Abstract: An apparatus for estimating an anaerobic threshold may include a heart rate detector configured to detect a heart rate from a signal sensed from a user, and an anaerobic threshold estimator configured to estimate an anaerobic threshold of the user based on a change in the detected heart rate.

Abstract: A one repetition maximum (1RM) estimating apparatus includes a potential signal receiving unit configured to receive a potential signal of a muscle from a sensor attached to a user, and a 1RM estimating unit configured to estimate a 1RM of the user based on the potential signal.

Abstract: A method of calculating an amount of exercise performed includes measuring noise based on a relative difference in displacement between a skin of a user and a sensor attached to the skin of the user, and determining a number of steps taken by the user based on the measured noise.

Abstract: A method of controlling a threshold for detecting peaks of physiological signals includes: obtaining a physiological signal measured from a person being examined; determining whether a peak of the physiological signals is detected based on a result of comparing the physiological signals with a threshold; and controlling the threshold based on a minimum threshold and either the threshold or a feature value of the detected peak based on a result of the determining. When a threshold for detecting peaks of physiological signals is controlled, even if an interval between the peaks is irregular or there is a large difference in values of the peaks, the peaks can be accurately detected.

Abstract: A method of classifying an input pattern and a pattern classification apparatus are provided. The method includes enabling an artificial neural network to learn based on learning input data received by an input layer of the artificial neural network, determining classification of an input pattern received by the input layer of the enabled artificial neural network according to an output value obtained from an output layer of the artificial neural network, the obtained output value being based on the input pattern, updating connection intensities of a plurality of connection lines of the enabled artificial neural network to output a result value indicating the determined classification from the output layer when the input pattern, and determining updated classification of the input pattern according to an updated output value obtained from an output layer of the updated artificial neural network, the obtained updated output value being based on the input pattern.