Signal Processing - March 2016 - 34
step-length estimation algorithm for the pedestrian navigation
system (PNS) has an accuracy of 95% in the worst case [10].
Two PDF algorithms including Weiberg and zero velocity
updates (ZVU) for stride-length estimation are tested at three
different walking speeds (slow, normal, and fast) [13]. The
authors show that the Weiberg algorithm performs better than
ZVU at all walking speeds. An IMU-based self-contained
pedestrian tracking method is proposed that uses ZVU and the
step length estimation as a control variable to correct the
acceleration drift. This method improves the tracking accuracy by decreasing the final position error for different scenarios
such as short and long distance walking and reduces the final
position error up to 66% when compared to other algorithms
[9]. A method using IMU sensors attached on soldiers' boots
is compared to the implementation of ZVU with and without
magnetic heading information [11]. Using ZVUs along with
magnetic heading information can be accurate for the soldiers
when they are operating an attack in a building. This method
stayed within 2 m of the true path over a path of more than 90 m.
A method using phone inertial sensors with a default rate of
50 Hz is proposed, i.e., infrastructure-free, phone position
independent, user adaptive, and easy to deploy [12]. The steplength estimation is used as a personal model for a user and
this model is updated each time the system collects data. The
users are put into different groups based on their personal
models. The step-detection error for the cellphone in hand and
in pocket cases for different algorithms were compared and
error rates from 1.6% to 24.5% (in hand) and 1.1% to 25.6%
(in pocket) were reported. An investigation using IMU sensors
sampling at 1 kHz detects preimpact falls using trunk vertical
velocity [15]. Falls can be distinguished from normal ADLs,
with 100% accuracy and with an average detection speed of
323 millesconds prior to trunk impact and 140 milliseconds
prior to knee impact, in their subject group. Sensor locations
Power consumption/computational complexity
Power-aware IMU-based sensors can potentially reduce the
size of batteries, enhance sensor lifetime, and enable long-term
monitoring. Signal processing algorithms with lower computational complexity make it possible to analyze the collected data
more quickly and provide faster feedback. Exploring the lowest
sampling rate for activity detection using FFT features can save
power [2]. The results show that 10 Hz is able to distinguish
between walking and sitting, but does not do well distinguishing falling with a wrist-worn accelerometer. A granular decision-making module is proposed to reduce the power
consumption significantly for a wearable IMU-based movement monitoring system [30]. Movements that are of no interest
are removed as early as possible from the signal processing
chain, deactivating all of the remaining modules in the signal
processing chain as well as the microprocessor. The bit resolution, the key factor that affects the system power consumption,
is only increased as the target movement is detected. Similarly,
a low-power programmable signal processing architecture for
dynamic and periodic activity monitoring applications saves
power by performing signal processing in a tiered fashion by
removing irrelevant data as soon as possible [25]. Using wavelet
decomposition 75.7% power savings are achieved while maintaining 96.9% sensitivity detection of target actions.
Conclusions
Sensor Sampling Rates
Sensor Locations
100 Hz
[4]-[6], [9]-[11],
[13], [17], [20],
[23]
Wrist/Hand [2], [4]-[7], [12], [17],
[23], [24], [26], [29]
50 Hz
[7], [12], [19],
[24], [29]
Hip/Waist
[14], [19], [24],
[25], [28]
The growth of wearable IMU sensors has created many
opportunities to improve people's health and lives through the
development of innovative applications. This article has provided an overview of signal processing techniques and their
performance for assisted living applications. Many of the
applications reviewed are the subject of ongoing research and
there many opportunities for improvement still remain. A
variety of signal processing techniques are being used, but for
an actual working system, the accuracy and power concerns
must be taken into consideration on a case by case basis noting that applications and related hardware have different
needs. Applications using wearable IMU sensors will continue to improve and provide valuable information to help people to have healthier lifestyles with greater independence.
40 Hz
[3], [8], [22]
Thigh
[14], [20], [21], [23],
[26]
Authors
Below
40 Hz
10 Hz [2], 25 Hz
[25], 32Hz [21]
Sternum/
Trunk
[3], [7], [8], [15],
[16], [18], [21],
[22], [24], [27]
47 Hz
[14]
Lower Leg/
Calf
[20], [23]
57 Hz
[28]
Ankle/Foot
[7], [9]-[11], [13],
[21], [25], [26]
64 Hz
[26]
Upper Arm
[4], [6], [17],
[24], [29]
Above
100 Hz
128 Hz [18], 250 Hz Other
[27], 1 kHz [15]
Table 3. Sensor sampling rate and location.
34
and sampling can impact accuracy. This information for the
reviewed papers is given in Table 3.
Ear [24], pocket [12],
knees [25]
Terrell R. Bennett (tbennett@utdallas.edu) received his S.B.
degree in electrical engineering and computer science from
the Massachusetts Institute of Technology in 2002 and his
M.S. degree in electrical engineering from the University of
Texas at Dallas in 2007. His research is focused on signal processing and algorithm design for synchronization of cyberphysical systems and the Internet of Things as well as working
with data from wearable sensors to detect activities, estimate
motion, and improve the quality of the sensor data.
Jian Wu (jian.wu@tamu.edu) received his M.S. degree in
communication and information systems in 2012 from
IEEE SIgnal ProcESSIng MagazInE
|
March 2016
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