Assefaw Gebremedhin, Papers on Mobile computing
Co-MEAL: Cost-Optimal Multi-Expert Active Learning Architecture for Mobile Health Monitoring
R. Saeedi, K. Sasani and A.H. Gebremedhin
Mobile health monitoring plays a central role in a variety of health-care applications.
Using mobile technology, health-care providers can access clinical information and
communicate with subjects in real-time.
Due to the sensitive nature of health-care applications, these systems need to
process physiological signals highly accurately.
However, as mobile devices are employed in dynamic environments,
the accuracy of a machine learning model drops whenever a change
in configuration of the system occurs.
Therefore, data mining and machine learning techniques that
specifically address challenges associated with dynamic environments
(e.g. different users, signal heterogeneity) are needed.
In this paper, using active learning as an organizing principle,
we propose a cost-optimal multiple-expert architecture to adapt
a machine learning model (e.g. classifier) developed in a given context
to a new context or configuration. More specifically, in our architecture,
a system's machine learning model learns from experts available to the system
(e.g. another mobile device, human annotator)
while minimizing the cost of data labeling.
Our architecture also exploits collaboration between experts
to enrich their knowledge which in turn decreases both cost and
uncertainty of data labeling in future steps.
We demonstrate the efficacy of the architecture using a publicly
available dataset on human activity. We show that
the accuracy of activity recognition reaches over
85% by labeling only 15% of unlabeled data.
At the same time, the number of queries from human expert is reduced by up to 82%.
Read full paper
Transfer Learning Algorithms for Autonomous Configuration of Wearable Systems
R. Saeedi, H. Ghasemzadeh and A.H. Gebremedhin
Status: IEEE BigData 2016.
Wearables have emerged as a revolutionary technology in many application domains including healthcare and fitness. Machine learning algorithms, which form the core intelligence of wearables, traditionally deduce a computational model from a set of training examples to detect events of interest (e.g. activity type). However, in the dynamic environment in which wearables typically operate in, the accuracy of a computational model drops whenever changes in configuration of the system (such as device type and sensor orientation) occur. Therefore, there is a need to develop systems which can adapt to the new configuration autonomously. In this paper, using transfer learning as an organizing principle, we develop several algorithms for data mapping. The data mapping algorithms employ effective
signal similarity methods and are used to adapt the system to the new configuration. We demonstrate the efficacy of the data mapping algorithms using a publicly available dataset on human activity recognition.
Read full paper