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Several smart phone connected devices are becoming popular as replacements for pedometers for monitoring human activity and exercise levels. Most of them, including Fitbit and Nike Fuel Band measure step cadence using accelerometers. It would be advantageous to obtain this data using smart phone sensors without additional devices but smart phones produce varying accelerometer signal patterns when carried in different locations on the body. Magnetometers are an alternative low power sensor on most smart phones that could potentially be used for cadence estimation. We provide an algorithm that derives walking and running cadence from magnetometer readings that is robust to the location on the body that the smart phone is carried. The algorithm has been tested with data gathered from twenty one subjects while walking and running, in several body locations that should comprehensively represent body movements. A high accuracy was achieved when the estimated cadence was verified against an accelerometer worn on the subject's thigh. The algorithm is robust in dealing with sudden changes in direction while walking or running which is also likely to make it robust against magnetic field fluctuations that are common in urban environments.

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