Abstract: A proposing an improvement in an installation environment of a weighing apparatus is made by including (a) a step of acquiring, weighed data of an object (7) to be weighed from a weighing sensor (13) together with time information, (b) a step of acquiring, environmental data together with time information, (c) a step of checking, with respect to the weighed data and the environmental data, whether an amount of change of the data per unit time or an average value of the data is less than or not less than a threshold with reference to a threshold database (191) and determining influence on the apparatus, and (d) a step of making a proposal on the installation environment for each result of the determination with reference to an improvement proposal database (193).

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: The purpose of the invention is to provide an information processing device including a first reception unit which receives from a plurality of secondary battery management systems, charge/discharge data of secondary batteries managed by the secondary battery management systems, and an estimation model generation unit which generates an estimation model for estimating a degradation indicator indicating a state of degradation of the secondary battery based on the charge/discharge data.

Abstract: An inclination of a weighing apparatus is automatically detected by the apparatus itself while preventing an increase in the number of components. In order to achieve the object described above, a weighing apparatus includes a weight sensor, a built-in weight to be loaded on the weight sensor, an adding/removing unit for adding/removing the built-in weight, a memory storing a theoretical value of the built-in weight, and an arithmetic processing unit, wherein the arithmetic processing unit includes an inclination angle computing unit configured to obtain an apparatus inclination angle from an arc-cosine of a weighing value of the built-in weight and the theoretical value of the built-in weight.

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 proposing an improvement in an installation environment of a weighing apparatus is made by including (a) a step of acquiring, weighed data of an object (7) to be weighed from a weighing sensor (13) together with time information, (b) a step of acquiring, environmental data together with time information, (c) a step of checking, with respect to the weighed data and the environmental data, whether an amount of change of the data per unit time or an average value of the data is less than or not less than a threshold with reference to a threshold database (191) and determining influence on the apparatus, and (d) a step of making a proposal on the installation environment for each result of the determination with reference to an improvement proposal database (193).

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 falling asleep prediction device includes a biological data acquisition unit that acquires biological information on a subject, a falling asleep prediction unit that predicts that the subject falls asleep after predetermined time by using the biological information, and a notification processing unit that notifies that the subject falls asleep after the predetermined time.

Abstract: A learning apparatus includes at least one memory and at least one circuit. The circuit (a) obtains a first neural network that has learned by using source learning data and obtains target learning data, the target learning data including a plurality of first data items each of which is given a first label and a plurality of second data items each of which is given a second label, (b) obtains a plurality of first output vectors by inputting the plurality of first data items to a second neural network and obtains a plurality of second output vectors by inputting the plurality of second data items to the second neural network, and (c) generates a first relation vector corresponding to the first label by using the plurality of first output vectors and generates a second relation vector corresponding to the second label by using the plurality of second output vectors.

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 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: An apparatus generates motor function estimation information by performing a process including calculating, using a sensor value of a subject, a feature vector corresponding to a feature value of a feature in a time segment, acquiring a first weight vector using the feature vector and a motor ability value of the subject, calculating a gradient vector with respect to the feature vector, determining a new time segment in the predetermined time period and a new feature value based on the new time segment, calculating, using the sensor value, a feature candidate vector corresponding to a feature value of the new feature in the new time segment, determining a feature candidate vector satisfying a predetermined condition associated with a gradient vector based on a difference between the feature candidate vector and the feature vector, and correcting the first weight vector to a second weight vector using the feature candidate vector.

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: An anomaly detection method includes generating second data by adding normal noise to first data; generating third data by adding abnormal noise to the first data; inputting the first data, the second data, and the third data to a neural network; calculating a first normal score, a second normal score, and a third normal score; calculating a third difference based on a first difference and a second difference, the first difference being based on a difference between the first normal score and the second normal score, the second difference being based on a difference between the first normal score and the third normal score; changing the neural network so that the third difference becomes smallest; inputting, to the changed neural network, fourth data that is unknown in terms of whether the fourth data is normal or abnormal; and determining whether the fourth data is normal or abnormal.

Abstract: A falling asleep prediction device includes a biological data acquisition unit that acquires biological information on a subject, a falling asleep prediction unit that predicts that the subject falls asleep after predetermined time by using the biological information, and a notification processing unit that notifies that the subject falls asleep after the predetermined time.

Abstract: An exercise test evaluation system includes an acceleration sensor that is worn by a user during an exercise test and acquires acceleration values of a foot of the user, a heart rate sensor that is worn by the user and measures a heart rate of the user, and a processor. The processor acquires a first maximum acceleration value and a second maximum acceleration value, calculates a reliability score using the first and second maximum acceleration values, acquires a walking velocity and a heart rate of the user and estimates a maximum oxygen intake amount using the user's walking velocity and heart rate if the reliability score is equal to or higher than a specific threshold value, and estimates the maximum oxygen intake amount using the user's walking velocity if the reliability score is lower than the specific threshold value.

Abstract: A graphite powder, preferably including scale-like particles, which satisfies the following formulae (1) and (2), wherein e(0.5) represents the initial charge-discharge efficiency of a coin cell fabricated from an electrode (work electrode) produced by compressing an electrode material employing graphite powder as an active material under a pressure of 0.5 t/cm2, a lithium metal counter electrode, a separator and an electrolytic solution; and e(3) represents the initial charge-discharge efficiency of a coin cell fabricated from an electrode (work electrode) produced by compressing an electrode material employing graphite powder as an active material under a pressure of 3 t/cm2, a lithium metal counter electrode, a separator and an electrolytic solution: e(3)(%)?e(0.5)(%)?1,??formula (1): e(3)(%)>85.??formula (2): Also disclosed is a method of producing the graphite powder; a graphite material for a battery electrode; an electrode for a lithium ion; and a lithium-ion secondary battery.