Human Activity Recognition Method Based On Wearable Sensor Data

The study of human activity recognition has received wide attention,which may play an important role in many ways including medical,security,entertainment and military scenarios.The traditional video-based methods may be constrained due to object occlusion and light conditions,and they are not conductive to user privacy protection.With the development of microelectronics and wireless communication technology,the sensor-based recognition methods have become a research hotspot.Compared with the video-based methods,wearable sensors have high computational power,small size and low cost,allowing people to interact with the devices as part of their daily living.This will greatly motivate the development of many applications such as mobile health management and motion monitoring,and has important significance in pervasive computing.In this study,acceleration and angular velocity data are collected and stored by using wearable sensor nodes.The main work consists of the following parts:1.Only one wearable sensor node is used to recognize human lower limb activity.For the real-time requirement of activity recognition,how to improve the classification efficiency is a key problem to be solved.In this study,a fuzzy clustering algorithm is proposed to convert lots of training samples into a small number of clusters,and then the classifier is designed based on the cluster centers and membership degree function.Experimental results show that the proposed method has high recognition accuracy,compared with some common classification algorithms.Moreover,the proposed method recognizes lower limb activities based on small samples,which not only shortens the time for the classifiers to perform tasks,but also reduces the storage requirements of the system.2.Existing research on activity recognition mainly analyzes some simple basic activities,and lacks discussion on the activities which involve a combination of upper limb and lower limb movements.A hierarchical method based on neural networks is proposed to divide complex concurrent activity recognition into two stages.At the lower level,one neural network is used to classify different lower limb activities.At the upper level,the upper limb movements are modeled by using multiple neural networks,and then the specific concurrent activity is inferred.The proposed hierarchical method divides the overall classification problem into two sub-problems,which reduces the complexity of the decision boundary.The performance is superior to some single-level methods.3.In complex activity recognition,it is necessary to fully explore the correlation between activity time series data.To this end,a statistical method based on parallel hidden Markov model(PHMM)is proposed in this study,describing the continuation and transition of activity through a set of feature vector sequence.The time-domain and frequency-domain features are extracted as observation vectors,and the principle component analysis(PCA)method is used to reduce the dimension.To classify specific activities,PHMM makes the final decision by combining the probabilities of two channels.Experimental results show that,the proposed method recognizes activities with a higher accuracy,compared with some single-frame classifiers.4.Existing research on sensor-based activity recognition mainly analyzes single-user activities,and lacks discussion on two-body interactive activities.In this study,some common interactive activities in people's daily life are investigated,and the dynamic time warping(DTW)and Markov logic network(MLN)are employed to recognize them.Specifically,the DTW method is used to identifiy the actions of a single person,and the MLN is used to model interactive activities through semantics.The MLN assigns an associated weight to each rule,such that it has a certain ability to correct errors at making decisions.This effectively improves the recognition accuracy of two-body interactive activity.