Abstract
We propose a novel active learning framework for
activity recognition using wearable sensors. Our
work is unique in that it takes physical and cognitive limitations of the oracle into account when selecting sensor data to be annotated by the oracle.
Our approach is inspired by human-beings’ limited capacity to respond to external stimulus such
as responding to a prompt on their mobile devices.
This capacity constraint is manifested not only in
the number of queries that a person can respond to
in a given time-frame but also in the lag between
the time that a query is made and when it is responded to. We introduce the notion of mindful active learning and propose a computational framework, called EMMA1
, to maximize the active learning performance taking informativeness of sensor
data, query budget, and human memory into account. We formulate this optimization problem,
propose an approach to model memory retention,
discuss complexity of the problem, and propose a
greedy heuristic to solve the problem. We demonstrate the effectiveness of our approach on three
publicly available datasets and by simulating oracles with various memory strengths. We show that
the activity recognition accuracy ranges from 21%
to 97% depending on memory strength, query budget, and difficulty of the machine learning task. Our
results also indicate that EMMA achieves an accuracy level that is, on average, 13.5% higher than
the case when only informativeness of the sensor
data is considered for active learning. Additionally,
we show that the performance of our approach is at
most 20% less than experimental upper-bound and
up to 80% higher than experimental lower-bound.
We observe that mindful active learning is most
beneficial when query budget is small and/or oracle’s memory is weak, thus emphasizing contributions of our work in human-centered mobile health
settings and for elderly with cognitive impairments