A sequential posture identification device for associating a posture and activity with a heart rate, comprising: an acceleration information measurement unit that is provided in a wearable device, and configured to measure acceleration information of motion of a subject whom the wearable device is attached to; a biological signal information measurement unit that is provided in the wearable device, and configured to measure biological signal information of the subject; a feature data extraction unit configured to extract, from the acceleration information and the biological signal information, first feature data corresponding to a first predetermined period and second feature data corresponding to a second predetermined period; a first generation unit configured to generate, by sequential machine learning based on the first feature data, a dynamic/static activity identification model for identification of whether the subject is involved in a dynamic activity or in a static activity; a second generation unit configured to generate, by sequential machine learning based on the first feature data, a dynamic-activity identification model for identification of plural dynamic-activity patterns; a third generation unit configured to generate, by sequential machine learning based on the first feature data, a static-activity identification model for identification of plural static-activity patterns; a first determination unit configured to determine, based on the dynamic/static activity identification model and the second feature data, whether the subject is involved in a dynamic activity or in a static activity in the second predetermined period; a second determination unit configured to determine, based on the dynamic-activity identification model and the second feature data, a dynamic-activity pattern of the subject in the second predetermined period; a third determination unit configured to determine, based on the static-activity identification model and the second feature data, a static-activity pattern of the subject in the second predetermined period; an identification unit configured to identify a posture and an activity of the subject in the second predetermined period, by combining together results of the determination by the first, second, and third determination units; and a generating unit configured to generate correspondence information associating between the posture and activity identified by the identification unit and biological signal information of the subject in the second predetermined period, wherein the biological signal information is the heart rate, wherein the feature data extraction unit is configured to extract, from the first predetermined period, plural periods overlapping one another and having different lengths, and extract the first feature data for each of the extracted periods. an acceleration information measurement unit that is provided in a wearable device, and configured to measure acceleration information of motion of a subject whom the wearable device is attached to; a biological signal information measurement unit that is provided in the wearable device, and configured to measure biological signal information of the subject; a feature data extraction unit configured to extract, from the acceleration information and the biological signal information, first feature data corresponding to a first predetermined period and second feature data corresponding to a second predetermined period; a first generation unit configured to generate, by sequential machine learning based on the first feature data, a dynamic/static activity identification model for identification of whether the subject is involved in a dynamic activity or in a static activity; a second generation unit configured to generate, by sequential machine learning based on the first feature data, a dynamic-activity identification model for identification of plural dynamic-activity patterns; a third generation unit configured to generate, by sequential machine learning based on the first feature data, a static-activity identification model for identification of plural static-activity patterns; a first determination unit configured to determine, based on the dynamic/static activity identification model and the second feature data, whether the subject is involved in a dynamic activity or in a static activity in the second predetermined period; a second determination unit configured to determine, based on the dynamic-activity identification model and the second feature data, a dynamic-activity pattern of the subject in the second predetermined period; a third determination unit configured to determine, based on the static-activity identification model and the second feature data, a static-activity pattern of the subject in the second predetermined period; an identification unit configured to identify a posture and an activity of the subject in the second predetermined period, by combining together results of the determination by the first, second, and third determination units; and a generating unit configured to generate correspondence information associating between the posture and activity identified by the identification unit and biological signal information of the subject in the second predetermined period, wherein the biological signal information is the heart rate, wherein the feature data extraction unit is configured to extract, from the first predetermined period, plural periods overlapping one another and having different lengths, and extract the first feature data for each of the extracted periods.