  With the rapid commoditization of wearable sensors, detecting human movements
from sensor datasets has become increasingly common over a wide range of
applications. To detect activities, data scientists iteratively experiment with
different classifiers before deciding which model to deploy. Effective
reasoning about and comparison of alternative classifiers are crucial in
successful model development. This is, however, inherently difficult in
developing classifiers for sensor data, where the intricacy of long temporal
sequences, high prediction frequency, and imprecise labeling make standard
evaluation methods relatively ineffective and even misleading. We introduce
Track Xplorer, an interactive visualization system to query, analyze, and
compare the predictions of sensor-data classifiers. Track Xplorer enables users
to interactively explore and compare the results of different classifiers, and
assess their accuracy with respect to the ground-truth labels and video.
Through integration with a version control system, Track Xplorer supports
tracking of models and their parameters without additional workload on model
developers. Track Xplorer also contributes an extensible algebra over track
representations to filter, compose, and compare classification outputs,
enabling users to reason effectively about classifier performance. We apply
Track Xplorer in a collaborative project to develop classifiers to detect
movements from multisensor data gathered from Parkinson's disease patients. We
demonstrate how Track Xplorer helps identify early on possible systemic data
errors, effectively track and compare the results of different classifiers, and
reason about and pinpoint the causes of misclassifications.
