Conventional human activity recognition (HAR) relies on accelerometers to frequently sample human motion (acceleration). Unfortunately, power consumption of accelerometers becomes a bottleneck for realising pervasive self-powering HAR as the amount of power that can be practically harvested from the environment is very small. Instead of using accelerometer, this paper advocates the use of energy harvesting power signal as the source of HAR when motion (kinetic) energy is being harvested to power the device. The proposed use of harvested power for classifying human activities is motivated by the fact that different activities produce kinetic energy in a different way leaving their signatures in the harvested power signal. Using information theoretic analysis of experimental data, we show that many standard statistical features provide significant information gain when the kinetic power signal is used for discriminating between different activities, confirming its potential use for HAR. We have evaluated activity recognition accuracy for kinetic power signal based HAR using 14 different sets of common activities each containing between 2-10 different activities to be classified. HAR accuracies varied between 68% to 100% depending on the set of activities. The average accuracy over all activity sets is 83%, which is within 13% of what could be achieved with an accelerometer without any power constraints.
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