人體計測資料庫數據驗證

人體計測資料對於產品、作業、手工具設計等皆為一具有極大參考價值之資料，但人體計測值之量測、蒐集與整理皆需耗費大量的人力、物力與時間。而資料處理過程中因人為失誤、儀器誤差等因素造成的資料異常或遺漏在所難免。當資料中出現異常數值或遺漏時，應用數學方法加以修正填補較重新量測更有效率。由於人體發展模式與傳統統計手法所採用的一次線性模式有極大的差異，因此本研究應用灰理論之廣義能量系統概念建立人體系統發展之慣性模型，以進行人體發展之預測。本研究針對一次累加生成建模、雙向建模、除法建模、開平方轉換之一次累加生成建模、對數轉換之一次累加生成建模、殘差均值修正、階躍數列建模與一次累減生成建模等八種灰預測模型建立方法進行研究、分析以找尋適用於人體計測之灰預測模型建立方式。
The data of Anthropometry has been the prior reference toward the designs of products, procedures, and implements. However, it takes a large number of manpower, physical resources and time on measurements, information collecting and dispositions of Anthropometry data.Due to the errors caused by the manpower or instruments, the omission of data and inaccurate value have been appeared on the study.Therefore, it is more efficient and effective to use mathematical model to correct and fill out the data instead of restarting the data measurements again.The following study constructs the metrology of the Gray Model to forecast the growing of human bodies, because there has been the discrepancy between the growing model of human bodies and traditional statistic method according to the linear model.The study has been focused on eight forecasting models of the Gray System to analyze and find out the suitable model for the forecasting of the growing human body.