A sliding window‐based false‐negative approach for ubiquitous data stream analysis

Summary Heart failure is a chronic disease that alternates intense and weak phases and requires repeated and frequent hospital treatments. The use of automatic instruments for a remote and ubiquitous monitoring of biological parameters relevant to heart failure pathophysiology offers new perspectives to improve the patients' quality of life and the efficacy of the treatment. The platform described in this paper represents an implementation of an automatic remote monitoring tool where smartphones play a crucial role. They are not employed just to communicate through traditional client‐server applications and they are not only the hub of information sent by sensors, but they also act as intelligent processors and autonomous sensors of the patients' motion through a high‐accuracy activity recognition algorithm. The proposed platform combines the evolution of health systems and the consequent needs of modern telemedicine with the current context‐aware capability of recent smartphones, obtained by implementing specific algorithmic solutions, and with the anything, anytime, and anywhere communication capability of the pervasive communications paradigm. Performance evaluation focuses on the accuracy of the activity recognition estimation, on its applicability to real life, and on the data exchange between smartphones and medical server, allowing to envisage the potential of the designed platform. Copyright © 2014 John Wiley & Sons, Ltd.

Section: Physical Activity Detectionmentioning

confidence: 99%

A smartphone‐centric platform for remote health monitoring of heart failure

Bisio

Lavagetto

Marchese

et al. 2014

“…The default setting of sampling rate is 100 Hz in the app. Therefore we set the width of sliding windows is 1.5 s and 50% overlap []. To cancel the effect of accelerometer orientation, we extract features from two signals based on the transformed accelerometer data: the vertical acceleration, a v ( t ) = a Z ( t ) and the horizontal acceleration magnitude,

a_{h} ((), t) = \sqrt{a_{X}^{2} ((), t) + a_{Y}^{2} ((), t)}

.…”

Section: Process Of Activity Recognitionmentioning

confidence: 99%

Human activity recognition based on transformed accelerometer data from a mobile phone

Xia

Wang

2014

Summary Mobile phones are equipped with a rich set of sensors, such as accelerometers, magnetometers, gyroscopes, photometers, orientation sensors, and gravity sensors. These sensors can be used for human activity recognition in the ubiquitous computing domain. Most of reported studies consider acceleration signals that are collected from a known fixed device location and orientation. This paper describes how more accurate results of basic activity recognition can be achieved with transformed accelerometer data. Based on the rotation matrix (Euler Angle Conversion) derived from the orientation angles of gyroscopes and orientation sensors, we transform input signals into a reference coordinate system. The advantage of the transformation is that it allows activity classification and recognition to be carried out independent of the orientation of sensors. We consider five user activities: staying, walking, running, ascending stairs, and descending stairs, with a phone being placed in the subject's hand, or in pants pocket, or in a handbag. The results show that an overall orientation independent accuracy of 84.77% is achieved, which is a improvement of 17.26% over those classifications without input transformation. Copyright © 2014 John Wiley & Sons, Ltd.

“…They modeled the multi-hop wireless networks as classic G/G/1 queuing networks. It was observed that data requirements in WMNs are mainly for Internet accesses [20,[23][24][25][26][27][28][29][30][31][32][33][34][35]. Jamieson and Balakrishnan [6] modeled gateway nodes as independent M/D/1 queue stations.…”

Section: Related Workmentioning

confidence: 99%

“…When s is equal to 0, it represents a gateway. Let N(s) denote the number of s-hop nodes, and let r(s) be the ratio between N(s) and N. We have the classic GI/Geom/1 discrete time queue [22][23][24][25][26][27][28][29][30][31][32][33][34][35], and then we insert the strategies of the exhaustive service, with booting procedure and closing procedure; namely users reach the special time as follows: t = n À , n = 0, 1, 2, . .…”

Section: Network Modelmentioning

confidence: 99%

GI/Geom/1 queue based on communication model for mesh networks

Wei

Wang

et al. 2013

127

SUMMARYIn mesh networks architecture, it should be permitted to visit the mobile client points. Whereas in mesh networks environment, the main throughput flows usually communicate with the conventional wired network. The so-called gateway nodes can link directly to traditional Ethernet, depending on these mesh nodes, and can obtain access to data sources that are related to the Ethernet. In wireless mesh networks (WMNs), the quantities of gateways are limited. The packet-processing ability of settled wireless nodes is limited. Consequently, throughput loads of mesh nodes highly affect the network performance. In this paper, we propose a queuing system that relied on traffic model for WMNs. On the basis of the intelligent adaptivenes, the model considers the influences of interference. Using this intelligent model, service stations with boundless capacity are defined as between gateway and common nodes based on the largest hop count from the gateways, whereas the other nodes are modeled as service stations with certain capacity. Afterwards, we analyze the network throughput, mean packet loss ratio, and packet delay on each hop node with the adaptive model proposed. Simulations show that the intelligent and adaptive model presented is precise in modeling the features of traffic loads in WMNs.