Work estimation apparatus, work estimation method, and computer readable medium

A work estimation method comprising:

• calculating a distance between a feature of which, regarding joint movement data that is time series data indicating a temporal transition of a position of each joint of a worker during work as inference target data, the inference target data is converted into multidimensional waveform data, and converted by convolving the multidimensional waveform data converted using a convolution parameter indicating weight found by learning, and each feature of which each piece of joint movement data that configures a plurality of pieces of joint movement data for learning the convolution parameter is converted into multidimensional waveform data, and each piece of multidimensional waveform data converted is converted by convolving using the convolution parameter, by a computer;
• and
• estimating work content corresponding to the inference target data based on each distance calculated, by the computer, wherein
• the convolution parameter is a parameter learned, using a loss function, in a way that a distance between each two pieces of joint movement data taken out from learning data consisting of a plurality of pieces of joint movement data associated with work labels indicating work content becomes relatively small in a case where the work labels associated with each two pieces of joint movement data are a same, and relatively large in a case where the work labels associated with each two pieces of joint movement data differ from each other.

A non-transitory computer readable medium storing a work estimation program that causes a work estimation apparatus that is a computer to execute:

• a distance calculation process to calculate a distance between a feature of which, regarding joint movement data that is time series data indicating a temporal transition of a position of each joint of a worker during work as inference target data, the inference target data is converted into multidimensional waveform data, and converted by convolving the multidimensional waveform data converted using a convolution parameter indicating weight found by learning, and each feature of which each piece of joint movement data that configures a plurality of pieces of joint movement data for learning the convolution parameter is converted into multidimensional waveform data, and each piece of multidimensional waveform data converted is converted by convolving using the convolution parameter;
• and
• a work estimation process to estimate work content corresponding to the inference target data based on each distance calculated, wherein
• the convolution parameter is a parameter learned, using a loss function, in a way that a distance between each two pieces of joint movement data taken out from learning data consisting of a plurality of pieces of joint movement data associated with work labels indicating work content becomes relatively small in a case where the work labels associated with each two pieces of joint movement data are a same, and relatively large in a case where the work labels associated with each two pieces of joint movement data differ from each other.