AUTOMATIC SEGMENTATION OF TRIAXIAL ACCELEROMETRY

SIGNALS FOR FALLS RISK ESTIMATION

Presenter: Bing-Soug He Adviser: Cheng- Jui Hung

2010/12/151

Stephen J. Redmond, Member, IEEE, Maria Elena Scalzi, Michael R. Narayanan, Stephen R. Lord,Sergio Cerutti, Fellow, IEEE, Nigel H. Lovell, Senior Member, IEEE

Outline ABSTRACT

INTRODUCTION

METHODS

RESULTS

DISCUSSION AND CONCLUSION2

Abstract

In recent years, falls detection technologies have become more common.

However, very few have adopted a preferable falls prevention strategy through unsupervised monitoring in the free-living environment.

The basis of the monitoring described herein was a self-administered directed-routine (DR) comprising three separate tests measured by way of a waist-mounted triaxial accelerometer.

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INTRODUCTION

Falls are one of the greatest risks facing the elderly population. In the over 65 age group, accidents are the fifth highest cause of death , and approximately two thirds of accidents are falls.

The most promising emerging telehealth technologies triaxial accelerometer (TA) or gyro-scopic.

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INTRODUCTION Clinical research groups have developed models, such as the

Physiological Profile Assessment (PPA)

gold-standard：Very low risk (-5), up to marked risk (5).

The automated segmentation algorithm will enable unsupervised falls risk assessment, in that the DR may be executed in the home environment at regular intervals.

PPA:生理狀況評估 5

METHODS A. Directed routine：

68 subjects were recruited (Age: 80.01 ± 4.48 years).

The DR refers to a set of movements and assessment tasks that can ostensibly be performed unsupervised.

(1)Timed Up-and-Go Test (TUGT)：Measures the time required for a person to rise from a chair, walk 3 m, turn around (180), walk back to the chair and sit down.

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METHODS

(2)Alternate Step Test (AST)platform (19 cm high and 40 cm wide) and alternatively placing the whole left foot on the platform and then replacing it on the floor, then place the whole right foot on top of the platform and then back to the floor again.

(3)Sit-to-Stand with five repetitions (STS5)

test is often used as a measure of lower limb strength and it consists of performing five sit-to-stand movements as quickly as possible.

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METHODS

B. Signal segmentation：least-mean-squares trained linear model.

In order to analyze signals recorded in the unsupervised environment automatic segmentation must be performed.

L denotes the total number of samples recorded in each signal.

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METHODS

(1)TUGT time markers: using a 0.5 s wide sliding window with a 50% overlap.

Using cubic splines, and resampled at the original sampling rate of 40 Hz, to obtain a feature vector,at each sample.

Performed to minimize the criterion function: b=w[n] mean value, over a certain range depending on the value of [n].

.

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METHODS

The three ranges are Using 2 Hz low-pass filtered forward-backward filtering

.

The determination of and is the most difficult aspect of segmenting the TUGT signals. during the turn-about at the 3 m mark and during the final turn-about and sit action, a repetitive signal is not anticipated, and alignment is not expected.

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METHODS

A simple squared error measure, e[n], of how the signal matches the shifted version of itself is calculated. e[n] should peak near and , and again at, indicating that a repetitive signal is not present at these times.

Before searching for these times e[n] is smoothed using a 0.5 s Hamming window FIR filter (filter order: 20; 0.5 s × 40Hz). Finally, the is estimated using e[n].

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METHODS

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METHODS

(d)(e)using a 0.5 s Hamming window FIR filter (filter order: 20; 0.5 s * 40Hz).

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METHODS

(2) AST time markers: The nine event markers for the AST are denoted(assuming the subject always leads with the left leg).

The start time for the first step, is found by maximizing , where , the estimated duration of the test.

Finally, the time markers are refined using two search procedures.

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METHODS

STS5 time markers:We denote the start times of each of the 5 sit-to-stand transitions as and . The since the subject is instructed to perform five repetitions of the movement.

The magnitude of the low-pass filtered signals (filtered at 3 Hz) ,using high-pass filtered at 0.1 Hz to remove the gravitational constant.

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METHODS

C. Falls risk estimationThe subjects’ falls risk estimates are obtained using feature subset selection, cross-validation and a linear least mean squares model.

A search is also performed to find a new subset of features which are more robust to timing errors introduced by any imperfections in the automatic segmentation algorithms.

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RESULTS

TABLE IMEAN, STANDARD DEVIATION,

MINIMUM AND MAXIMUM SEGMENTATION ERRORS.

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RESULTS

TABLE IIRESULTS OF ESTIMATED FALLS RISK, USING VARIOUS FEATURE SUBSETS AND SEGMENTATION TECHNIQUES.

RMSE:均方根誤差18

DISCUSSION AND CONCLUSION

We must recognize that human reaction time will account for a certain portion of the errors observed. However, the manually performed human annotation still provides the best performance in terms of falls risk estimation.

While it is evident that the TA signals contain the relevantinformation for falls risk estimation, a more robustapproach must be adopted.

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FUTURE WORKS

Use a better algorithm to reduce the error.

Made to the seniors for risk estimation of three-axis accelerometer.

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Thank you for your attention

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