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.

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.