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RANGE-BASED ALGORITHM FOR POSTURE CLASSIFICATION AND FALL-DOWN DETECTION IN

SMART HOMECARE SYSTEM

Konlakorn Wongpatikaseree Azman Osman Lim Yasuo Tan Hideaki Kanai

Japan advanced institute of science and technologyJapan advanced institute of science and technology

Outline1. Introduction

2 Problem of existing researches2. Problem of existing researches

3. Objective

4 Proposed ideas4. Proposed ideas

• Range-based algorithm

5. Experiments and results

• Experimental setup

• Experimental results

6. Conclusion

7. Future works

Konlakorn Wongpatikaseree10/5/2012 1

1. Introduction• A growing elderly population and strong demand for efficient in-

home healthcare have fueled a growth in smart homecarehome healthcare have fueled a growth in smart homecare system.

W h i i i l f hi h b• We propose a human monitoring service platform, which can be also used for the applications of human behavior monitoring and human positioning.

• The proposed platform architecture is composed of three parts: S i• Sensing

• Algorithm• Database.


2. Problem of existing researches• Poorer performance results when…

• Involve less physically characteristic movements I l li l i di ill• Involve little motion or standing still

(a) Differences in feature values computed from FFTs * (b) A set of sample accelerometer signal for the seven activities **

* L. Bao and S.S. Intille, “Activity recognition from user-annotated acceleration data,” In Pervasive Computing, pp.1-17, Vienna, Austria, 2004. ** M. Lee, A. M. Khan, J. Kim, Y. Cho and T. Kim, “A Single Tri-axial Accelerometer-based Real-time Personal Life Log System Capable of Activity

Classification and Exercise Information Generation,” In: 32nd EMBS, pp. 1390-1393, Buenos Aires, Argentina, September 2010.


3. Objectives• Introduce a new algorithm for human posture classification,

called range-based algorithm.called range based algorithm.• Simple technique.

• High accuracy• High accuracy.

• Classify basic human posture.Classify basic human posture.• Sit, Stand, and lie down.

• Detect the unexpected situation.• Fall-down


4. Proposed ideas• Each posture has different physical pattern.• Concept of this algorithm is determines the relation of body p g y

parts, and extracting the activities from the range between body parts.

• Sensors are attached on shoulder hip and knee• Sensors are attached on shoulder, hip, and knee.


4. Proposed idea

4.1Range-based algorithm• Four techniques are adopted in the range-based algorithm.1. Decision tree

2. Finite State Machine

3 Posture Window3. Posture Window

4. Posture Pattern Recognition


4.1 Range-based algorithm

4.1.1 Decision tree• The tree has 5 nodes, 3 of which are leaf nodes and 2 of which represent a binary decision. p y

• For each branch of the tree, the threshold value was defined byd d by• Proportion range between sensors for range-based algorithm • Heightʼs shoulder of user and visual inspection for height-based g p g

algorithm T1

T1

T2

T2

StandStand

SitSit Lie downLie downSitSit Lie downLie down



4.1.2 Finite State Machine• 9 states; 5 changing posture states, 4 posture

states.

Stand Fall Fall down

Sit -> StandSit -> Stand

Stand -> Sit

Stand -> Sit Stand -> Lie

downStand -> Lie

down

SitLie down ->

SiLie down ->

Si Lie downSitSit

Sit -> Lie Sit -> Lie downdown



4.1.3 Adaptive Posture Window Scheme• Adaptive Posture Window Because the results of classification change rapidly, the adaptive posture

window scheme is adopted to identify the current posture and make the system more reliable.

4 states/Adaptive Posture Window

St St St St

St St St Sit L Sit Sit L

Sit Sit L LStand

Stand

Sit -> Lie down

SitSt St St Sit

St Sit Sit Sit

L Sit Sit LStand

Sit

Sit

St St Sit L Stand



4.1.4 Posture Pattern Recognition (1/3)

• From the adaptive posture window, there are three fundamental patterns occurred.• Majority pattern

• Equal pattern

• Minority pattern

• To recognize the resultant state, we use two methodsmethods.• Ratio method

T i i i h h d• Transition weight method


5. Experiments and results• In our experiment, the ultrasonic sensors are used to collect the

height data.

• It is completely different with existing researches in term of type of data.

• We setup the height-based algorithm, which used the same t ith th b d l ithresources to compare with the range-based algorithm.



5.1 Experimental setup• The idea of the height-based algorithm uses one sensor attached

on the userʼs shoulderon the user s shoulder.

• The decision tree is similar to the range-based algorithm, but it is different as to the threshold value.

• The activity window, the activity pattern, and the FSM are same.



5.2 Experimental results• Four experiments are proposed to evaluate the range-

based algorithmbased algorithm.

1. Adaptive posture window experiment1. Adaptive posture window experiment2. Static posture experiment3. Consequence posture experiment4. Fall-down detection experiment


5.2 Experimental results

5.2.1 Adaptive posture window experiment• Size of adaptive posture window is changing from 3 to 6 posture

states.

• Time sampling is defined to 0.2 seconds.



5.2.2 Static posture experimentPosture Height-based

AlgorithmRange-based

AlgorithmStanding 100% 100%

Sitting (type A) 100% 100%Sitti (t B) 0% 100%Sitting (type B) 0% 100%

Lying down (type C) 100% 100%Lying down (type D) 0% 100%Lying down (type D) 0% 100%

• Type A: user sitting on object that is lower than hipʼs height.• Type B: user sitting on object that is higher than hipʼs height.Type B: user sitting on object that is higher than hip s height.• Type C: objectʼs height that user lying down is lower than 15 cm.• Type D: objectʼs height that user lying down is greater than 15 cm.



5.2.3 Consequence posture experiment(1/3) Lie down -> Stand -> Sit -> Sit -> Stand-> Stand -> Lie down

Stand Fall Fall down

Sit -> StandSit -> Stand

Stand -> Sit

Stand -> Sit Stand -> Lie

downStand -> Lie

down

Sit

Lie downLie down ->

SitLie down ->

Sit Lie down

Sit -> Lie down

Sit -> Lie down



5.2.3 Consequence posture experiment(2/3) • Focus on the changing posture state.

Consequence postures Height-based Range-basedLi d St d Sit St dLie-down→Stand→Sit→Stand→

Lie-down→Sit→Stand 86 % 98 %

• The main error of both algorithms occurs in state “Lie down >Sit”Lie-down->Sit



5.2.3 Consequence posture experiment(3/3)



5.2.4 Fall-down detection experiment• It is extremely important for monitoring the unexpected

situation, especially old people who live alone in the home.

Algorithms Accuracy of FallAlgorithms Accuracy of Fall Detection *

Height-based 80 %Range-based 100 %

* Test five times


6. Conclusion• The range-based algorithm is to measure the distance

between body partsbetween body parts.

• The results showed the high percentage of posture classification.

• This algorithm are applicable because it does not depend on any type of sensor.


7. Future works• The range-based algorithm has been required additional

techniques to classify more specific activity

• We also plan to use this information for analyzing the human behavior in the healthcare system.


Th k f tt tiThank you for your attention


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