With the rise of wearable devices, which integrate myriad of health-care and fitness procedures into daily life, a reliable method for measuring various bio-signals in a daily setup is more desired than ever. Many of these physiological parameters, such as Heart rate (HR) and Respiratory Rate (RR), are extracted indirectly and using other signals such as Photoplethysmograph (PPG). Part of the reason is that in some cases, such as RR measurements, the devices which directly measure them are cumbersome to wear and thus, rather impractical. On the other hand, signals, such as PPG from which the RR can be extracted, are not very clean. This poses a challenge on reliable extraction of these metrics. The most important problem is that they are corrupted by motion artifacts. In this paper, we review the state of the art algorithms which are used to detect and filter motion artifacts in PPG signals and compare them in terms of their performance. The insight provided by this paper can help the scientists and engineers to obtain a better understanding of the field and be able to use the most suitable technique for their work, or come up with innovative solutions based on existing ones.

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Recently, it has become easier and more common to measure physiological signals through wearable devices such as smart watches. Extracting emotional states of individuals with problems expressing it, such as autistic individuals, can help their parents, friends, and therapists to obtain a better understanding of what they feel throughout their day. Although emotion recognition methods based on physiological signals have been studied for many years, there is a smaller body of literature about systems working with data obtained from wearable devices. In this paper, we present an emotion recognition system with a small footprint suitable for limited resources of wearable devices. Other than identifying the emotions (with a success rate of 65%), The proposed system also tags each recognition with a confidence value (on average 57%).

Abstract-Observation plays a crucial role in self-awareness. In many scenarios, such as the Observe-Decide-Act (ODA) loops, self-awareness is founded upon observations of the system. In other words, observation generates the understanding of the system from the status and behavior of its self and its environment. Although recently more focus has been put on comprehensive and competent observations, we believe that further attention and work is due, especially in the field of cyberphysical systems. Hence, in this paper, we discuss our position on various aspects of observation methods. In a short list, the major aspects are Abstraction, Disambiguation, Desirability, Relevance, Data Reliability, Confidence, Attention, and History. We elaborate and anticipate the potential of these factors in improving the quality of the observation of the system, decreasing the processing load of higher layers, increasing the reliability of decisions, and consequently the overall performance of the system. To put these aspects into perspective, we elaborate them in the context of their potentials in our emotion recognition system under development.

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