Abstract: A mobile device includes a pedometer function that calculates an estimated distance based on sensor measurements. The sensor measurements are used to determine a step frequency of the user, which is used to estimate the distance traveled by the user. To correct for the unique step frequency of the user, a calibration factor is calculated that can be multiplied by the estimated distance to improve the accuracy of the distance estimate. New calibration factors resulting from calibration trials are assigned to step frequency bands. An average calibration factor is calculated for each frequency band. The average calibration factors of the bands are updated to ensure that the average calibration factor of any given band is greater than or equal to the average calibration factors in all lower bands.

Abstract: In some implementations, optimizations for detecting steps when a pedometer is worn at a user's wrist are described. In some implementations, a threshold crossing step detection method can be enhanced for wrist locations by counting the number of positive peaks between comparison threshold crossings, adjusting a minimum peak-to-peak threshold for qualifying threshold crossings, and inferring a second step based on the amount of time between threshold crossings. In some implementations, the pedometer can automatically determine that the pedometer is being worn on a user's wrist.

Abstract: Techniques are provided for improving pedometer readings. In some embodiments, motion data, such as acceleration data is detected, and a magnitude of the acceleration data, referred to as the modulus, is processed. The modulus is used to detect walking steps and running steps during a measurement. In some embodiments, a distance ran is calculated based on the detected running steps and an estimated running stride length, and a distance walked is calculated based on the detected walking steps and an estimated walking stride length. The estimated running and walking stride lengths are calculated based on various parameters associated with the acceleration data, population data, and user-specific data. Expended calories may be estimated based on the distance ran and walked. The distance analysis process further includes calibration techniques, including, for example, least squares simple regression, least squares multiple regression, and K-factor analysis.

Abstract: Techniques are provided for improving pedometer readings. In some embodiments, motion data, such as acceleration data is detected, and a magnitude of the acceleration data, referred to as the modulus, is processed for improved step detection. Techniques involve computing a moving average of the modulus and applying an acceleration threshold filter to the modulus. Crossings are detected based on peak-to-peak swing of the modulus about the moving average. In some embodiments, the frequency of the modulus is used in an adaptive filtering technique. based on the dominant frequency of the modulus, a frequency band is selected to filter to modulus. The frequency band may be dynamically changed to one of several frequency bands when a significant frequency change is detected in the dominant frequency of the modulus. In some embodiments, steps are detected based on the acceleration threshold-filtered and the frequency-filtered modulus.

Abstract: Techniques are provided for improving pedometer readings. In some embodiments, motion data, such as acceleration data is detected, and a magnitude of the acceleration data, referred to as the modulus, is processed. Techniques involve performing frequency analysis on the modulus. In some embodiments, the modulus samples are counted perform frequency analysis on each complete sample set. Each complete sample set includes overlapping samples with the previous sample set. A suitable frequency analysis algorithm, such as fast Fourier transform (FFT) dominant frequency computation is performed on each sample set to determine the dominant frequency of the modulus. The dominant frequency of the modulus may be smoothed in some embodiments to remove irregularities such as spikes or periods of inactivity. In some embodiments, results of frequency analysis may be used to determine whether a detected step is a walking step or a running step.