A Full-Body Wireless Wearable UWB-Based Human Motion Capture and Gait Analysis System - E-Healthcare Systems and Wireless Communications - page 454

Sensor-Fusion the effect of body shadowing becomes dominant.

For the 0 o body angle, the BER approaches the
performance in the CM3 model alone, whereas
considering other body rotation angles causes
significant degradation in the BER. This means
that for our system we should ultimately consider
the 0 o transmission scenario. This would require
storing the acquired data for sometime on the
on-body central-node.

One of the advantages of UWB radios is their
suitability for the integration with other motion
sensors. With UWB sensors and inherent rang-
ing and localization approaches, our system is
capable of accurately estimating the gait kinemat-
ics. Ultimately, our system should be capable of
estimating both kinematic and kinetic parameters
associated with gait analysis. Thus, we further
propose the use of force sensors placed under the
test subject's feet. The analog data, force sensor
data, is first converted into the digital form, and
transferred to the on-body central-node. This is
typically analogous to the force plate used in op-
tical tracking systems. It is worth noting that the
fusion of data might not be directly obvious, as
both kinetics and kinematics are used for obtain-
ing a complete picture of the gait. So, fusion here
means using more than one type of sensors for
obtaining the data required by one system, the gait
analysis system. However, some gait parameters
do require the data obtained from both kinetics and
kinematics, such as the moment, which is equal
to the force (kinetics) multiplied by the distance
(kinematics) (Wardlaw 1990).
In order to examine sensor integrability and
accuracy of the proposed system for actual gait
parameters, gait data files acquired via force sen-
sors were obtained from (Goldberger 2000). These
files were processed using MATLAB to extract
the gait data. The data was first converted to the
binary format using a 12-bit ADC, and then used
in a simulation which was used as binary data in
the IEEE 802.15.6a UWB channel model. The
detected bits were then reconverted and compared
to original data for normal gait and Parkinson's
gait. The achieved BER was 7e-5 at E p / N 0 = 28
dB in the IEEE 802.15.6a UWB CM3 on-body
channel model. This is equivalent to E b / N 0 = 18
dB with 10 pulses-per-bit. When adding the effect
of CM4 (on-body to off-body communication),

Overall Power Consumption and
Battery Lifetime Estimation

This sub-section studies the memory and battery
lifetime requirements based on design param-
eters of actual system components. We start by
estimating the memory requirement as follows.
Assuming 16 bits per range estimate, this gives
2 bytes per frame per node. For all 50 nodes, it
gives an overall data per frame = 100 bytes. The
memory requirements for all UWB nodes per day
= 24*60*60/1 s * 100 bytes =
8.64e9 bytes/day. Considering also force-sen-
sors at a 300 Hz rate, 2*24*60*60*300 = 51.84e6
bytes/day. The overall memory requirements =
8.69e9 bytes/day = 8.09 Gigabytes/day. Thus, a
64 Gigabyte micro-Secure Digital (SD) memory
card is sufficient for storing approximately 8 days
of acquired data. Moreover, the overall power
consumption and battery lifetime are estimated
as follows. Assuming a duty-cycle per node= 2
µs / 1 ms = 2e-3.An estimated average power
consumption for 100% duty-cycle is ≈ 100 mW.
Hence, the average power consumption for a
0.2% duty-cycle = 100 mW * 2e-3 = 0.2 mW.
If the system is considered to have one battery,
thus considering all 50 nodes, the average power
consumption = 10 mW. Roughly considering a
common 1.5 V voltage source; this gives 6.667
mA. Thus, considering a common battery (AAA
battery) source (750 mAh), the average battery-life
is 750 mAh / 6.667 mA = 112.5 hrs/24 = 4.6 days.