Time-varying on-body wireless channel model during walking
- Michael Cheffena^{1}Email author
https://doi.org/10.1186/1687-1499-2014-29
© Cheffena; licensee Springer. 2014
Received: 6 May 2013
Accepted: 6 February 2014
Published: 20 February 2014
Abstract
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).
Keywords
1 Introduction
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 [5]. A two-state alternating Weibull renewal process for describing the dynamical properties of the on-body channel is proposed in [6]. In addition, by modeling the trunk, arms, and legs of a human body as infinite cylinders, Liu et al. [7] 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 [15], a phantom created by an animation software is used for simulating the time-varying on-body communication channel. Similar study is conducted in [16] 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.
2 The human model
Body part translations and rotations (see Figure 1 )
Lateral translation | T_{L}(t^{′}) |
Horizontal translation | T_{H}(t^{′}) |
Vertical translation | T_{V}(t^{′}) |
Forward/backward angle | θ_{FB}(t^{′}) |
Left/right angle | θ_{LR}(t^{′}) |
Shoulder angle | θ_{S}(t^{′}) |
Elbow angle | θ_{E}(t^{′}) |
Hip angle | θ_{H}(t^{′}) |
Knee angle | θ_{K}(t^{′}) |
Ankle angle | θ_{A}(t^{′}) |
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 T_{V}(t^{′}), lateral translation T_{L}(t^{′}), and horizontal T_{H}(t^{′}) translation situate the body relatively to the average position it has while moving with the velocity v_{r}. These are empirical relations found in [19]. 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,
- i
0=v_{r}<0.5 - from the rest position to reach a slow gait
- ii
0.5=v_{r}<1.3 - walking at almost constant speed
- iii
1.3=v_{r}<3 - walking fast.
3 The propagation model
3.1 Shadowing
Integral limits for parameter u and v of the three surfaces[23]
Σ _{1} | Σ _{1} | Σ _{3} | |
---|---|---|---|
u | −∞, $\sqrt{2}\frac{{x}_{1}-{x}_{0}}{{R}_{1}}$ | $\sqrt{2}\frac{{x}_{1}-{x}_{0}}{{R}_{1}}$, $\sqrt{2}\frac{{x}_{2}-{x}_{0}}{{R}_{1}}$ | $\sqrt{2}\frac{{x}_{2}-{x}_{0}}{{R}_{1}}$, ∞ |
v | $-\sqrt{2}\frac{{z}_{0}}{{R}_{1}}$, ∞ | $\sqrt{2}\frac{h-{z}_{0}}{{R}_{1}}$, ∞ | $-\sqrt{2}\frac{{z}_{0}}{{R}_{1}}$, ∞ |
The total received diffracted field is then the combination of the three aperture diffracted fields whose surfaces are Σ_{1}, Σ_{2}, and Σ_{3} 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 σ^{2} is the variance of the incoherent component.
The Rice K-factor (K=s^{2}/2σ^{2}) 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 [1].
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 [24]. 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 [[7]]
Radio link | BW (Hz) | γ |
---|---|---|
Hip-chest | 1.5 | 0.004 |
Hip-thigh | 6.4 | 0.067 |
Hip-wrist | 7.3 | 0.094 |
Hip-foot | 10 | 0.231 |
Left ear-hip | 1.3 | 0.007 |
Left ear-wrist | 8 | 0.574 |
Left ear-foot | 12.6 | 0.862 |
Left ear-right ear | 0.3 | 0.013 |
3.4 The overall simulation model
where θ and ϕ are the elevation and azimuth angles of the antenna, respectively. Parameters $\delta {\theta}_{{t}^{\prime}}$ and $\delta {\varphi}_{{t}^{\prime}}$ 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.
4 Numerical results and discussions
4.1 Simulations
Human body dimensions in centimeters
Body part | Right ( x, y, z) | Left ( x, y, z) | Body ( x, y, z) |
---|---|---|---|
Shoulder | (0, -21.6, 29.4) | (0, 21.6, 29.4) | - |
Elbow | (0, -21.6, -9.9) | (0, 21.6, -9.9) | - |
Fingers | (0, -21.6, -44.1) | (0, -21.6, -44.1) | - |
Hip | (0, -17.8, -17.8) | (0, 17.8, -17.8) | - |
Knee | (0, -17.8, -64.4) | (0, 17.8, -64.4) | - |
Ankle | (0, -17.8, -117.8) | (0, 17.8, -117.8) | - |
Toe | (20.3, -17.8, -117.8) | (20.3, 17.8, -117.8) | - |
Spine center | - | - | (0, 0, 0) |
Throat bottom | - | - | (0, 0, 29.5) |
Throat top | - | - | (0, 0, 39.7) |
Head center | - | - | (0, 0, 53.1) |
Head top | - | - | (0, 0, 65.2) |
Simulation parameters
Parameter | Value |
---|---|
Frequency, f | 2.4 GHz |
Sampling frequency, f_{s} | 10 Hz |
Rice K-factor, K | 7 dB |
TX and Rx dipole antenna length, L | 0.5 and 1.5λ |
Human body dimension | See Table 4 |
Relative waking velocity, v_{r} | 1 |
Number of walking cycles, N | 1 and 100 |
4.2 Performance evaluation
5 Conclusion
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.
Declarations
Acknowledgements
The author would like to thank Gjøvik University College, Norway, for supporting this work.
Authors’ Affiliations
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