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Accurate acquisition for customer’s requirements is the base and key in product configuration design. However, original customer’s requirements must be decomposed in order to comprehend truly and apply them to guide product configuration design, because they are usually some fuzzy, general even contradictory customized demands. In the paper, two concepts are introduced for customer’s requirement decomposition. One is the requirement element, and the other is the granularity of requirement element. Moreover, the controlling principle for granularity of requirement element is given and the method of requirement decomposition is proposed. This method means semantic segmentation, semantic translation, supplement or subdivision of human-machine-environment and semantic combination. Customer’s abstract demands could be effectively decomposed into some specific requirement elements according to the proposed method of requirement decomposition as well as by controlling the granularity of requirement element reasonably. Finally, the customized design of a money-binding machine is taken as an example to validate the effectivity of proposed method.

The global pandemic of the coronavirus disease (COVID-19) is dramatically changing the lives of humans and results in limitation of activities, especially physical activities, which lead to various health issues such as cardiovascular, diabetes, and gout. Physical activities are often viewed as a double-edged sword. On the one hand, it offers enormous health benefits; on the other hand, it can cause irreparable damage to health. Falls during physical activities are a significant cause of fatal and non-fatal injuries. Therefore, continuous monitoring of physical activities is crucial during the quarantine period to detect falls. Even though wearable sensors can detect and recognize human physical activities, in a pandemic crisis, it is not a realistic approach. Smart sensing with the support of smartphones and other wireless devices in a non-contact manner is a promising solution for continuously monitoring physical activities and assisting patients suffering from serious health issues. In this research, a non-contact smart sensing through the walls (TTW) platform is developed to monitor human physical activities during the quarantine period using software-defined radio (SDR) technology. The developed platform is intelligent, flexible, portable, and has multi-functional capabilities. The received orthogonal frequency division multiplexing (OFDM) signals with fine-grained 64-subcarriers wireless channel state information (WCSI) are exploited for classifying different activities by applying machine learning algorithms. The fall activity is classified separately from standing, walking, running, and bending with an accuracy of 99.7% by using a fine tree algorithm. This preliminary smart sensing opens new research directions to detect COVID-19 symptoms and monitor non-communicable and communicable diseases.