Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.

Human activity recognition (HAR) is a hot research topic which aims to understand human behavior and can be applied in various applications. However, transitions between activities are usually disregarded due to their low incidence and short duration when compared against other activities, while in fact, transitions can affect the performance of the recognition system if not dealt with properly. In this paper, we propose and implement a systematic human activity recognition method to recognize basic activities (BA) and transitional activities (TA) in a continuous sensor data stream. First, raw sensor data are segmented into fragments with sliding window and the features are constructed based on window segmentation. Then, cluster analysis with K-Means is used to aggregate activity fragments into periods. Next, generally, realize the classification of BA and TA according to the shortest duration of the BA, and then deal with the hidden phenomenon of BA. Third, the fragments between adjacent BA are evaluated to decide whether they are TA or disturbance process. Finally, random forest classifier is used to accurately recognize BA and TA. The proposed method is evaluated on the public dataset SBHAR. The results demonstrate that our method effectively recognizes different activities and can deliver high accuracy with all activities considered. INDEX TERMS Human activity recognition, transitional activities, period extraction, cluster analysis. I. INTRODUCTIONHuman activity recognition (HAR) detects, interprets and identifies human behavior, activity types and patterns, either habitual or occasional. With its potential applications spanning various areas such as smart home, healthcare, nursing, and athletics, it could aid transforming our daily life to be smarter, safer and more convenient [1]-[4].HAR methods can be divided into two main categories: vision-based and sensor-based [5]-[10]. However, many limitations exist in vision-based methods, especially when applied in practical scenarios. For example, the use of cameras is constrained by various factors such as illumination, location, angle, potential obstruction, and privacy invasion concerns, which are far from ideal in many scenarios [8], [9].The associate editor coordinating the review of this manuscript and approving it for publication was Dong Wang.

Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

Dance emotion recognition technology is of great significance for the digitalization, virtual performance, inheritance and protection of folk dance. Based on the mechanism that emotion expression in dance performance can be fully expressed through the strength and rhythm of dance movements, a novel dance emotion expression method is proposed to train hybrid deep learning neural network, to effectively identify the seven basic dance emotions of fear, anger, boredom, excitement, joy, relaxation and sadness. First, in order to fully express the emotions contained in the dance movements, this paper defines a dance emotion expression method through Laban Movement Analysis (LMA) method, which includes the characteristic parameters of the three aspects of body structure, spatial orientation and force effect, and converts the original dance movement data into three characteristic expression parameters to obtain dance emotion data. Then, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) hybrid neural network models are used to test and train dance emotion data. Finally, in order to verify the applicability of the CNN-LSTM model, decision tree, random forest, CNN and LSTM are established and compared for accuracy. The results show that it is feasible to identify dance emotion from the perspective of dance movement, and the CNN-LSTM model is of high accuracy.

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