Abstract: A leg muscle strength estimation system includes: an obtainer that obtains an image including a user that is walking as a subject of the image; and an estimator that estimates a leg muscle strength of the user based on the obtained image.

Abstract: A physical-ability estimation system (1) Includes: an analyzer (32) that estimates a gait feature of a user (U) from a moving image generated by capturing the user (U) walking; and an estimator (33) that estimates, based on the gait feature, respective assessment results on at least two assessment Items for assessing a physical ability of the user.

Abstract: A sarcopenia evaluation method in a sarcopenia evaluation device that evaluates sarcopenia based on a walking motion of a subject includes: acquiring walking data related to walking of the subject; detecting, from the walking data, at least one walking parameter of an angle of a knee joint of one leg in a stance phase of the one leg of the subject, an angle of the knee joint of the one leg in a swing phase of the one leg, a vertical displacement of a toe of one foot in the stance phase, a vertical displacement of the toe of the one foot in the swing phase, an angle of an ankle joint of the one foot in the stance phase, and an angle of the ankle joint of the one foot in the swing phase; and determining whether or not the subject has sarcopenia using at least one walking parameter.

Abstract: A cognitive function evaluation method in a cognitive function evaluation device that evaluates a cognitive function based on a walking motion of a subject includes: acquiring walking data related to walking of the subject; detecting, from the walking data, at least one of an angle of an ankle joint of one foot and an angle of a knee joint of one leg of the subject; and determining a cognitive function level of the subject using at least one of the angle of the ankle joint and the angle of the knee joint.

Abstract: A factor estimation system is a factor estimation system that estimates a factor of fall risk indicating a possibility of a fall of a measured person, including: a calculator that obtains body motion data indicating body motion of the measured person during walking, and calculates two or more walking parameters of the measured person based on the body motion data obtained; and a factor analyzer that estimates, based on the two or more walking parameters, one or more main components that are included in the factor of the fall risk of the measured person and are based on the two or more walking parameters, and outputs an estimation result.

Abstract: A leg muscle strength estimation system includes: an obtainer that obtains an image including a user that is walking as a subject of the image; and an estimator that estimates a leg muscle strength of the user based on the obtained image.

Abstract: A detecting device obtains a first body motion signal from a body motion sensor while a person walks with a predetermined stride, the body motion sensor being attached to a body of the person and configured to detect a body motion of the person, obtains a reference-walk-waveform from a memory, the reference-walk-waveform being generated from a second body motion signal obtained from the body motion sensor while the person walks with the predetermined stride with the body motion sensor attached to an initially attached position, compares the obtained reference-walk-waveform with a waveform of the obtained first body motion signal to determine whether an attached position of the body motion sensor is shifted from the initially attached position, and outputs a determination result.

Abstract: A fall risk evaluation method in a fall risk evaluation device that evaluates a fall risk based on a walking motion of a subject includes: acquiring walking data related to walking of the subject; detecting, from the walking data, at least one walking parameter of a vertical displacement of a waist of the subject in a stance phase of one leg of the subject, a vertical displacement of the waist of the subject in a swing phase of the one leg, an angle of a knee joint of the one leg in the stance phase, and an angle of an ankle joint of one foot in the swing phase; and determining a fall risk of the subject by using at least one walking parameter.

Abstract: A cognitive function evaluation method in a cognitive function evaluation device that evaluates a cognitive function based on a walking motion of a subject includes: acquiring walking data related to walking of the subject; detecting, from the walking data, at least one of an angle of an ankle joint of one foot and an angle of a knee joint of one leg of the subject; and determining a cognitive function level of the subject using at least one of the angle of the ankle joint and the angle of the knee joint.

Abstract: A sarcopenia evaluation method in a sarcopenia evaluation device that evaluates sarcopenia based on a walking motion of a subject includes: acquiring walking data related to walking of the subject; detecting, from the walking data, at least one walking parameter of an angle of a knee joint of one leg in a stance phase of the one leg of the subject, an angle of the knee joint of the one leg in a swing phase of the one leg, a vertical displacement of a toe of one foot in the stance phase, a vertical displacement of the toe of the one foot in the swing phase, an angle of an ankle joint of the one foot in the stance phase, and an angle of the ankle joint of the one foot in the swing phase; and determining whether or not the subject has sarcopenia using at least one walking parameter.

Abstract: A lower limb muscle strength evaluation device acquires an angle of at least one of left and right lower limbs of a user while the user stands up from a sitting state, detects at least one lower limb parameter from among a first lower limb maximum value indicating the angle when the lower limb is initially inclined the most in a first direction, a lower limb minimum value indicating the angle when the lower limb is inclined the most in a second direction, a second lower limb maximum value indicating the angle when the lower limb is again inclined the most in the first direction, a first lower limb elapsed time, a second lower limb elapsed time, and a third lower limb elapsed time, evaluates a lower limb muscle strength of the user using the at least one lower limb parameter, and outputs an evaluation result.

Abstract: A detecting device obtains a first body motion signal from a body motion sensor while a person walks with a predetermined stride, the body motion sensor being attached to a body of the person and configured to detect a body motion of the person, obtains a reference-walk-waveform from a memory, the reference-walk-waveform being generated from a second body motion signal obtained from the body motion sensor while the person walks with the predetermined stride with the body motion sensor attached to an initially attached position, compares the obtained reference-walk-waveform with a waveform of the obtained first body motion signal to determine whether an attached position of the body motion sensor is shifted from the initially attached position, and outputs a determination result.