- Open Access
Time-varying on-body wireless channel model during walking
© Cheffena; licensee Springer. 2014
- Received: 6 May 2013
- Accepted: 6 February 2014
- Published: 20 February 2014
A novel dynamic channel model for on-body wireless communication during walking is proposed. The developed model utilizes a human walking model which provides detailed information on the movement of the human body parts. The diffraction of the signal around the body parts is used to describe the time-varying shadowing effects. Body part movements are also used to estimate the signal fading caused by angular variations of the transmitting and receiving antenna gains. A Rice distribution is used to represent the multipath fading effects caused by objects around the human body. Simulation results of the first- and second-order statistics of the received signal affected by moving body parts for 2.4 GHz signal are presented. To illustrate the capabilities of the developed model, time series were generated and used in system performance calculations. The obtained results give an insight into the potential advantages of link diversity technique in wireless body area networks (WBANs).
- Body area network
- On-body communication
- Fading channels
In recent years, body area networks are gaining increasing attention because of their potential applications in various domains such as health, entertainment, and sports. The use of wireless communication in the immediate vicinity of the human body eliminates the need for wired interconnections and hence the concept of wireless body area network (WBAN).
However, various propagation measurement results have shown that the on-body wireless channel is subject to fading caused by the movement of the human body [1–9]. In addition to the shadowing of the signal by moving body components, signal reflection/scattering from objects around the human body result in multipath fading effects [1, 2, 10]. Furthermore, the angular variations of the antenna gains during walking give rise to time-varying channel conditions [9, 11, 12]. Understanding the on-body propagation channel is thus important for successful design of WBAN systems.
A number of studies on the signal fading caused by the movement of the human body are reported in the literature, most of them are based on radio frequency (RF) measurements [1–9] or using numerical simulations such as finite-difference time-domain (FDTD) [10, 13–15]. A finite-state Markov model for dynamic on-body channels, where the model parameters are extracted from the RF measurements is reported in . A two-state alternating Weibull renewal process for describing the dynamical properties of the on-body channel is proposed in . In addition, by modeling the trunk, arms, and legs of a human body as infinite cylinders, Liu et al.  proposed a method for calculating the scattering signal by body components. A series of time-consecutive scenarios with different positions of the arms at the azimuth is included to describe the time-varying behavior of the channel. In , a phantom created by an animation software is used for simulating the time-varying on-body communication channel. Similar study is conducted in  to characterize the shadowing properties of an arm-waving human body.
In this paper, a novel dynamic channel model for on-body wireless communication during walking is proposed by utilizing a human walking model. Using geometrical relations, the diffraction of the signal around body parts is calculated to describe the time-varying shadowing effects. The movements of the body parts are also used to estimate the signal fading caused by angular variations of the antenna gains. In addition, a Rice distribution is used to represent the multipath fading effects caused by objects around the human body. To show the potential of the proposed model, time series were generated and used in system performance calculations. The results give an insight into the advantages of link diversity technique in WBANs.
The paper begins by discussing the human walking model in Section 2. The proposed dynamic channel model for on-body wireless communication is presented in Section 3. Numerical results and discussions are given in Section 4. Finally, the conclusions are given in Section 5.
Body part translations and rotations (see Figure 1 )
The different angle trajectories and translations shown in Figure 1 can be expressed as function of the relative time, t′, which is defined as the time between zero and one human cycle, t′=|t f|mod 1. They are synchronized with the left human body motion, and the origin is at the left heel strike. Displacements of the right human body parts are obtained by a phase displacement of half a cycle, i.e., t′=|t f+ϕ|mod 1.
The position of all body parts is calculated relative to the spine center (i.e., the center of the coordinate system shown in Figure 1). The vertical translation TV(t′), lateral translation TL(t′), and horizontal TH(t′) translation situate the body relatively to the average position it has while moving with the velocity vr. These are empirical relations found in . The lateral translation is positive on the left-hand side of the human body and is given by the expression.
The horizontal and vertical translations have equivalent expressions. The pelvis rotates in forward/backward, left/right, and torsion rotation as the person walks. The forward/backward rotation of the pelvis moves the center of gravity of the body in order to help the forward motion of the leg and is expressed as,
0=vr<0.5 - from the rest position to reach a slow gait
0.5=vr<1.3 - walking at almost constant speed
1.3=vr<3 - walking fast.
Integral limits for parameter u and v of the three surfaces
The total received diffracted field is then the combination of the three aperture diffracted fields whose surfaces are Σ1, Σ2, and Σ3 with assumed infinite depth. The transitions between LOS and NLOS conditions are not sharp and thus more realistic regarding the empirical results. Using this model, good estimation of the shadow region behind a human body part might be obtained.
3.2 Multipath fading
where s is the magnitude of the coherent component, and σ2 is the variance of the incoherent component.
The Rice K-factor (K=s2/2σ2) depends on the mobility of the human body. High value of Rice K-factor might be expected when the human subject is at rest (i.e., a strong main signal path exists). As a result, there might be little fast fading which is mainly caused by, e.g., involuntary breathing movements. However, low K-factor is expected in walking scenario as the link between the transmitter and the receiver is shadowed by the body and propagation occurs by reflections from the surrounding environments .
3.3 Doppler shift
The movement of body parts create time-varying channel conditions. Characterization of the Doppler spectra is thus important for the determination of the time variance of the on-body wireless channel. The situation where the antenna is moving in a random environment leads to the classical Jakes spectrum (with bathtub-like shape) for scatters uniformly distributed in azimuth . For the case where the antenna is stationary, moving scatterers in the channel will lead to a different Doppler spectrum which peaks at 0 Hz and falls off rapidly [25, 26].
Doppler spectrum parameter for on-body wireless channel []
Left ear-right ear
3.4 The overall simulation model
where θ and ϕ are the elevation and azimuth angles of the antenna, respectively. Parameters and describe the change in the orientation of the antenna due to the movement of the body part where the antenna is mounted and are calculated from the walking model discussed in Section 2.
Human body dimensions in centimeters
Right ( x, y, z)
Left ( x, y, z)
Body ( x, y, z)
(0, -21.6, 29.4)
(0, 21.6, 29.4)
(0, -21.6, -9.9)
(0, 21.6, -9.9)
(0, -21.6, -44.1)
(0, -21.6, -44.1)
(0, -17.8, -17.8)
(0, 17.8, -17.8)
(0, -17.8, -64.4)
(0, 17.8, -64.4)
(0, -17.8, -117.8)
(0, 17.8, -117.8)
(20.3, -17.8, -117.8)
(20.3, 17.8, -117.8)
(0, 0, 0)
(0, 0, 29.5)
(0, 0, 39.7)
(0, 0, 53.1)
(0, 0, 65.2)
Sampling frequency, fs
Rice K-factor, K
TX and Rx dipole antenna length, L
0.5 and 1.5λ
Human body dimension
See Table 4
Relative waking velocity, vr
Number of walking cycles, N
1 and 100
4.2 Performance evaluation
The on-body wireless channel is subject to fading caused by the movement of the human body parts and by the reflection/scattering of the signal by objects in the vicinity of the human body. These effects need to be taken into account for a realistic characterization of the channel. In this contribution, a novel dynamic channel model for on-body wireless communication during walking is proposed. The developed model uses a human walking model which provides detailed information on the movement of the human body parts. Using geometrical relations, the diffraction of the signal around the body parts is calculated to describe the time-varying shadowing effects. The CDFs of these shadowing effects follow a normal distribution which is consistent with the reported measurement results. A Rice distribution is used to represent the fast fading effects caused by objects around the human body, where the Rice K-factor depends on the mobility of the human body. High value of Rice K-factor is expected when the human subject is at rest, while low K-factor is expected in a walking scenario. The model takes also into account the Doppler effects caused by the movement of the different body parts.
Body part movements are also used to estimate the signal fading caused by angular variations of the antenna gains. It is found that the effect of antenna gain variations strongly depends on the radiation pattern of the antennas and has a significant effect on the first- and second-order statistics of the signal.
Furthermore, the simulation results of the first- and second-order statistics of the received signal at 2.4 GHz for the right-belt-to-left-wrist and right-belt-to-left-ankle links are presented. In addition, to illustrate the capabilities of the developed model, time series were generated and used in system performance calculations. The obtained results suggest that choosing properly diversity antenna locations may increase the system performance, giving an insight into the potential advantages of link diversity technique in WBANs.
In general, the proposed dynamic channel model can be used to carry out different system-level analyses such as capacity and bit error rate for on-body wireless communication. Future work includes validating the developed time-varying on-body wireless channel model during walking using measurements. In addition, the model will be extended covering the body effect on antenna and its polarization, a better off-body multipath description and the random properties of human kinetics.
The author would like to thank Gjøvik University College, Norway, for supporting this work.
- DErrico R, Ouvry L: A statistical model for on-body dynamic channels. Int. J. Wireless Inf. Netw 2010, 17: 92-104. 10.1007/s10776-010-0122-0View ArticleGoogle Scholar
- Nechayev YI, Hall PS, Khan I, Constantinou CC: Wireless channels and antennas for body-area networks. Paper presented at the 7th international conference on wireless on-demand network systems and services (WONS). Kranjska Gora; 3–5 Feb 2010:137-144.Google Scholar
- IEEE P802.15 Working Group for Wireless Personal Area Networks: IEEE P802.15: Statistical Property of Dynamic BAN Channel Gain at 4.5GHz. Piscataway: IEEE; 2008.Google Scholar
- Reusens E, Joseph W, Latre B, Braem B, Vermeeren G, Tanghe E, Martens L, Moerman I, Blondia C: Characterization of on-body communication channel and energy efficient topology design for wireless body area networks. IEEE Tran. Inf. Tech. Bio 2009, 13(6):933-945.View ArticleGoogle Scholar
- Zhen B, Kim M, Takada J, Kohno R: Finite-state Markov model for on-body channels with human movements. Paper presented at the, IEEE international conference on communications (ICC). Cape Town; 23–27 May 2010.Google Scholar
- Taparugssanagorn A, Zhen B, Tesi R, Mamalainen M, Iinatti J, Kohno R: A dynamic channel model of UWB-WBAN for some medical applications. Paper presented at the wireless personal multimedia communications symposium. Lapland; 8–11 Sept 2008.Google Scholar
- Liu L, Derrico R, Ouvry L, Doncker PD, Oestges C: Dynamic channel modelling at 2.4 GHz for on-body area networks. Adv. Wireless Commun. Netw 2011., 2(4):Google Scholar
- Roy SV, Quitin F, Liu L, Oestges C, Horlin F, Dricot J, Doncker PD: Dynamic channel modelling for multi-sensor body area networks. IEEE Tran. Ant. Prop 2013, 61(4):2200-2208.View ArticleGoogle Scholar
- Roblin C, Laheurte J, Derrico R, Gati A, Lautru D, Alves T, Terchoune H, Bouttout F: Antenna design and channel modelling in the BAN context—part II: channel. Ann. Telecommun 2011, 66: 157-175. 10.1007/s12243-010-0231-xView ArticleGoogle Scholar
- Aoyagi T, Iswandi MK, Takada J, Hamaguchi K, Kohno R: Body motion and channel response of dynamic body area channel. Paper presented at the 5th European conference on antennas and propagation (EUCAP). Rome; 11–15 Apr 2011.Google Scholar
- Kim M, Takada J, Iswandi: Analysis of antenna motion dependency on BAN channels. IEICE Technical Report (MICT2010). Tokyo; 2010.Google Scholar
- Scanlon WG, Conway GA, Cotton SL: Antennas and propagation considerations for robust wireless communications in medical body area networks. Paper presented at IET seminar on antennas and propagation body-centric wireless communications. London; 24 Apr 2007.Google Scholar
- Swaisaenyakron S, Young P, Batchelor J: Animated human walking movement for body worn antenna study. Paper presented at the Loughborough antennas and propagation conference. Loughborough; 14–15 Nov 2011.Google Scholar
- Wang Q, Tayamichi T, Kimura I, Wang J: An on-body channel model for UWB body area communications for various postures. IEEE Tran. Ant. Prop 2009, 4(57):991-998.View ArticleGoogle Scholar
- Gallo M, Hall PS, Nechayev YI, Bozzetti M: Use of animation software in simulation of on-body communications channels at 2.45 GHz. IEEE Ant. Wireless Prop. Lett 2008, 7: 321-324.View ArticleGoogle Scholar
- Ogawa K, Honda K: BAN shadowing properties of an arm-waving dynamic phantom. Paper presented at the 5th European conference on antennas and propagation (EUCAP). Rome; 11–15 Apr 2011.Google Scholar
- Thomas F, Johanston O: The illusion of Life: Disney Animation. New York; 1995.Google Scholar
- White T: The Animator’s Workbook. New York: Watson-Guptill; 1988.Google Scholar
- Boulic R, Magnenat-Thalmann N, Thalmann D: A global human walking model with real-time kinematic personification. Vis. Comput 1990, 6: 344-358. 10.1007/BF01901021View ArticleGoogle Scholar
- Dorp P, Groen FCA: Human walking estimation with radar. IEE Proc. Radar Sonar Navig 2003, 150(5):356-365. 10.1049/ip-rsn:20030568View ArticleGoogle Scholar
- Hristov HD: Fresnel Zones in Wireless Links, Zone Plate Lenses and Antennas. Norwood: Artech House; 2000.Google Scholar
- Fontan FP, Abele A, Montenegro B, Lacoste F, Fabbro V, Castanet L, Sanmartin B, Valtr P: Modelling of the land mobile satellite channel using a virtual city approach. Paper presented at the 2nd European conference on antennas and propagation (EUCAP). Edinburgh; 11–16 Nov 2007.Google Scholar
- Perez-Fontan F, Espineira PM: Modelling the Wireless Propagation Channel: A Simulation Approach with MATLAB. Chichester: Wiley; 2008.View ArticleGoogle Scholar
- Clarke RH: A statistical theory of mobile radio reception. Bell Syst. Tech. J 1968, 47: 957-1000. 10.1002/j.1538-7305.1968.tb00069.xView ArticleGoogle Scholar
- Bultitude RJC: Measurement characterization and modelling of indoor 800/900 MHz radio channels for digital communications. IEEE Commun. Meg 1987, 25(6):5-12.View ArticleGoogle Scholar
- Howard SJ, Pahlavan K: Doppler spread measurements of indoor radio channel. Electron. Lett 1990, 26(2):107-109. 10.1049/el:19900074View ArticleGoogle Scholar
- Andersen JB, Nielsen JO, Pedersen GF, Bauch G: Doppler spectrum from moving scatterers in a random environment. IEEE Trans. Wirl. Commun 2009, 8(6):3270-3276.View ArticleGoogle Scholar
- Mackowiak M, Correia LM: A statistical model for the influence of body dynamics on the gain pattern of wearable antennas in off-body radio channels. Wireless Pers. Commun 2013, 73: 381-399. doi:10.1007/s11277-013-1193-x 10.1007/s11277-013-1193-xView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.