  Conventional systems for motion context detection rely on batteries to
provide the energy required for sampling a motion sensor. Batteries, however,
have limited capacity and, once depleted, have to be replaced or recharged.
Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration
into usable electricity and can enable batteryless, maintenance free operation
of motion sensors. The signal from a KEH transducer correlates with the
underlying motion and may thus directly be used for context detection, saving
space, cost and energy by omitting the accelerometer. Previous work uses the
open circuit or the capacitor voltage for sensing without using the harvested
energy to power a load. In this paper, we propose to use other sensing points
in the KEH circuit that offer information rich sensing signals while the energy
from the harvester is used to power a load. We systematically analyse multiple
sensing signals available in different KEH architectures and compare their
performance in a transport mode detection case study. To this end, we develop
four hardware prototypes, conduct an extensive measurement campaign and use the
data to train and evaluate different classifiers. We show that sensing the
harvesting current signal from a transducer can be energy positive, delivering
up to ten times as much power as it consumes for signal acquisition, while
offering comparable detection accuracy to the accelerometer signal for most of
the considered transport modes.
