Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been propos...Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been proposed.However, the recognition rate is relatively low. In this paper, we apply back propagation(BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude(SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.展开更多
Wireless Body Area Networks(WBANs) are expected to achieve high reliable communications among a large number of sensors.The outage probability can be used to measure the reliability of the WBAN.In this paper,we optimi...Wireless Body Area Networks(WBANs) are expected to achieve high reliable communications among a large number of sensors.The outage probability can be used to measure the reliability of the WBAN.In this paper,we optimize the outage probability with the harvested energy as constraints.Firstly,the optimal transmit power of the sensor is obtained while considering a single link between an access point(AP) located on the waist and a sensor attached on the wrist over the Rayleigh fading channel.Secondly,an optimization problem is formed to minimize the outage probability.Finally,we convert the non-convex optimization problem into convex solved by the Lagrange multiplier method.Simulations show that the optimization problem is solvable.The outage probability is optimized by performing power allocation at the sensor.And our proposed algorithm achieves minimizing the outage probability when the sensor uses energy harvesting.We also demonstrate that the average outage probability is reduced with the increase of the harvested energy.展开更多
基金supported by the National Natural Science Foundation of China(No.61074165 and No.61273064)Jilin Provincial Science&Technology Department Key Scientific and Technological Project(No.20140204034GX)Jilin Province Development and Reform Commission Project(No.2015Y043)
文摘Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been proposed.However, the recognition rate is relatively low. In this paper, we apply back propagation(BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude(SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.
文摘Wireless Body Area Networks(WBANs) are expected to achieve high reliable communications among a large number of sensors.The outage probability can be used to measure the reliability of the WBAN.In this paper,we optimize the outage probability with the harvested energy as constraints.Firstly,the optimal transmit power of the sensor is obtained while considering a single link between an access point(AP) located on the waist and a sensor attached on the wrist over the Rayleigh fading channel.Secondly,an optimization problem is formed to minimize the outage probability.Finally,we convert the non-convex optimization problem into convex solved by the Lagrange multiplier method.Simulations show that the optimization problem is solvable.The outage probability is optimized by performing power allocation at the sensor.And our proposed algorithm achieves minimizing the outage probability when the sensor uses energy harvesting.We also demonstrate that the average outage probability is reduced with the increase of the harvested energy.
