Learning Based Falling Detection Using Multiple Doppler Sensors

Automated falling detection is one of the important tasks in this ageing society. Such systems are supposed to have little interference on daily life. Doppler sensors have come to the front as useful?devices to detect human activity without using any wearable sensors. The conventional Doppler sensor based falling detection mechanism uses the features of only one sensor. This paper presents falling detection using multiple Doppler sensors. The resulting data from sensors are combined or selected to find out the falling event. The combination method, using three sensors, shows 95.5% accuracy of falling detection. Moreover, this method compensates the drawbacks of mono Doppler sensor which encounters problems when detecting movement orthogonal to irradiation directions.

Multi-Class Sentiment Analysis on Twitter: Classification Performance and Challenges

Sentiment analysis refers to the automatic collection,aggregation,and classification of data collected online into different emotion classes.While most of the work related to sentiment analysis of texts focuses on the binary and ternary classification of these data,the task of multi-class classification has received less attention.Multi-class classification has always been a challenging task given the complexity of natural languages and the difficulty of understanding and mathematically"quantifying"how humans express their feelings.In this paper,we study the task of multi-class classification of online posts of Twitter users,and show how far it is possible to go with the classification,and the limitations and difficulties of this task.The proposed approach of multi-class classification achieves an accuracy of 60.2%for 7 different sentiment classes which,compared to an accuracy of 81.3%for binary classification,emphasizes the effect of having multiple classes on the classification performance.Nonetheless,we propose a novel model to represent the different sentiments and show how this model helps to understand how sentiments are related.The model is then used to analyze the challenges that multi-class classification presents and to highlight possible future enhancements to multi-class classification accuracy.