This article has been accepted for publication in IEEE Intelligent Systems but has not yet been fully edited.
Some content may change prior to final publication.
Adaptive Body Posture Analysis Using Collaborative Multi-Sensors for Elderly Falling
Detection
Chin-Feng Lai1, Yueh-Min Huang1, Jong Hyuk Park2 and Han-Chieh Chao3
National Cheng Kung University Taiwan1, Kyungnam University Korea2,
National Ilan University Taiwan3
[email protected], [email protected], [email protected], [email protected]
–”‘†—…–‹‘
There are many studies of elderly body posture analysis for falling detection, while most employed image
detection or used single acceleration sensors for identification [1][2][3]. Recognition by image detection has
high operational complexities, and infringe on privacy. Single acceleration sensors have high accuracy in body
posture analysis, however, the accuracy of judging body posture requires highly complex operations, and thus,
cannot provide real-time notice for body posture detection. This study explored the collaborative detection of
body behavior modes and accidental falling incidents by using collaborative sensors. Information is provided by
sensors distributed over the body that transmit positions to analyze and recognize hominine motion. Under
gravity, direction of force on each limb of the body varies. These characteristics are utilized to study the
collaborative detection of sensors. As everyone has different living habits, manifestations of poses will differ as
well, therefore, we use adaptive adjustment model to detect elderly body postures more accurately.
The remainder of this paper is organized as follows. Section 2 introduces the recent research about elderly
falling detection. Section 3 presents the system architecture of the Adaptive Body Posture Analysis System, as
well as Collaborative Accelerometer Sensors, Body Posture Analysis, Adaptive Adjustment Model and Falling
Detection. Section 4 presents the results. Section 5 gives the conclusions.
‡Žƒ–‡†‘”
In the past, various solutions have been proposed to detect the falling of senior citizens. One solution is to
have elderly raise alarms when they meet accident by pushing a button on a wearable device [4][5]. At the same
time, the device will notify the hospital through an alarm. But the solution depends on the senior member’s
capability to push the button for raising the alarm. If the elderly loses consciousness, this solution is useless.
Video and audio monitoring is usually preferred, but these solutions are restricted to a fixed area and fixed
amount of equipments [6][7]. Moreover, due to the privacy considerations, it is not adequate to monitor the user
all day long. Besides the two kinds of solutions mentioned before, accelerometers sensor is also applied to
falling detection [8][9][10]. By using the data returned by the sensors, it is possible to calculate the possible
pose and motion of the user. In recent research, related solution has been proposed. However, without taking
personalized falling detections and body reaction after falling down into account, such a solution still cannot be
widely applied. Therefore, we proposed Adaptive Body Posture Analysis Using Collaborative Multi-Sensors for
Elderly Falling Detection to provide a more accurate way to detect the senior citizen’s tendency of motion and
determine the possible injured parts of the body.
Digital Object Indentifier 10.1109/MIS.2010.2
0885-9000/$26.00 © 2010 IEEE
This article has been accepted for publication in IEEE Intelligent Systems but has not yet been fully edited.
Some content may change prior to final publication.
†ƒ’–‹˜‡‘†›‘•–—”‡ƒŽŽ›•‹•›•–‡
Figure 1 presents the overall archhitecture of our system. It includes four phasees: collaborative
accelerometer sensors, body posture analysis,
a
falling detection and adaptive adjusttment model. Firstly,
Collaborative Accelerometer Sensors for calculating the six tri-axial G-Sensor are described, then use Body
Posture Analysis to identify the elderlly motions. Finally, we propose Adaptive Adjjustment Model to retrieve the
result according to the past analyses, and then detect the falling event more accuraately.
Figure 1 System Module Architecture
Phase 1: Collaborative Accelerometter Sensors
Due to earth’s gravity, all objeccts experience gravitational pull to the earth’’s center, and the acceleration
unit of this pull is referred to as “g” or
o “g force”. All objects are subject to 1g accceleration to the earth’s center;
this is an important reference consttant for calculation of azimuth, inclination angle, etc. When the biaxial
G-sensor is inclined, axes X and Y produce
p
acceleration under gravity, then AX,, AY, and gravity acceleration
form a right triangle, as in Figure 2. As
A shown in Eqs. 1 and 2, in order to obtain inclination
i
angle ș, the inverse
trigonometric function can be applieed, as in Eq. 3. However, when both axes, X and Y, are perpendicular to
gravity acceleration, both AX and AY
A are zero, indicating that the G-sensor is
i parallel to the ground, and
without inclination angle.
F
Figure
2 Inclination of Biaxial Acceleration
AX = sin θ
----------------- (1)
Digital Object Indentifier 10.1109/MIS.2010.2
0885-9000/$26.00 © 2010 IEEE
This article has been accepted for publication in IEEE Intelligent Systems but has not yet been fully edited.
Some content may change prior to final publication.
AY = cos θ ----------------- (2)
θ = arctan
AX
AY
------------ (3)
Calculation of the tri-axial G-Sensor inclination angle is the same as that of the biaxial G-Sensor, as in
Figure 3. And its calculation equations are shown in Eqs. 4, 5, and 6, where ȡ denotes the inclination angle of
axis X, with respect to ground level; ij denotes the inclination angle of axis Y with respect to ground level; and
ș denotes inclination angle of axis Z with respect to gravity acceleration.
Figure 1 Inclination of Triaxial Acceleration
§
AX
·
¸ ----------------------------------------------- (4)
¨ A2 + A2 ¸
Z ¹
© Y
§
AY
ρ = arctan ¨
·
¸ ----------------------------------------------- (5)
¨ A2 + A2 ¸
X
Z ¹
©
φ = arctan ¨
§ A2 + A2
Y
X
¨
AZ
©
θ = arctan ¨
·
¸ ----------------------------------------------- (6)
¸
¹
Sum square of acceleration speed of each axis of tri-axial G-sensor, and obtain the square root, to find out
the total acceleration SVM, as in Eq. 7.
SVM =
AX2 + AY2 + AZ2 ------------------------------------------------ (7)
When a tri-axial G-sensor is stationary, the total acceleration S must be constant, as in Eq. 8, because of
gravity acceleration:
SVM =
AX2 + AY2 + AZ2 = 1g -------------------------------------------- (8)
Dividing the body into upper (above waist) and lower body (below waist) can demonstrate daily behaviors
of inclination angles, with respect to ground level in all positions, the halves possess similarities and differences.
For example, standing and sitting positions are similar in upper body, but different in lower body. Also, sitting
and lying positions are different in upper body, but similar in lower body. According to such similarities and
differences of various positions, body positions can be detected. This study fitted G-Sensors to six points of the
elderly body: neck (N), waist (W), left wrist (HL), right wrist (HR), left thigh (FL), and right thigh (FR), as
shown in Figure 4, and defined the front and back side of all sensors in respect to body location as: axis-X,
left-right is axis-Y, up-down is axis-Z. The system simultaneously collects information from these G-Sensors at
the six positions to judge body pose.
Digital Object Indentifier 10.1109/MIS.2010.2
0885-9000/$26.00 © 2010 IEEE