create an inverse dynamics model to calculate loads on muscles, joints, and spinal column with the human body kinematics and anthropometry data. Then we will predict optimal exoskeleton parameters for the users' muscular, thermal and mental loads, incorporating artificial intelligence into digital human modelling. Third, we will conduct a case study on a specific population to gather data for the exoskeleton design. The proposed research will contribute to the natural sciences and engineering by advancing user-centred methods for designing assistive devices, making them suitable and accessible to a broader population. Developing tools that prevent injuries will benefit society and the economy by improving working conditions and ensuring a stable workforce.