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Recent advances in smart devices have sustained them as a better alternative for the design of human-machine interaction because they are equipped with accelerometer sensor, gyroscope sensor, and an advanced operating system. This paper presents a continuous hand gestures recognition technique that is capable of continuous recognition of hand gestures using threeaxis accelerometer and gyroscope sensors in a smart device. To reduce the influence of unstableness of a hand making the gesture and compress the data, a gesture coding algorithm is developed. An automatic gesture spotting algorithm is developed to detect the start and end points of meaningful gesture segments. Finally, a gesture is recognized by comparing the gesture code with gesture database using dynamic time warping algorithm. In addition, a prototype system is developed to recognize the continuous hand gestures based human-machine interaction. With the smartphone, the user is able to perform the predefined gestures and control smart appliances using the Samsung AllShare protocol.

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Humans are recognized as having a major role in influencing environmental variability and change, including their influence on the climate system. To advance scientists' understanding of the role of humans within the climate system, there remains a need to resolve which of the following three hypotheses is correct: Hypothesis 1: Human influence on climate variability and change is of minimal importance, and natural causes dominate climate variations and changes on all time scales. In coming decades, the human influence will continue to be minimal.

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