Foot morphology was analyzed using methods of classification. Data concerning 18 parameters of bare feet was collected from 362 feet of subjects aged in their 20s, 50s or between 60 and79. The data were subjected to a cluster analysis by Kmeans method, using 3, 4, 5 and 6 as numbers of partitions. The results of this analysis were subjected to Kruskal's similarity assessment. This assessment, which was carried out while the number of partitions was increased, allowed us to extract morphological features of the feet, as shown below.

  (1) The clusters for each age group at partition numbers of 3, 4 and 6 could be two-dimensionally represented, at a stress below 0.20. The characteristics of clusters at each of the three different partition numbers (3, 4 and 6) resembled each other. Thus, re-grouping of clusters with different partition numbers was possible.

  (2) When changes in clusters of 362 feet following an increase of the partition number from 3 to 4 and 6 were analyzed, six types of change (cluster-combination types) were identified and the frequencies of these types differed among different age groups. These 6 types were seen in 70.7% of the 362 feet examined.

  (3) When foot characteristics were extracted from the six change patterns mentioned above, feet for the young group were characterized by the lack of specific features in the anterior portion and the presence of specific features in the 5th toe side, and the feet for the middle and aged groups were characterized by specific shapes of the 1st toe side.

  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.