The combination of the SA advice and monitoring of step counts is an inexpensive treatment to maintain or even improve daily activity, instead of embracing a pain avoidance attitude, which commonly has been observed among general practitioners [ 17– 20].
We propose an instrumented shoe insole to provide such monitoring (number of steps, distance covered and gait speed).
That said, the work of Hundeshagen and colleagues [ 5] is clearly a step forward in quantitative cell population based monitoring of distinct steps of the autophagy pathway in the screening for autophagy modulators.
However, these assays are restricted to individual steps of the autophagic pathway and do not allow for concurrent monitoring of multiple steps within the autophagic/endolysosomal process.
Only careful monitoring of these steps allows for the identification of potential shortcomings in the screening program, as well as areas for improvement [ 6].
As with any programmatic effort, improvements result from careful and continuous monitoring of key steps in the process, the assessment of that information, and the introduction of changes that continuously improve program performance.
In response to this, we propose the architecture of an engineering system called OMMA, Operator-guided Monitoring of Multi-step Attacks, for integration of multi-step attack detection methods working with heterogeneous sets of events.
Fig. 4 Global architecture of OMMA (Operator-guided Monitoring of Multi-step Attacks).
As explicated in Section 2, multimodal monitoring of basic step models and real-time performances is an important component of the Dance-the-Music.
In this paper, we propose OMMA (Operator-guided Monitoring of Multi-step Attacks), an open and collaborative engineering system which offers a platform to integrate the methods developed by the multi-step attack detection research community.
