Methods for sweat volume detection based on sweat print imaging

A method for sweat volume detection based on sweat print imaging, comprising:

• data collecting:
• obtaining a raw data set, the raw data set including at least one sweat print image formed by dropping different volumes of sweat on a test paper under different environmental parameters, the environmental parameters including a temperature and a humidity;
• image partitioning:
• identifying a color value of the at least one sweat print image, obtaining a color value extreme, and dividing the at least one sweat print image into a preset count of first regions according to the color value extreme;
• model constructing:
• calculating a total sweat collection volume of the at least one sweat print image and calculating a sweat error based on the total sweat collection volume and a real sweat volume, wherein the sweat error is obtained by:
• obtaining a sweat collection volume of at least one first region and summing the sweat collection volume of the at least one first region to obtain the total sweat collection volume:
• Q′i=(kx′1+b)a1+(kx′2+b)a2+, . . . ,(kx′p+b)ap, . . . ,+(kx′m+b)am,
• wherein x′p denotes a median color value of a p-th region of the at least one sweat print image, ap denotes an area of the p-th region of the at least one sweat print image, k and b denote preset coefficients, (ky′p+b)ap denotes a sweat collection volume of the p-th region of the at least one sweat print image, Q′i denotes the total sweat collection volume, and m denotes a count of first regions;
• and
• determining the sweat error based on a difference between the total sweat collection volume and the real sweat volume;
• and
• constructing an error prediction model based on a Back-propagation (BP) neural network using the temperature, the humidity, and the count of first regions as an input of the error prediction model and the sweat error as an output of the error prediction model;
• model training using the temperature, the humidity, and the count of first regions of the raw data set as the input of the error prediction model, and training parameters of the error prediction model to obtain a trained error prediction model;
• and
• sweat volume predicting:
• pasting a test paper to a to-be-tested part of a tester to obtain a to-be-tested sweat print image formed on the pasted test paper after a preset time period;
• dividing the to-be-tested sweat print image into at least one region and calculating a total sweat collection volume of the to-be-tested sweat print image;
• obtaining current environmental parameters, inputting the current environmental parameters and a count of second regions of the to-be-tested sweat print image into the trained error prediction model, and determining a sweat error of the to-be-tested sweat print image;
• and
• calculating a sweat volume and a sweat rate of the to-be-tested part based on the sweat error of the to-be-tested sweat print image.