IEICE TRANS. ??, VOL.Exx–??, NO.xx XXXX 200x

1

PAPER

Human Activity Identification by Height and Doppler
RCS Information Detected by MIMO Radar
Dai SASAKAWA† , Student Member, Naoki HONMA† , Member, Takeshi NAKAYAMA†† ,and Shoichi IIZUKA†† , Nonmembers

SUMMARY
This paper introduces a method that identifies
human activity from the height and Doppler Radar Cross Section(RCS) information detected by Multiple-Input Multiple-Output(MIMO) radar. This method estimates the three-dimensionaltarget location by applying the MUltiple SIgnal Classification(MUSIC) method to the observed MIMO channel; the Doppler
RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler
RCS. In experiments, the proposed method achieves over 90%
recognition rate (average).
key words: MIMO array, human activity identification, human
localization, MUSIC method, microwave sensors, Doppler RCS.

1.

Introduction

Wi-Fi is now a very popular access system technology.
While developed for communication Wi-Fi can also be
used to realize sensing-based services. The aging society raises new social concerns such as lonely death, andthe increase in the number of elderly people demands
to sense systems that can observe the elderly by detecting activities such as falls. Internet of thing devices
such as networked video cameras [1] or wearable sensors[2][3] can be used to monitor the elderly. However, theformer violates privacy in spaces such as the bathroom
and restroom, and it provides only line-of-sight coverage. The latter requires wearing sensors continuously,
and so places excessive physical and mental burdens
on the user. This approach is also unsuitable for theelderly because observation depends on the person remembering to wear the device.
Microwave sensing provides another approach toprotecting the elderly. Its key features are privacy assurance and contact-less observation, no wearable device is needed [4]-[6]. Microwave-based monitoring canbe realized by Direction of Arrival (DOA)/Directionof Departure (DOD) based Multiple-Input MultipleOutput (MIMO) radar systems [7][8]. Although such
systems can localize targets, their precision is inadequate because the desired signal is interfered by undesired waves due to the multi-path environment of
most private homes. We need a system/algorithm suit†

Graduate School of Engineering, Iwate University,
Morioka, 020-8551, Japan.
††
Home Appliances Development Center, Panasonic Corporation, Kadoma, 571-8504, Japan.

able for human localization in multi-path environments.
Known approaches include Time Difference of Arrival(TDOA) estimation [9][10], and trigonometry methods based on DOA/DOD estimation using the MUSIC
method [11]-[14]. Though TDOA localizes targets evenin multi-path environments, it needs a wide-band signal of over 1.79 GHz (from 5.46 GHz to 7.25 GHz)and can only estimate the target location. Moreover,static channels are essential, i.e. the environment without humans must be measured in advance, and themeasurements must be repeated if the environment ischanged, e.g., a piece of furniture is shifted. The localization method based on MUSIC [11] uses a lowfrequency band, 250 MHz, and estimates target location by using spherical mode MUSIC to process theoscillating return signal. Unfortunately, in this method
the array aperture must be comparable to the estimateddistance because of the low frequency, observation periods of over 10 seconds are necessary. DOD/DOAbased MIMO radars using the trigonometry technique[12]-[14] can localize the target by Fourier transforming
the MIMO channel [15]. However, this method needs
to observe the channel for several tens of seconds toaccurately capture human biological information, andthe only target location is estimated.
Fortunately, human activity recognition is possible
by using microwave sensors [16]- [18]. These methods
can estimate human activity in actual environments.
[16] and [17] measure channel state information (CSI)
affected by a human activity, and they identify the activity by using the characteristics of the Doppler shiftof the CSI. However, these methods cannot estimate atarget location, and fail to identify an activity if multiple people occupy the same environment because theDoppler effect of the activity, which is generated bymultiple targets, is mixed. [18] estimates the positionof the object in the house and can be used for activity
recognition. However, this method cannot actually detect actions, because the position is used to identify theactivity. For example, cooking is the activity associatedwith the position of the kitchen.
The authors have already proposed a human posture identification method that uses a MIMO array [19].
This method estimates target location in three dimensions by using the time-differential channel techniqueSubjectHeartbeat

2.1

Respiration

…

…

…

Transmitter

#

…

…

#1
…

…

…

#1

#
ReceiverFig. 1: Concept of microwave system for human activity
identification.

[20][21]; the Doppler RCS is calculated from the powerreflected from the target and the distance between
the estimated location and the receiver/transmitter.
Though the method can accurately estimate human location with the root mean square error (RMSE) of just
0.25 m, it handles only static targets. The authors havealso studied estimating human activity from height andDoppler Radar Cross Section (RCS) information detected by MIMO radar [22][23] that uses a single frequency, 2.47 GHz. However, work reported was a justinitial study, and no formal identification algorithm wasdescribed. To rectify this omission, this paper presents
a complete human activity identification method thatuses MIMO radar. The trajectories of the temporaltransition of the estimated height and the Doppler RCS
are extracted, and a gesture recognition algorithm [24]is applied to the trajectory. Experiments carried out
in an actual indoor environment demonstrate that the
proposed method can identify human activity with anaccuracy of over 80%.
2.

Human Activity Identification Using MIMORadar

The authors previously proposed a fast localization algorithm that estimates subject location by using thetime-variant channel in multi-path environments [20].
In this study, we apply the localization algorithm to theobserved channel, and the three-dimensional location of
the subject is estimated by MIMO radar. The Doppler
RCS is calculated from the received power spectrum
and the distance between the transmitter/receiver,which is calculated from the estimated target location.
This algorithm detects the period of the activity fromthe peaks in Doppler RCS because the Doppler RCS
greatly fluctuates with subject motion. After detecting
the period of the activity, the gesture recognition algorithm [24] is introduced to identify the activity. The
following section explains the method in detail.

Three-dimensional Human Localization

Figure 1 show the concept of our microwave system forhuman activity identification. This study assumes a
MIMO radar with an Mr element array receiver and an
Mt element array transmitter. In a multi-path environment containing one person, the time-variant channel isgenerated by the fluctuations of the human body’s surfaces due to motion, respiration, and heartbeat. Here,the Mr × Mt time-variant MIMO channel is expressedas,
h11 (t) . . . h1Mt (t)


..
..
..
H(t) = ,.
.
.
hMr 1 (t) . . . hMr Mt (t)(1)where, hij is the complex channel response from thej-th transmitter element to the i-th receiver element,
and t represents the observation time. Mr × Mt MIMO
radar can be considered to be Mr Mt × 1 virtual SingleInput Multiple-Output (SIMO) radar [8]. The converted SIMO channel is expressed as,h(t) = [h11 (t), . . . , hMr 1 (t), . . . , hMr Mt (t)]T ,(2)

T

where, {·} means transposition. DOA and DOD of
the target can be estimated using this virtual SIMO
channel, but unwanted path components disturb the
estimates of the human location. The unwanted pathcomponents consist of the direct wave from transmitter to receiver and waves reflected from the wall, floor
and furniture; fortunately, these components are static.
On the other hand, the wave reflected from the humanbody fluctuates because of the user?s motion, respiration, and heartbeat. Therefore, the undesired components are excluded by applying the fast localization
algorithm to the converted SIMO channel. This algorithm uses the time-differential channel, which is defined as,
hsb (t, tsb ) = h(t) − h(t + tsb ),(3)

where, tsb represents the time difference. Here, we calculate the averaged correlation matrix R as,
R = Ri (t, tsb ),(4)H

Ri (t, tsb ) = hsb (t, tsb )hsb (t, tsb ) ,(5)where, {·} is the averaging operator, Ri represents theinstantaneous correlation matrix, {·}H means complex
conjugate transposition. In (4), t and tsb are averaging
parameters. By eigenvalue decomposition, the averaged
correlation matrix R is given by,
R = U ΛU H ,(6)

U = [u1 , ..., uMr Mt ],(7)

Λ = diag([λ1 , ..., λMr Mt ]),(8)where, U and Λ represent the eigenvector and the
diagonal matrix representing eigenvalues, respectively.
Note that the eigenvalues, Λ, are related as follows,
λ1 > λ2 = · · · = λMr Mt = σf2)(9)where, σf2 represents the expected value of thepower of the channel component fluctuation createdby noise. The eigenvector corresponding to noise,[u2 , · · · , uMr Mt ], is expressed as U N because one target
is assumed. In this study, subject location is estimatedby three-dimensional MUSIC with spherical mode vector; an extension of the original MUSIC method [7] tocover the 3D domain. The three-dimensional sphericalmode vector a(x, y, z) is expressed as,a(x, y, z) = at ⊗ ar ,

( ,

(

,

)

Fig. 2: Moving distance, ∆Pi , and the direction of movement, αi .

(10)

at (x, y, z) = [e−j2π/λDt1 , . . . , e−j2π/λDtMt ]T , (11)ar (x, y, z) = [e−j2π/λDr 1 , . . . , e−j2π/λDr Mr ]T , (12)where, at (x, y, z) and ar (x, y, z) are the steering vectors at the transmitting and receiving side, respectively.
⊗ represents the Kronecker product, λ is wavelength,
Dtj /Dr i means the distance between position (x, y, z)
and the j-th transmitter element/i-th receiver element,
respectively. The evaluation function of the MUSIC
method (MUSIC spectrum) is written as,
aH (x, y, z)a(x, y, z).
P (x, y, z) =aH (x, y, z)U N U H
N a(x, y, z)

Fig. 3: Determination of the direction code when the division number of direction code, Nd , is 8.

range equation for σ; this is expressed as,

σ =

(13)

The MUSIC spectrum peak represents the estimatedtarget location.
2.2

Calculating Doppler RCS

The first eigenvector, u1 , of (7) corresponds to target
location. The converted SIMO channel, h(t), (2) andthe first eigenvector, u1 , are multiplied to enhance thebiological component of the target, and this signal, y(t),is expressed as,y(t) = uH
1 h(t)s,(14)

where s is the transmitted signal, which is a continuous wave, i.e. s is a constant value in this study. The
observed signal, y(t), is Fourier-transformed, and is defined as F (ω). Then, the received power, Pr (ω) is expressed as,
2

Pr (ω) =|F (ω)|
,
Mr Mt

(15)

where, ω represents frequency. Here, the Doppler radarcross section (RCS) is defined by solving the radar(4π)3 rr2 rt2
Gr Gt λ2 Pt

Z f2Pr (ω)dω,

(16)

f1

rr =

p

(xe − Xr )2 + (ye − Yr )2 + (ze − Zr )2 , (17)

rt =

p

(xe − Xt )2 + (ye − Yt )2 + (ze − Zt )2 ), (18)where, rr and rt represent the distances of the estimated target location from the centers of the receiverand transmitter antennas, respectively. (xe , ye , ze ) is
the estimated location of the target. (Xr , Yr , Zr ) and(Xt , Yt , Zt ) are the center of the receiver and transmitter antennas, respectively. Pt represents the transmitting power and Gr and Gt are the gain of receiving
antenna and transmitting antenna, respectively. f1 andf2 define the frequency range that encompasses the vital sign effects.
2.3

Human Activity Identification Using Gesture
Recognition Algorithm

We obtain the estimated location and the Doppler
RCS by (13) and (16), respectively. The Doppler RCS
greatly fluctuates during the subject action. Therefore, this algorithm detects the period of the activity
by searching the large Doppler RCS. After detecting
the period of the activity, the gesture recognition algorithm [24] is used to identify the human activity. Then,Dk = d1

Model code:

Direction code

Observed code:∆P1
× Ns ),
∆Psum

(22)

∆Pi ,

(23)

(1 ≤ k ≤Code difference:∆Psum =

No
X
j=1

Standardized code length:
Time

Fig. 4: Concept of comparing model code Dk to observed
code dj .

where, k is the index number in the normalized code.
ii) When the index range is 2 ≤ j ≤ No , the normalized
code Dk is defined as,
Dk = dj ,
Pj

Pj−1
(
the temporal transition of height, zi , and Doppler RCS,
σi , is obtained by continuous estimation, where zi andσi represent the i-th estimated height and i-th calculated Doppler RCS, respectively. Figure 2 shows themoving distance, ∆Pi , and the direction of movement,
αi ; they are defined as,∆Pi =p(σi − σi−1 )2 + (zi − zi−1 )2 ,αi = tan−1

zi − zi−1.
σi − σi−1

(19)
(20)

The trajectories of the height and Doppler RCS aregiven by (19) and (20).
Direction codes are determined from the directionof movement, αi , between the two sampled points on
the trajectory. Figure 2 shows that the direction code
corresponds to the movement, αi ; the division number of direction code, Nd , is 8 in this study. Figure 3
shows the determination of the direction code in thisstudy. However, the number of trajectory points differs
even for the same activity because the speeds of theactivity are not same for all trials. For this reason, thelength of the direction code is normalized regardless ofthe trajectory speed. A normalized code, Dk , is generated from original direction code dj by considering
the code length and the period of the activities, where
the lengths of normalized code Dk and original direction code dj are Ns and No , respectively. Here, code
lengths Ns and No satisfies the relation,
No ≤ Ns .

(21)

The flow of conversion from the original code dj to thestandardized code Dk is shown below.
i) When the index j (j: index number in the raw code)
is 1, the normalized code Dk is defined as,

i=1 ∆Pi∆Psum

× Ns < k ≤i=1 ∆Pi∆Psum

× Ns ).

(24)

Then, the trajectory is converted into the direction
code. The training data of all actions is made andidentified. An observed trajectory is converted into theappropriate direction code, and the deviation from all
training codes is calculated. The model codes were generated from the most frequent values of the feature as
discovered in 10 trials for each motion, and the generated model code is defined as Dmodel .
Figure 4 shows the concept of comparing the model
code and an observed code, where the normalized codeof the observed activity is defined as dtest . The sum ofsquared deviation, E, which is used for activity identification, is defined as,
NsXδd2l ,(25)

δdl = |dtest,l − Dmodel,l |,

(26)

E =

l=1

where, dtest,l and Dmodel,l represent the l-th direction
codes of the observation code and the model code, respectively. However, since the direction code is circular
as shown in Fig. 2, the maximum difference in the direction code that can be taken is one half of the direction
division number Nd , i.e. 4 in this study. Therefore,
when δdl ≥ Nd /2, δdl is replaced as follows,
dl = Nd − δdl .

(27)

The sum of squared deviation, E, is calculated for themodel code of each activity, and the current action isclassified to one of the learned actions by finding thetraining code with minimum deviation from the current
one.
The agility and accuracy of the estimation havea trade-off relationship. In this study, the observation
time should be short to detect human activity, but then

Table 1: Measurement conditions.
Antenna element (Tx/ Rx)
Distance between Tx and Rx, dHeight of the Tx/ Rx, hFrequency
Tx power
Snapshot rate
Channel measurement period
Interval of localizationand calculating Doppler RCS

Partition

16-element patch antenna
4m
1.0 m
2.47125 GHz-10 dBm
100 Hz0.64 sWindow

Y [m]
4.0
3.0
2.0

0.25 s

7.0 m

1.0
m

Tx arrayRx array

Door

Receiver

0
0

Z
…

Shelf

m
Y

…

SP64T

X

Transmitter
2.0
3.0
X [m]

4.0

Desk
6.0 m

LNA
…

Amplifier1.0

Fig. 7: Experimental room with concrete walls; its width,
depth and height were 7.0, 6.0 and 2.7 m, respectively.

Down
converter
I1 Q1…

I16 Q16

Data acquisition
unit (DAQ)

Signalgenerator

Fig. 5: Measurement overview.
(a) Sitting down(b) Falling down

Patch antenna(d) Getting up
from a bed(c) Standing up
from a chair

FixturePTFE substrate

Fig. 8: Human activities tested.

Fig. 6: Photo of MIMO array.

the estimated values vary widely. Therefore, a filtering
technique is applied to the estimated values to improve
identification accuracy.
3.

Experimental Condition and Measurement
Setup

Table 1 and Figure 5 overview the measurement setup.
The element space of the arrays of receiver and transmitter was one half the wavelength. The distance between transmitting and receiving antennas was set to
4.0 m. The receiver and the transmitter were directedto the center of the room. As shown in Figure 5, aSingle-Pole 64 Throw (SP64T) switch was used at thetransmitting side. Though the exact observation moment is not the same for all elements in the MIMO
channel matrix, the time differences among the ele-ments are so short compared to the vital and moving
activity that they can be ignored. A continuous wave
(CW) signal of 2.47125 GHz was used; the transmitting power was set to -10 dBm. The CW signal was
split to the down-converter at the receiver side sinceaccurate synchronization between transmitting and receiving sides is needed. At the receiver side, received
signals are input to a down-converter unit by way of a
Low-Noise Amplifier (LNA) unit. The down-converted
baseband signals (I1 , Q1 ,∼I16 , Q16 ) were digitized by adata-acquisition unit (DAQ) with sampling frequencyof 20 kHz. The snapshot rate of the MIMO channel
is determined by the switching speed of the SP64T.
In the experiments, the rate for taking a snapshot of
the MIMO channel was set to 100 Hz. In this study,
the observation period for localization and calculating
Doppler RCS was set to 0.64 seconds. Thus the numberof snapshots per single observation period was 64. The

2

0.00351.8

0.00250.002
0.00150.001

Standing

0.00050

2

Sitting down

0.025
0.02

1.2

0.015

1

10

Fig. 9: Example of the time-variant channel response fortarget activity of sitting down.

transmission components of S parameters were used as
the propagation channel. Figure 6 shows a photo of thearray antenna used as the receiver and the transmitter.
16 × 16 MIMO radar was used in the experiment. The
receiver and transmitter have 4×4 patch antennas on asquare grid. All array antennas used a PTFE substrate,
and antenna thickness, width, and height were 1.6, 60
and 240 mm, respectively. These elements have a vertical polarization. The array’s center was set to h = 1.0m, which corresponds to the torso height of the subjects. The number of targets was determined following
the MUSIC method. The normalized code length Ns
was set to 100. In this study, time difference tsb was
set to 0.05 [s] ≤ tsb ≤ 0.63 [s] for localization, while therange of frequency was set from 1.56 Hz to 10.94 Hz
in calculating the Doppler RCS; antenna gain Gr andGt was 4.96 dB, which is the average gain value from−40◦ to 40◦ . Figure 7 shows the experimental room.
The experiment was carried out in a room containing
desks and shelves. The room had concrete walls andits width, depth and height were 7.0, 6.0 and 2.7 m,
respectively. The measurement location of the target
was set to (x, y) = (2.0, 2.0) [m].
Figure 8 shows the human activities tested in thiscampaign. While the channels were being captured, thesubject performed 4 activities: sitting down (a), falling
down (b), standing up from a chair (c), and getting
up from a bed(d). The model codes were generated
from the most frequent values of the feature observedin 10 trials for each motion, and the recognition ratewith all motions was calculated using 10 recognition
experiments (all activities).
4.

0.03

Doppler RCS

1.4

0.6
8

0.035

0.01

0.8

Sitting on the chair

4
6
Observation time [s]Estimated height

1.6

Experimental Results

Figure 9 shows an example of the time-variant channel
response, h11 , of the observed channel when the target
sat down. In this figure, the channel variation exhibits
the periodic biological activities such as respiration before and after action. Obviously, the channel widely

0.005
0

5

10

15
20
25
30
Estimation number

35

40

0

Fig. 10: Temporal transition in estimated height and the
Doppler RCS.

1.2

Startup position
Sitting down

1

Estimated height, z [m]Amplitude

0.003

0.04

Doppler RCS [m2]

Estimated height [m]

0.004

0.8
0.6

Falling down

0.4
0.2
0

0.05

0.1
0.15
Doppler RCS,

0.2

[

0.25

0.3

]

Fig. 11: Example of the trajectory of the estimated heightand Doppler RCS for subject activities of sitting
down and falling down.

Table 2: Average recognition rates of the human activity
for all filters.
Filter
No filter
Median (fourth-order)
Moving average(fourth-order)

Recognition rate
82.5%
80%
92.5%

changes during sitting down.
Figure 10 shows the temporal transition in the estimated height and the Doppler RCS when the channel of Figure 9 was used. This figure shows that thehuman activities yielded large Doppler RCS. The estimated height values were about 1 meter in the first half,
and the estimated height values were lower than previous state after sitting down. On the other hand, theestimated height exhibits significant variation becauseof the short observation time.
Figure 11 shows an example of the trajectory of
the estimated height and Doppler RCS when the subject sat down and fell down. Note that the subject
was standing still before those actions, and the figure
shows that the start values of the estimated height are1.2
Estimated height [m]1.2

Getting up from a chair
Estimated height, z [m]

1
0.8
0.6

Startup position

0.4

1
0.8
0.6
0.4
0.2
0

Getting up from a bed

0

0.2
0.1
0.15
Doppler RCS,

0.2

[

0.25

Fig. 12: Example of the trajectory of the estimated heightand Doppler RCS for the activities of standing up
from a chair and getting up from a bed.

clustered near 1.0 m. The two activities yielded different trajectories. With activity completion, the estimated heights of sitting down and falling down clustered around 0.7 m and 0.2 m, respectively.
Figure 12 shows an example of the trajectory of theestimated height and Doppler RCS for the subject activities of standing up from a chair and getting up froma bed. The initial positions for these activities weresitting on the chair and lying on the bed, respectively.
The figure shows that the initial values of the activitiesof standing up from a chair and getting up from a bed
clustered around 0.7 m and 0.3 m, respectively. The
action of the standing up from a chair showed a sharp
trajectory. On the other hand, the action of getting
up from a bed yielded a trace like a circle. After theaction, the both estimated heights gathered near 1.0 m.
Fig. 13 shows an example of the trajectory of theestimated height and Doppler RCS when the target sat
down. Figure 13(a) show the trajectory without any
filter, Figure 13(b) and (c) show the results processedby the fourth-order median filter and the fourth-ordermoving average filter, respectively. In this figure, thetrajectories yielded by using raw values vary widely because the observation time used to estimate location
and Doppler RCS is short. On the other hand, thefiltered trajectories exhibit no outliers and reduced dispersion.
Figure 14 shows the training code and test data
for the activity of sitting down. The training code wastaken to be most frequent value recorded in 10 trials.
This figure shows that the test data well agrees withthe training code, and in this test the activity was correctly identified as sitting-down motion. Table 2 shows
the averaged recognition rate of the human activity for
all filters. The averaged recognition rate is the meanvalue of the identification rate of all motions. In thistable, the recognition rate with no filter was 82.5%.

0.4
]

0.5

0.4
]

0.5

0.4
]

0.5

(a) No filter.

0.3

]

1.2
Estimated height [m]

0.05

1
0.8
0.6
0.4
0.2
0

0

0.1
0.2
0.3
Estimated RCS [(b) Median filter.

1.2
Estimated height [m]

0

0.1
0.2
0.3
Estimated RCS [

1
0.8
0.6
0.4
0.2
0

0

0.1
0.2
0.3
Estimated RCS [(c) Moving average filter.
Fig. 13: Example of the trajectory of the estimated heightand Doppler RCS with/without filter.

In comparison, the averaged recognition rate with thefourth-order median filter was 80% (lower than the ratewith no filter); while the result with the fourth-ordermoving average filter was 92.5%, a significant improvement in the recognition rate.
Fig.15 shows the confusion matrix of the recognition rate of the result with the moving average filter.
From this figure, all recognition rates exceeded 80%.
To validate the effectiveness of the proposed method,
we evaluated the recognition performance of anotheralgorithm, where the temporal correlation between the
training and test data is used. In this algorithm, the observed activity is classified as the training activity withmaximum correlation. Figure 16 shows the confusion

Predicted activity

10
Sitting
down

Direction Code8

Direction code
(test data)

Falling
down
Actualactivity Standing up
from a chair

Training code

6

Getting up
from a floor

4

Sitting
down

Falling
downStanding up Getting up
from a chair from a floor

0.4

0.6

0

0

0.3

0.6

0

0.1

0.4

0.2

0.4

0

0.3

0.2

0.1

0.4

Fig. 16: Confusion matrix of the recognition rate of the raw
observation channel.

2
0
0

20

40
60
Normalized Number

80

100

10

Model code:
Standing up from chair

Fig. 14: Training code and test data.

Observed code:
Getting up from floor

Predicted activity
Sitting
down

Falling
down

Sitting
down

1

0

0

0

Falling
down

0.1

0.8

0

0.1

0

0

0.9

0.1

0

0

0

1

Actualactivity Standing up
from a chair
Getting up
from a floor

Standing up Getting up
from a chair from a floor

Direction code

8
6
4
2
0

Fig. 15: Confusion matrix of the recognition rate of theresult with moving average filter.

matrix of the recognition rate of the correlation-basedalgorithm. From this figure, the maximum recognition
rate was 60% for falling down, and the average recognition rate was 45%. This result shows that the proposed
method achieves highly accurate activity identification.
Figure 17 shows an example of the prediction error. In this figure, the observed activity was getting up
from the floor, while the identified activity was standing up from a chair. As can be seen from the figure,the model codes of both activities are similar, and thiscaused prediction error. These errors can be reducedby estimating the posture before and after the activity
and to correct incorrect decisions.

Model code:
Getting up from floor

0

20

40

60

80

100

Normalized number
Fig. 17: Example of prediction error, when the activity was“getting up from the floor”, and this was identified
as “standing up from a chair”.

average for the classification of four common activities:
sitting down, falling down, standing up from a chair,and getting up from a bed. This proves that the proposed method well estimates human activities in thehome. This paper assumes an environment containing only one target, while a realistic environment often
contains multiple targets. When there are multiple targets in the same environment, the targets’ signals are
mixed in the observed channel. Therefore, the humanactivity identification requires to distinguish the multiple targets’ signals. In our future work, we will study
an algorithm for detecting multiple targets’ activities
in a realistic environment.
References

5.

Conclusion

This paper proposed and demonstrated a human activity identification method that is based on estimating the height and Doppler RCS information fromMIMO radar data. In this paper, we introduced threedimensional localization and calculation of the Doppler
RCS. A gesture recognition algorithm was applied tothe trajectory of the temporal transitions of the estimated height and the Doppler RCS. Experiments
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