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Journal ArticleDOI

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

01 Jan 2010-Progress in Electromagnetics Research B (EMW Publishing)-Vol. 20, Iss: 20, pp 147-166

TL;DR: This work proposes a technique to detect and characterize activity associated with a stationary human in through-the-wall scenarios using a Doppler radar system, using bio-mechanical human arm movement models and the empirical mode decomposition (EMD) algorithm for doppler feature extraction.

AbstractIn homeland security and law enforcement situations, it is often required to remotely detect human targets obscured by walls and barriers. In particular, we are speciflcally interested in scenarios that involve a human whose torso is stationary. We propose a technique to detect and characterize activity associated with a stationary human in through-the-wall scenarios using a Doppler radar system. The presence of stationary humans is identifled by detecting Doppler signatures resulting from breathing, and movement of the human arm and wrist. The irregular, transient, non-uniform, and non-stationary nature of human activity presents a number of challenges in extracting and classifying Doppler signatures from the signal. These are addressed using bio-mechanical human arm movement models and the empirical mode decomposition (EMD) algorithm for Doppler feature extraction. Experimental results demonstrate the efiectiveness of our approach to extract Doppler signatures corresponding to human activity through walls using a 750-MHz Doppler radar system.

Topics: Doppler radar (63%), Continuous-wave radar (60%), Doppler effect (56%)

Summary (3 min read)

1. INTRODUCTION

  • In recent years, there has been a great deal of research directed towards the use of Doppler-radar systems for monitoring human activity.
  • Doppler-radar was first demonstrated for remotely monitoring human activity in [1, 2].
  • Human activity can be considered as a combination of one or many of these movements, and each activity occurs over a different time scale.
  • Based on the reasoning presented in Section 3, the criteria the authors choose to decide on the time-frequency technique are — frequency resolution, ability to resolve time-frequency components of low amplitude, non-linearity of transformation and adaptive selection of time-scales.
  • The novel contribution of the present paper is the detection and characterization of Doppler from stationary humans, i.e., wherein the human torso is not moving.

2.1. Introduction to EMD-HS

  • The EMD-HS algorithm (also called the Hilbert Huang Transform (HHT)) was proposed in [13] for analyzing non-stationary signals originating from non-linear processes.
  • EMD extracts intrinsic oscillatory modes defined by the time scales of oscillation, called IMFs.
  • Such functions permit the application of the Hilbert transform and the corresponding definition of instantaneous frequency in [13].
  • The functions s(t) and ω(t) are the instantaneous amplitude and instantaneous frequency of the signal, respectively.

2.2. Sifting Process

  • The basic step of the EMD algorithm is the sifting process which essentially extracts scales of the signal.
  • The points of set S1max are interpolated to form the upper envelope of the signal, x̂max.
  • Following this, the function xr1 = x(t) − x1(t) is created, and the sifting process is repeated, resulting in x2(t), the second IMF.
  • Empirically, the EMD has been shown to be effective in extracting relevant components in a variety of applications involving non-stationary signals.
  • The first IMF represents the fastest modes of oscillation in the signal, and with subsequent IMFs, the frequency, as measured by the number of zero crossings decays exponentially as the index of the IMF.

3. MODELING DOPPLER SIGNATURES DUE TO HUMAN ACTIVITY

  • The Doppler modulations due to human activity vary in time according to the dynamics of human movement.
  • Non-stationary models for Doppler due to walking human targets were proposed in [8, 14].
  • Walking induces high Doppler shifts in the waveform that can be observed over short time durations.
  • The finite non-zero dimensions of the human arm and other parts of the body result in a Doppler return that consists of multiple frequency components at each time instant [14].
  • The authors conjecture that a human whose torso is not moving can be identified from the Doppler signatures due to activity such as breathing and movements of the arm.

3.1. Modeling Human Arm Motion

  • A characteristic Doppler event associated with stationary human targets is the movement of the arm.
  • Details of the motion of the arm contains information regarding the intent of humans behind the wall.
  • It is desirable to detect and characterize Doppler signatures of human arm motion for through-the-wall monitoring applications.
  • The authors present a model for Doppler due to human arm movements.
  • Human arm motion is composed of three components, defined by the joints driving its motion.

3.2. A General Model for Human Arm Motion

  • The three components of the arm, as represented by the wrist, forearm and arm can each be modeled as a solid shaft exhibiting rotational motion around the corresponding joint- wrist joint, elbow joint or shoulder joint.
  • In such a model, the movement of the human arm is defined by the three components: ω1, ω2, and ω3, representing the angular velocities of the three segments OA, AB and BC, around the points defined by O, A and B, respectively.
  • For deriving the Doppler shift resulting from this motion, the authors consider an infinitesimal element on each of the line segments OA, AB and BC.
  • From Equation (6), it is clear that the length of the moving component controls this ‘spread’ in the frequency implying that a scatterer of larger dimensions results in a higher frequency-spread.

3.3. Velocity of the Human Arm

  • The goal of their work is to identify Doppler characteristics that distinguish human activities.
  • This information has to be extracted from the time-dependency of the frequency, and the spread of the frequency.

3.3.1. Doppler Modeling Based on the Biomechanics of Human Movement

  • Doppler-radar models for human walking based on well known models of human locomotion used in computer animation are presented in [8, 22].
  • Figure 2 shows the idealized velocity profile of human arm movement considered in [23].
  • The Doppler shift due to a single element of the human arm is integrated over the entire length.
  • The authors drop the subscript i from Equation (6) for convenience.
  • The unimodal velocity profile over the duration of motion is seen to result in a return signal with four distinct maxima and a region of stationary points close to the time instant of maximum velocity.

3.4. Intermittent Human Activity

  • The return signal can then be represented as a linear combination of different waveforms, each of which is non-zero over a different time interval and with each waveform corresponding to the Doppler modulation due to the human activity.
  • Over each time interval, a different type of human motion results in a different Doppler modulation of the transmit signal which is represented as a non-stationary signal ai(t).
  • Without a knowledge of ti, it is not possible to pre-define optimum time and frequency resolutions for computing the joint time frequency distributions.
  • If for some ai(t), the events are non-stationary within the width of the window function w(t), then the spectrogram will fail to capture the complete time-frequency distribution of ai(t).

4. EXPERIMENTAL RESULTS

  • A human target located behind a brick wall of about 16 cm thickness was imaged using a radar system operating in the ultrahigh frequency (UHF) band.
  • The transmitted power was −5 dBm and the antenna gain was 5 dB.
  • The 750-MHz radar system was used to extract Doppler signatures associated with different activity associated with a stationary human.
  • The IMFs are indexed inversely as the scales of oscillations.
  • The Doppler oscillation caused by a person shuffling from a stationary position for about 2 s produces features of about 1.5.

4.1. Signatures of Different Types of Arm Movement

  • The human arm model was earlier described as consisting of three components, centered at the shoulder, the elbow and the wrist joints.
  • Figure 4 illustrates Doppler features extracted from the two experiments.
  • In the first experiment, the human target repeatedly moved the wrist around the wrist joint for a duration of 10 seconds, while keeping the rest of the arm stationary.
  • The energy distribution across the IMFs is considerably flat, and demonstrates that the energy is concentrated within a small number of IMFs.
  • The number of non-stationary oscillatory components in the former is higher than in the latter.

4.2. Experimental Verification of the Kinematic Model

  • The authors present the experimental results of human arm movement.
  • The return waveform was processed as described earlier.
  • The resulting plots for three different trials with different subjects are shown in Figure 5.
  • This demonstrates the viability of characterizing activities associated with a stationary human using a model based approach.
  • The experimental results validated the theoretical results as given by the Gaussian velocity profile model even when different individuals were used as targets.

5. CONCLUSION

  • The authors have developed a system for through-the-wall detection of a stationary human, based on the empirical mode decomposition-Hilbert spectrum algorithm.
  • The Doppler detection system was validated by testing the algorithm on real data.
  • The modeled waveform compared favorably with experimental results.
  • A model based approach for classifying human activity was thus shown to be feasible.
  • Doppler modulations due to different types of human activity were shown to occur over different scales.

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Progress In Electromagnetics Research B, Vol. 20, 147–166, 2010
THROUGH-THE-WALL DETECTION OF STATIONARY
HUMAN TARGETS USING DOPPLER RADAR
R. M. Narayanan, M. C. Shastry, P.-H. Chen, and M. Levi
The Pennsylvania State University
University Park, PA 16802, USA
Abstract—In homeland security and law enforcement situations, it is
often required to remotely detect human targets obscured by walls and
barriers. In particular, we are specifically interested in scenarios that
involve a human whose torso is stationary. We propose a technique to
detect and characterize activity associated with a stationary human in
through-the-wall scenarios using a Doppler radar system. The presence
of stationary humans is identified by detecting Doppler signatures
resulting from breathing, and movement of the human arm and wrist.
The irregular, transient, non-uniform, and non-stationary nature of
human activity presents a number of challenges in extracting and
classifying Doppler signatures from the signal. These are addressed
using bio-mechanical human arm movement models and the empirical
mode decomposition (EMD) algorithm for Doppler feature extraction.
Experimental results demonstrate the effectiveness of our approach to
extract Doppler signatures corresponding to human activity through
walls using a 750-MHz Doppler radar system.
1. INTRODUCTION
In recent years, there has been a great deal of research directed towards
the use of Doppler-radar systems for monitoring human activity.
Doppler-radar was first demonstrated for remotely monitoring human
activity in [1, 2]. SAR imaging and range detection [3–6] do not
work well to distinguish human targets from cluttered background.
In general, humans seldom stay still and their activities involve
considerable movement of their limbs. These movements are not always
captured by ranging systems. To recognize the presence of a human in a
target scene, it is desirable to look at the Doppler modulations of the
reflected waveforms, as these contain information about movements
Corresponding author: R. M. Narayanan (ram@engr.psu.edu).

148 Narayanan et al.
that are characteristic of human activity [7, 8]. Doppler detection
systems have the added advantages of simple design, low sampling
rates, and easy deployment. Indoor environments have minimal
Doppler clutter, which is highly desirable for effective detection.
Simple systems proposed in [2, 7, 9] for the detection of human
Doppler utilize time domain, frequency domain [9], and spectrogram
based approaches [7, 10]. The S-method is proposed in [11] for micro-
Doppler based characterization. Reassigned joint time-frequency
transforms are proposed in [12] for analysis. Existing systems for
human Doppler detection mostly deal with gross movement of the
human torso. In this paper, we consider detection and characterization
of Doppler from stationary humans, i.e., wherein the human torso
is not moving. In such scenarios, it is essential to extract Doppler
from breathing and transient movements of the arm. In this respect,
existing approaches to human Doppler analysis are limited by the time-
frequency ambiguity, and the a priori choice of time-frequency bases,
which are characteristic of traditional time-frequency distributions.
The EMD-Hilbert spectrum (referred to hereafter as EMD-HS)
algorithm is a recent development in the field of time-frequency
analysis [13]. It involves adaptive decomposition of a signal into
constituent time-frequency components called intrinsic mode functions
(IMFs). Preliminary work on using the EMD-HS approach towards
human Doppler analysis was presented in [14]. We review the EMD-
HS algorithm in Section 2 and define the instantaneous frequency
of a signal. The detection of transient activities is often crucial
to the detection of stationary human targets in the environment.
The Doppler frequencies associated with human movement can be
considered to result from the movement of the torso, movement of
the limbs, swinging of the limbs, expansion and contraction of the
chest cavity, and the changes in the position of the limbs. Human
activity can be considered as a combination of one or many of these
movements, and each activity occurs over a different time scale. In
the most general case, significant challenges to Doppler detection arise
because there is no way of knowing about the specifics of the human
activity a priori. In Section 3, we propose a model for human activity,
and consider issues involving time-frequency analysis. Based on the
reasoning presented in Section 3, the criteria we choose to decide on
the time-frequency technique are frequency resolution, ability to
resolve time-frequency components of low amplitude, non-linearity of
transformation and adaptive selection of time-scales. These properties
are satisfied by the EMD algorithm. In Section 4, we present the results
of experiments involving human Doppler.
There has been considerable work in the past in the field of remote

Progress In Electromagnetics Research B, Vol. 20, 2010 149
detection of respiration in human beings. The focus in earlier work
was on cooperative human targets, with the radar operating with
exact knowledge about the position of the target. These systems were
designed specifically for health monitoring.
In our system, we consider the problem of detecting human arm
movements for security applications, where the radar operator do es
not have the cooperation of the target. The novel contribution of
the present paper is the detection and characterization of Doppler
from stationary humans, i.e., wherein the human torso is not moving.
We believe this is the first paper to apply bio-mechanical models
of human movement to study transient Doppler modulations due to
a stationary human. In Section 4, we show that our model for
human arm movement can predict Doppler signatures reasonably well
using the EMD algorithm. We present experimental results that
demonstrate distinct Doppler modulations that result from different
types of transient, non-repetitive human activity.
2. EMPIRICAL MODE DECOMPOSITION
2.1. Introduction to EMD-HS
The EMD-HS algorithm (also called the Hilbert Huang Transform
(HHT)) was proposed in [13] for analyzing non-stationary signals
originating from non-linear processes. EMD extracts intrinsic
oscillatory modes defined by the time scales of oscillation, called IMFs.
Such functions permit the application of the Hilbert transform and
the corresponding definition of instantaneous frequency in [13]. The
Hilbert transform yields the analytic version of the signal, from which,
the instantaneous frequency is extracted as shown in Equations (1)–
(3).
z(t) = x(t) + jH{x(t)} (1a)
= x(t) + jy(t) (1b)
= s(t)e
j
R
ω(t)dt
, (1c)
where
s(t) =
p
(x(t))
2
+ (y(t))
2
, (2)
ω(t) =
d arctan {y(t)/x(t)}
dt
. (3)
In Equation (1), H{} denotes the Hilbert transform. The functions s(t)
and ω(t) are the instantaneous amplitude and instantaneous frequency
of the signal, respectively.

150 Narayanan et al.
2.2. Sifting Process
The basic step of the EMD algorithm is the sifting process which
essentially extracts scales of the signal. Consider a signal with
P maxima and Q minima. The sifting process starts with
identifying the extrema of the signal, x(t), given by the set
S
1
max
= x
max
(t
1
), x
max
(t
2
), . . . , x
max
(t
j
), . . . , x
max
(t
P
) and S
1
min
=
x
min
(t
1
), x
min
(t
2
), . . . , x
min
(t
i
), . . . , x
min
(t
Q
). The points of set S
1
max
are interpolated to form the upper envelope of the signal, ˆx
max
.
Similarly, the points of the set S
1
min
are interpolated to form the
minimum envelope, ˆx
min
. The average envelope, (ˆx
max
+ ˆx
min
)/2 is
subtracted from the original signal x(t) resulting in the first iteration
of the sifting process, which is expressed as x
k
j
(t) where k denotes the
iteration (k = 1 for the first iteration). The iteration on k is continued
until the time-average hx
k
1
j
(t)i = 0 and the number of extrema of
x
k
1
j
is no more than one less than the number of zero-crossings. For
simplicity, we will drop the term k
1
and write the resulting function
as x
j
. The first sifting process produces the first IMF, with j = 1.
Following this, the function x
r
1
= x(t) x
1
(t) is created, and the
sifting process is repeated, resulting in x
2
(t), the second IMF. The
IMFs are generated until the residue x
r
j
= x(t)
P
n=j
n=1
x
j
(t). The
functions x
j
(t), j = 1, 2, . . . , N, exhaust x(t) and are nearly orthogonal
to one another. Since each IMF has only one extrema between any two
successive zero crossings, the frequency of the signal can be directly
inferred by measuring the temporal distribution of the zero crossings
of the signal. Further, the IMFs have symmetric envelop es, with the
difference between the number of extrema and the number of zero
crossings being no more than one. Owing to these characteristics, the
IMFs are referred to as being mono-component.
Since the residue is computed by successively subtracting the
sifted functions from the original signal, the EMD algorithm is data
driven and adaptive. Furthermore, the performance of the EMD
algorithm is sensitive to the interpolation procedure which results in
an inexact estimation of the envelope. The sifting process is defined for
continuous signals which means that the performance of EMD depends
on the sampling rate [15]. The dependence of the EMD algorithm
on these factors precludes a general, unique theoretical framework for
EMD. Defining a function space for the EMD algorithm is an ill-posed
problem, making it difficult to construct an analytical description of
EMD. However, empirically, the EMD has been shown to be effective in
extracting relevant components in a variety of applications involving
non-stationary signals. Its effectiveness has been demonstrated for

Progress In Electromagnetics Research B, Vol. 20, 2010 151
processing audio signals [16], global position systems [17], gravitational
waves [18], seismic signals [19], etc. While wavelet decomposition
decomposes a signal into components using predefined filter banks,
the EMD algorithm decomposes it into components whose mo des of
oscillations are adaptively decided by the nature of the signal.
In the absence of an analytical formulation, the performance of
the EMD algorithm is inferred from empirical observations. One of the
important properties of EMD is that it behaves like a dyadic filter for
a white noise input signal. The frequency of the IMFs resulting from
the decomposition of a white noise signal follows an exponential trend.
The first IMF represents the fastest modes of oscillation in the signal,
and with subsequent IMFs, the frequency, as measured by the number
of zero crossings decays exponentially as the index of the IMF. The
final IMF, always has just one zero crossing. From simulations, it was
found that the number of zero crossings in an intrinsic mode function is
proportional to e
0.6n
, where n is the index of the IMF. Similarly, the
energy of the IMFs also reduces according to an exponential rule [20].
2.3. Hilbert Spectrum
Traditional time-frequency distributions define the frequency of a
signal based on the Fourier transform. This definition has the inherent
property of time-frequency uncertainty, as expressed by the lower
bound on the time-bandwidth product,
t
f
1/2. The analytic
signal corresponding to each IMF is constructed using the Hilbert
transform. The instantaneous frequency of this analytic signal is
defined as the derivative of the instantaneous phase defined in [13].
The different IMFs resulting from the EMD algorithm are orthogonal
to each other. The IMFs thus represent different time-scales of
oscillations, which form a set of basis functions. This implies that
there is no redundancy in the information contained in the different
IMFs. Using this property, a distribution is constructed from the
instantaneous frequencies of each of the IMFs. This distribution is
called the Hilbert spectrum (HS). Since the instantaneous frequency of
the EMD-HS approach is not defined based on the Fourier transform,
the time-frequency resolution is not limited by uncertainty. In our
implementation, we used a modified version of the code provided
in [21].

Citations
More filters

Journal ArticleDOI
Abstract: The ability to identify human movements can serve as an important tool in many different applications such as surveillance, military combat situations, search and rescue operations and patient monitoring in hospitals. This information can provide soldiers, security personnel and search and rescue workers with critical knowledge that can be used to potentially save lives and/or avoid dangerous situations. Most research involving human activity recognition employs the short-time Fourier transform (STFT) as a method of analysing human micro-Doppler signatures. However, the STFT has time-frequency resolution limitations and Fourier transform-based methods are not well-suited for use with non-stationary and non-linear signals. The authors approach uses the empirical mode decomposition to produce a unique feature vector from the human micro-Doppler signals following which a support vector machine is used to classify human motions. This study presents simulations of simple human motions, which are subsequently validated using experimental data obtained from both an S-band radar and a W-band millimetre wave (mm-wave) radar. Very good classification accuracies are obtained at distances of up to 90 m between the human and the radar.

119 citations


Journal ArticleDOI
TL;DR: A portable, digital, real-time random noise radar system operating in the ultrahigh frequency range for through-the-wall detection and imaging and its capability to image target scenes and characterize human activity from different stand-off distances is demonstrated.
Abstract: We present the design and implementation of a portable, digital, real-time random noise radar system operating in the ultrahigh frequency range for through-the-wall detection and imaging. Noise radar technology is combined with modern digital signal processing approaches to architect a system to covertly perform range imaging of obscured stationary and moving targets as well as to detect the presence of humans via micro-Doppler detection combined with empirical mode decomposition. We model the propagation and sampling nonidealities in the system and propose techniques to overcome the effect of these nonidealities. Experimental results demonstrate the system's capability to image target scenes and characterize human activity from different stand-off distances.

58 citations


Cites background or methods from "Through-the-Wall Detection of Stati..."

  • ...The theory of micro-Doppler recovery based on empirical mode decomposition was first introduced in [47] and further developed in [48]....

    [...]

  • ...This was not performed during the short-range detection in [48]....

    [...]

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    [...]

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    [...]

  • ...14, where experimental results from another paper from our group [48] are compared....

    [...]


Journal ArticleDOI
TL;DR: A time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing demonstrates that CEEMD promises higher spectral-spatial resolution than the other two EMD methods in GPR signal denoising and target extraction.
Abstract: In this letter, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by empirical mode decomposition (EMD) applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode-mixing problem in the EMD method and improve the resolution of ensemble EMD (EEMD) when the signal has a low signal-to-noise ratio. First, we analyze the difference between the basic theory of EMD, EEMD, and CEEMD. Then, we compare the time and frequency analysis with Hilbert–Huang transform to test the results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral–spatial resolution than the other two EMD methods in GPR signal denoising and target extraction. Its decomposition is complete, with a numerically negligible error.

50 citations


Cites background from "Through-the-Wall Detection of Stati..."

  • ...2415736 wall radar human detection to extract the human micro-Doppler signal [4]....

    [...]


Journal ArticleDOI
TL;DR: A technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs using a naive Bayesian classifier supported by principal component analysis is presented.
Abstract: A through-the-wall radar image (TWRI) bears little resemblance to the equivalent optical image, making it difficult to interpret. To maximize the intelligence that may be obtained, it is desirable to automate the classification of targets in the image to support human operators. This paper presents a technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs. The dependence of the image on the target location is discussed using a system point spread function (PSF) approach. It is shown that the position dependence will cause a classifier to fail, unless the image to be classified is aligned to a classifier-training location. A target image alignment technique based on deconvolution of the image with the system PSF is proposed. Comparison of the aligned target images with measured images shows the alignment process introducing normalized mean squared error (NMSE) ≤ 9%. The HRRP extracted from aligned target images are classified using a naive Bayesian classifier supported by principal component analysis. The classifier is tested using a real TWRI of canonical targets behind a concrete wall and shown to obtain correct classification rates ≥97%.

48 citations


Cites background from "Through-the-Wall Detection of Stati..."

  • ...Even when they have no gross motion, a person exhibits small motions, such as the rise and fall of the chest due to breathing, and the resulting -DSs are distinct and can be detected behind walls [11]....

    [...]


ReportDOI
01 Jan 2015
Abstract: The materials used in the study consisted of 720 small clear specimens of nominal dimensions 20 x 20 x 60 mm, of Turkish Red Pine (Pinus brutia) Lebanon cedar (Cedrus libani), Oriental beech (Fagus oriantalis), and English oak (Quercus robur ) from Turkey. The specimens were grouped into 4 batches of 15 specimens each and were tested in the ETH laboratories in Zurich, Switzerland. The influence of EMC was studied over four batches of 15 specimens each, conditioned for 6-8 weeks before testing at a temperature of 20 ± 2oC and at four different relative humidities (50%, 65%, 85%, and 95%). Time of flight value was measured with an ultrasonic commercial device Steinkamp BP-V. Measurements were made end to end directions (L, R, T) on each specimen, with a constant sensor coupling pressure. According to the time results of ultrasound devices, the wave velocities (length/time) and Edyn were calculated. Samples were also tested in uniaxial compression in order to determine E values in three orthotropic directions using a Zwick Z 100 universal testing machine. A load cell with 100-kN maximum capacity was used for compression tests performed in all directions. The feed rate was defined in such a way that the failure of the specimen should be reached in 90 (±30) s. The strains were evaluated using the digital image correlation DIC technique. Wood MC was determined by the oven-drying method. The R values between E and Edyn ranged from 0.79 to 0.96 for the species tested. Moisture content seems to be an influencing factor on sound velocities.

43 citations


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  • ...The results in [23] and [24] suggest that a human arm moving in response to a stimulus follows a similar velocity profile across different human subjects and trials....

    [...]

  • ...In this section, we propose a model for the motion of the human arm, primarily based on [23]....

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  • ...In [23], the authors monitored the velocity of the human arm using a set of light emitting diodes placed on the human arm....

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  • ...Figure 2 shows the idealized velocity profile of human arm movement considered in [23]....

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Journal ArticleDOI
TL;DR: The significance of the individual interaction forces during reaching movements in a horizontal plane involving only the shoulder and elbow joints has been assessed and trajectory formation strategies which simplify the dynamics computation are presented.
Abstract: Movement of multiple segment limbs requires generation of appropriate joint torques which include terms arising from dynamic interactions among the moving segments as well as from such external forces as gravity. The interaction torques, arising from inertial, centripetal, and Coriolis forces, are not present for single joint movements. The significance of the individual interaction forces during reaching movements in a horizontal plane involving only the shoulder and elbow joints has been assessed for different movement paths and movement speeds. Trajectory formation strategies which simplify the dynamics computation are presented.

784 citations


"Through-the-Wall Detection of Stati..." refers background in this paper

  • ...The velocity profiles for the movement of the human arm in response to different types of stimuli are presented in [24]....

    [...]

  • ...The results in [23] and [24] suggest that a human arm moving in response to a stimulus follows a similar velocity profile across different human subjects and trials....

    [...]


Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "Through-the-wall detection of stationary human targets using doppler radar" ?

The authors propose a technique to detect and characterize activity associated with a stationary human in through-the-wall scenarios using a Doppler radar system. Experimental results demonstrate the effectiveness of their approach to extract Doppler signatures corresponding to human activity through walls using a 750-MHz Doppler radar system.