裸足形態把握のために分類的考察を行うことを目的とし, 18計測項目からなる20歳代, 50歳代, 60~70歳代の裸足計測値362例について, 分割数3, 4, 5, 6を指定して, Kmeans法によるクラスター分析を行い, この分析結果にKruskalの類似性判断を施すと, 裸足形態を分割数の変化の中で捉らえることができ, つぎの結果が得られた。

(1) 分割数3, 4, 6の年齢層別クラスターはstress 0.20以下の適合度で2次元表示され, 分割数3, 4, 6のクラスター特性は高度に類似しており, 分割数が異なるクラスターで構成されるグループにわけられる。

(2) 分割数3から分割数4, 6のクラスターへ362例のオブザベーションの移行を追跡すると, 6つの移行タイプがあげられ, 年齢の差が認められる。これら6タイプで362例の70.7%をカバーする。

(3) 6つの移行タイプから裸足形態を抽出すると, 若年層に多くみられる前足部に癖のない形状, 第5趾側に特徴がある形状, 中高年層にみられる第1趾側に特徴がある形状などのタイプがあげられる。

  In case of the measurement of bending rigidity G of clothes using slide type testing machine, G varies with sample length ℓ, this is unreasonable, becouse a material must have only one proper rigidity.

  In this paper, six clothes including hard clothes like canvas, broad clothes, and soft knitted fabric were used as samples, some measurements related to the rigidity of clothes with slide type method were carried out carefully, and the method to obtain the proper rigidity G of clothes was discussed.

  1) The next formula was given through observation of measurement in this paper,

    δ/ℓ＝Kℓ³, (K; const.)

here, δ ; hanging length when a long strip of sample hang in the shape of cantilever, ℓ ; sample length.

  When, the next relation was obtained,

    G＝W/8K

here, W ; weight per unit area of sample clothes.

  Accordingly, the gradient of initial linear part in δ / ℓ ~ ℓ³ curves was given, from the above formula G of clothes was able to calculated.

  2) To obtain the rigidity G of clothes practically, it is large enough to use, in the sample clothes such as canvas, broad and knitted fabric, ℓ_i ; are 5cm, 3.5cm, 3cm respectively.

  And to measure δ when sample length ℓ_i, calculating value of K_i (＝δ / ℓ_i⁴), and putting this K_i in the formula related to G above mentioned in 1), G was given.

  The purpose of this study is to clarify the relationship between amounts of fabric wastes and living factors and to predict for amounts of fabric household wastes in Tokyo 23 wards.

  The results can be summarized as follows ;

1) The results of multiple regression analysis according to statistics indicated that there was no relationship between amounts of fabric household wastes per year for those people and the number of family members per household.

2) The results of multiple regression analysis according to statistics showed the predicted data for amounts of fabric household wastes per year for those people.