Advances in embedded systems have enabled integration of many lightweight sensory devices within our daily life. In particular, this trend has given rise to continuous expansion of wearable sensors in a broad range of applications from health and fitness monitoring to social networking and military surveillance. Wearables leverage machine learning techniques to profile behavioral routine of their end-users through activity recognition algorithms. Current research assumes that such machine learning algorithms are trained offline. In reality, however, wearables demand continuous reconfiguration of their computational algorithms due to their highly dynamic operation. Developing a personalized and adaptive machine learning model requires real-time reconfiguration of the model. Due to stringent computation and memory constraints of these embedded sensors, the training/re-training of the computational algorithms need to be memory- and computation-efficient. In this paper, we propose a framework, based on the notion of online learning, for real-time and on-device machine learning training. We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers. Our results, based on Pegasos online learning, demonstrate that the proposed approach achieves 97% accuracy in detecting activities of varying intensities using a limited memory while power usages of the system is reduced by more than 40%.
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Model Predictive Real-Time Monitoring of Linear Systems
The predictive monitoring problem asks whether a deployed system is likely to fail over the next T seconds under some environmental conditions. This problem is of the utmost importance for cyber-physical systems, and has inspired real-time architectures capable of adapting to such failures upon forewarning. In this paper, we present a linear model-predictive scheme for the real-time monitoring of linear systems governed by time-triggered controllers and time-varying disturbances. The scheme uses a combination of offline (advance) and online computations to decide if a given plant model has entered a state from which no matter what control is applied, the disturbance has a strategy to drive the system to an unsafe region. Our approach is independent of the control strategy used: this allows us to deal with plants that are controlled using model-predictive control techniques or even opaque machine-learning based control algorithms that are hard to reason with using existing reachable set estimation algorithms. Our online computation reuses the symbolic reachable sets computed offline. The real-time monitor instantiates the reachable set with a concrete state estimate, and repeatedly performs emptiness checks with respect to a safety property. We classify the various alarms raised by our approach in terms of what they imply about the system as a whole. We implement our real-time monitoring approach over numerous linear system benchmarks and show that the computation can be performed rapidly in practice. Furthermore, we also examine the alarms reported by our approach and show how some of the alarms can be used to improve the controller.
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- Award ID(s):
- 1646556
- PAR ID:
- 10078977
- Date Published:
- Journal Name:
- 2017 IEEE Real-Time Systems Symposium (RTSS)
- Volume:
- 1
- Page Range / eLocation ID:
- 297 to 306
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RTSS.2017.00035
