  Detecting motor activities from sensor datasets is becoming increasingly
common in a wide range of applications with the rapid commoditization of
wearable sensors. To detect activities, data scientists iteratively experiment
with different classifiers before deciding on a single model. Evaluating,
comparing, and reasoning about prediction results of alternative classifiers is
a crucial step in the process of iterative model development. However, standard
aggregate performance metrics (such as accuracy score) and textual display of
individual event sequences have limited granularity and scalability to
effectively perform this critical step.
  To ameliorate these limitations, we introduce Track Xplorer, an interactive
visualization system to query, analyze and compare the classification output of
activity detection in multi-sensor data. Track Xplorer visualizes the results
of different classifiers as well as the ground truth labels and the video of
activities as temporally-aligned linear tracks. Through coordinated track
visualizations, Track Xplorer enables users to interactively explore and
compare the results of different classifiers, assess their accuracy with
respect to the ground truth labels and video. Users can brush arbitrary regions
of any classifier track, zoom in and out with ease, and playback the
corresponding video segment to contextualize the performance of the classifier
within the selected region.
  Track Xplorer also contributes an algebra over track representations to
filter, compose, and compare classification outputs, enabling users to
effectively reason about the performance of classifiers. We demonstrate how our
tool helps data scientists debug misclassifications and improve the prediction
performance in developing activity classifiers for real-world, multi-sensor
data gathered from Parkinson's patients.
