Energy-Efficient Real-Time Human Mobility State Classification Using Smartphones

The key benefits of using the smartphone accelerometer for human mobility analysis, with or without location determination based upon GPS, Wi-Fi or GSM is that it is energy-efficient, provides real-time contextual information and has high availability. Using measurements from an accelerometer for human mobility analysis presents its own challenges as we all carry our smartphones differently and the measurements are body placement dependent. Also it often relies on an on-demand remote data exchange for analysis and processing; which is less energy-efficient, has higher network costs and is not real-time.

We present a novel accelerometer framework based upon a probabilistic algorithm that neutralizes the effect of different smartphone on-body placements and orientations to allow human movements to be more accurately and energy-efficiently identified. Using solely the embedded smartphone accelerometer without need for referencing historical data and accelerometer noise filtering, our method can in real-time with a time constraint of 2 seconds identify the human mobility state. The method achieves an overall average classification accuracy of 92 percent when evaluated on a dataset gathered from fifteen individuals that classified nine different urban human mobility states.

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