Development of a hand classification system for smart hand wearables

Abstract

Research purpose: This study aimed to lay a research foundation for smart hand wearable design by classifying the right-hand data of 4545 adults aged 20 to 69. Further, to increase the practical applicability of the hand classification system, a hand type discrimination method and regression equations for the hand dimensions of each type were presented. Methods: This study statistically analyzed eighth Size Korea data with IBM SPSS Ver.26.0. Cluster analysis was performed to classify both finger length and circumference type. Discriminant analysis was conducted, yielding discriminant functions to aid potential smart hand wearable wearers in self-diagnosing their hand types. Linear regression analysis yielded regression equations for the detailed finger dimensions for the pattern-making of smart hand wearables. Results: The finger length type was categorized into four types: the Uphill type, Downhill type, Mountain type, and Horizon type for both men’s and women’s hands. The finger circumference type was categorized into two types, the Cone and Cylinder types, for both men’s and women’s hands. The discriminant function showed a mean accuracy rate of 89.9% and the regression equations a mean explanatory power of 72.9%. Conclusion: The hand classification system proposed in this study aimed to improve the fingertip fit of smart hand wearable products by analyzing the configuration of motion tracking gloves or haptic gloves. In addition, considering the practical applicability for both wearers and designers of smart hand wearables, a discrimination method of finger types for wearers’ self-diagnosis and regression functions of finger dimensions for designers’ pattern making were provided.