Design methods and tools for mass personalization of smart wearable products

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Smart wearable products such as wristwear, headwear, footwear, and embedded implants, are progressing swiftly in both academia and industry with a forecasted market size of over $265 Billion by 2026 [ref: MARKETSANDMARKETS]. These products can offer expanded functions (e.g., communication in smart helmet) or help to collect and analyze personal or use data that can be useful for smart decision making (e.g., smart footwear for athletic training). One pressing issue of current wearables is how to cost-effectively address the design and fabrication challenges of mass personalization for avoiding long-term wearing discomfort (e.g., respirator-induced skin damage issues in Covid19) and improving customer satisfaction and adoption. The concept of design for mass personalization (DfMP) offers such a tailored need. The long-term objective of this proposed program is to establish methods to enable smart and connected product development and applications. The short-term objective is to apply cyber-physical system (i.e., intelligent system with intertwined physical and cyber objects), computational design, and additive manufacturing (AM) technologies to tackle challenges of developing a data-driven DfMP system in support of a wide range of personalized wearables development. More specifically, it will contribute to knowledge of how to involve customers in the co-design process, how to realize mass customer-specific bespoke design, and how to promote design innovation with AM. Accordingly, a set of practical DfMP toolkits will be developed including a cyber-physical customer co-design system for customer preference intake and rapid product design and validation, a smart personal data (i.e., anatomic and behavior data)-informed custom fit design tool for static and dynamic products (e.g., face mask and shoe insoles), and an AM-compliant DfMP tool for function integration and performance improvement. Traditional mass personalization design approaches have deficiencies such as being ad hoc, single-generation product focused, professional knowledge-intensive, and centralized design and fabrication. In contrast, the proposed program will enable customer preference reuse, automated custom-fit design process, and decentralized fabrication with improved customer satisfaction (e.g., comfort) and cost effectiveness. This program will also help to train HQPs in the demanding fields of artificial intelligence (AI), cyber-physical system, and AM as well as incubate spin-off companies in smart mass personalization services for various industries such as sport equipment, fashion, and healthcare in line with Canada's Pan-Canadian AI Strategy. It will significantly contribute to the fast-growing smart wearable device market and help Canada to gain global leadership in this emerging field.