In this paper we comprehensively survey the concept and strategies for building a resilient and integrated cyber-physical system (CPS). Here resilience refers to a 3S-oriented design, that is, stability, security, and systematicness: Stability means the CPS can achieve a stable sensing-actuation close-loop control even though the inputs (sensing data) have noise or attacks; Security means that the system can overcome the cyber-physical interaction attacks; and Systematicness means that the system has a seamless integration of sensors and actuators. We will also explain the CPS modeling issues since they serve as the basics of 3S design. We will use two detailed examples from our achieved projects to explain how to achieve a robust, systematic CPS design: Case study 1 is on the design of a rehabilitation system with cyber (sensors) and physical (robots) integration. Case Study 2 is on the implantable medical device design. It illustrates the nature of CPS security principle. The dominant feature of this survey is that it has both principle discussions and practical cyber-physical coupling design. 1
Common methods for monitoring of cigarette smoking, such as portable puff-topography instruments or self-report questionnaires, tend to be biased due to conscious or unconscious underreporting. Additionally, these methods may change the natural smoking behavior of individuals. Our long term objective is the development of a wearable non-invasive monitoring system (Personal Automatic Cigarette Tracker – PACT) to reliably monitor cigarette smoking behavior under free living conditions. PACT monitors smoking by observing characteristic breathing patterns of smoke inhalations that follow a cigarette-to-mouth hand gesture. As envisioned, PACT does not rely on self-report or require any conscious effort from the user. A major element of the PACT is a proximity sensor that detects typical cigarette-to-mouth gesture during cigarette smoking. This study describes the design and validation of a prototype RF proximity sensor that captures hand-to-mouth gestures with a high sensitivity (0.90), and a methodology that can reject up to 68% of artifacts gestures originating from activities other than cigarette smoking.
Cigarette smoking is a serious risk factor for cancer, cardiovascular, and pulmonary diseases. Current methods of monitoring of cigarette smoking habits rely on various forms of self-report that are prone to errors and under reporting. This paper presents a first step in the development of a methodology for accurate and objective assessment of smoking using noninvasive wearable sensors (Personal Automatic Cigarette Tracker-PACT) by demonstrating feasibility of automatic recognition of smoke inhalations from signals arising from continuous monitoring of breathing and hand-to-mouth gestures by support vector machine classifiers. The performance of subject-dependent (individually calibrated) models was compared to performance of subject-independent (group) classification models. The models were trained and validated on a dataset collected from 20 subjects performing 12 different activities representative of everyday living (total duration 19.5 h or 21,411 breath cycles). Precision and recall were used as the accuracy metrics. Group models obtained 87% and 80% of average precision and recall, respectively. Individual models resulted in 90% of average precision and recall, indicating a significant presence of individual traits in signal patterns. These results suggest the feasibility of monitoring cigarette smoking by means of a wearable and noninvasive sensor system in free living conditions.
A combination of wearable Respiratory Inductive Plethysmograph and a hand-to-mouth Proximity Sensor (PS) can be used to monitor smoking habits and smoke exposure in cigarette smokers. In our previous work, detection of smoke inhalations was achieved by using a Support Vector Machine (SVM) classifier applied to raw sensor signals with 1503-element feature vectors. This study uses empirically-defined 27 features computed from the sensor signals to reduce the length of vectors. Further reduction in the length of the feature vectors was achieved by a forward feature selection algorithm, identifying from 2 to 16 features most critical for smoke inhalations detection. For individual detection models, the 1503-element feature vectors, 27-element feature vectors and reduced feature vectors resulted in F-scores of 90.1%, 68.7% and 94% respectively. For the group models, F-scores were 81.3%, 65% and 67% respectively. These results demonstrate feasibility of detecting smoke inhalations with a computed feature set, but suggest high individuality of breathing patterns associated with smoking.
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