Real time patient's gait monitoring through wireless accelerometers with the wavelet transform

Abstract

Gait analysis through on-body wireless accelerometers can provide valuable information for multiple health-related applications. The dynamic nature of human body acceleration signals makes their analysis with the wavelet transform optimum. Nevertheless, one of the main issues for the practical development of this signal processing tool in real time is the difficulty in the selection of the appropriate scale and mother wavelet for each particular gait. In this paper we show how these problems can be solved, resulting in a system that can accurately monitor patients' gait in real time without the need for calibration.

Savings in Location Management Costs Leveraging User Statistics

“…Assuming that a user of a mobile communications network follows a random movement and that all the location areas under study have the same area, the frequency of the location updates will depend on the speed of the mobile user [19][20][21][22][23][24][25][26], v, and the surface and perimeter length of the location areas [27][28][29][30][31][32]. Taking into account that the location update operations can take place within a same VLR (case 1, with probability 1 β ), or between two VLRs, making use of the…”

“…Where R is the hexagonal cell side, N is the number of cells per location area, and ) ( cos, i Nbl case is the number of bytes generated by a location update at interface i for any of the three different cases explained before. Defining a parameter called 2 β as the probability of location update using different VLRs, 21 β can be approximated by 80% of 2 β [33], and 22 β by 20% of 2 β . In Section 2.1, we will introduce two new algorithms for the calculation of these parameters.…”

“…Regarding (3), it is interesting to note that until recently, the estimation of the user velocity represented a difficult task, especially for indoor environments in which GPS sensors do not work. This issue can now be easily solved leveraging the WiFi radio and the accelerometer and magnetometer sensors embedded in current state-of-the-art smart phones [5,10,15,19,[21][22][23][24][25][26].…”

Analysis Of the Paging Costs In Location Management for Mobile Communications Networks Leveraging The Diffusion Constant

“…Therefore, the diffusion constant reflects the mobility of the user, regardless of the direction of the movement. It is interesting to note that the sensors embedded in current state-of-the-art smart phones can be leveraged to obtain very precise information about the mobility of the user [12][13][14][15][16][17], which can be very helpful to manage the location of the mobile communications networks' users. Another clear consequence inferred from Figure 1 is the fact that as the p or q values increase, so does the diffusion constant, but for combinations of p and q which add up the same quantity, the diffusion constant will be higher for those values of p and q that enclose the larger area in a theoretical rectangle of dimensions q p ⋅ .…”

“…Most of the recent research in this field has focused on the evaluation of the signalling costs involved in both location update and paging [7][8][9][10]. Common techniques to assess Location Management signalling costs make use of time-varying probability distributions on the mobile user's location, derived either from motion models or approximated by means of empirical data [11][12][13][14][15][16][17]. This strategy is especially suitable when the mobile terminal changes location according to stochastic processes.…”