Abstract—This paper presents a design fo

detection and classification of eight dailycollected with two tri-axial accelerometers, oright part of the hip and the other one moupart of the right leg. This classifier gave goodin controlled laboratory experiments, in wsubjects carried out a set of eight basic movem

Keywords—acceleration data, activity recogniextraction, tri-axial accelerometer.

I. INTRODUCTION UE to the last decade detecting andmovements were discovered to be factors in domains like biomedical e

sports, cinematography, agriculture. Acceledevices, low cost, and provide enough inforin applications which need both acceleratiand accelerations due to body movement. Tvery useful for detection of daily movement

Zang et al. [1] combined a tri-axial acccell phone to use it for fall detection with KDiscriminant) Algorithm. Fall detection wafor the elderly people who were living alone

Brusey et al.[2] used tri-axial accelerodisposal suit. The combination of the protecphysical activity, high ambient temperatuairflow can cause the operative’s tempedangerous levels during missions. The efmissions is dependent on the operative’s pproperly assess thermal state, temperaturesystems need to take posture into accodecision tree algorithm was able to obta97.2% for classification of postures.

Robert et al.[3] made an evaluation of accelerometers to monitor and classify becattle. Behavior or activity has been linkestatus of animals and they found that accelean objective, non-invasive measure of actlinked to specific animal health or performa

The objective of this paper is to classpostural orientations of subjects using pneural networks from data collected waccelerometers one mounted on the right p

Manuscript received March 5, 2011. This work wa

the Project development studies Ph.D. in advanced tecPOSDRU/6/1.5/S/5 ID767.

Authors are with the Technical University of ClElectronics, Telecommunications and Information Tecno. 26-28, Cluj-Napoca, Romania (e-mails: Ioana.FElena.DORAN@bel.utcluj.ro).

Detection of Collected with

Fark

D

or a classifier using y movements data one mounted on the unted on the lower

d accuracy of 99.8% which four healthy ments.

ition, feature

d monitoring daily a very important

engineering, army, erometers are small rmation to be used ion due to gravity Therefore they are ts. celerometer with a

KFD (Kernel Fisher as made especially e. ometers for bomb ctive suit’s weight, ures and restricted eratures to rise to ffectiveness during posture. In order to e-based assessment ount. Using C4.5 in an accuracy of

three-dimensional ehavior patterns in ed to the wellness erometers provided tivity that may be ance outcomes. sify activities and

pattern recognition with two tri-axial part of the hip and

as supported in part by chnologies “PRODOC”

luj-Napoca, Faculty of chnology, str. G. Baritiu ARKAS@bel.utcluj.ro,

the other one mounted on the lo

II. METHO

A. Instrumentation The system used to collec

Cogent Computing Applied RUniversity, and is described by[4]-[6].

Two tri-axial accelerometersmeasurements. This system wVerdex XM4-bt as a main footprint of 80 x 20 x 6,3mm a400MHz Marvell PXA270 X16MB of flash memory, Bluetoand it also provide USB host, aa 120-pin MOLEX connectconnector.

Two acceleration sensor boGumstix device via an expansibus connection, and connectsconnector.

The microcontroller is a while the accelerometer useLIS3LV02DQ which is capabover a bandwidth of 640Hz for

The peak current consumpvoltage 2.16-3.6V and shut dmicroamperes.

The data collected from thefrom Gumstix via Bluetooth to was provided from 4 batteries o

Using wireless method, we mobility of the subject or locaFig. 1 represents the system and

Fig. 1. System used to collect data and

f Daily Movements Froh Two Tri-Axial Accelekas Ioana-Iuliana and Doran Rodica-Elena

ower part of the right leg.

ODOLOGY

ct the data was developed at Research Centre, at Coventry y Brusey and Rednic et.al [2],

s were chosen to perform the was composed of a Gumstix processing platform, with a

and weight of 8 g, containing a Scale CPU, 64MB of RAM, ooth communications on-board a 60-pin Hirose I/O connector, tor and 24-pin flex ribbon

oards were connected to the ion board, which provides I2C s to the Gumstix via Hirose

Microchip PIC24FJ64GA002 d is a ST Microelectronics ble of measuring acceleration all axes. ption is 0.65-0.8mA, supply own mode consumption 1-10

e accelerometers were passed a computer. The power supply

of 1.8V each. don’t have restrictions on the

ation of the monitoring device. d sensor body placement.

the body sensors placement.

om Data erometers

978-1-4577-1411-5/11/$26.00 ©2011 IEEE TSP 2011376

B. Algorithm Development The algorithm of this classification is bui

decision tree using pattern recognition neura binary decision tree means that all decisiohave exactly two branches. The movemenfrom the most specific, to be placed inside ttree.

Neural networks have been trained to functions in various fields, including paidentification, classifications, vision and con

The algorithms for each branch of the dbased on pattern recognition neural recognition neural network need a target vvector. Target vector is composed of an aone value “1” and the remaining values arthe values in the array represents the numbe

First branch of the tree is composed of pperiods of activity. The branch of activityperiods of walking and periods of crawlinseparated into periods of upright and periodbranch is separated into periods of sittikneeling.

The branch of lying is separated into lydown, lying with the face up and lying leaves of the tree is represented by thestanding, kneeling, lying with the face dowface up, lying on one side , walking and kne

First branch of the tree was separated intby applying a high pass filter to the origisplitting the data into overlapping windowswindow signal magnitude area and avercalculated and compared with preset thretarget vector.

Now that we obtained the target vector vector to be able to apply pattern recognitioThe input vector is actually a features composed of eight features calculated for e6axes x 8features = 48) for each window wihigh pass filter. Because the input vector hof data we applied principal component athis number.

In the next step the data are divided into periods of activity and we find the targwindow by applying a threshold to the fvertical axis.

For periods of activity and for periodsagain a threshold to the vertical axis and weof rest into periods of lying and upright. Wfrom the accelerometer mounted on the lowto calculate target vector for kneeling, sittinwith the face up, walking and crawling.

For lying with the face down and lyinfeatures from the tri-axial accelerometer mopart of the hip were used to compute tfeatures used for the input vector were the accelerometer mounted on the right part of t

The principle for computing target vecactivity and periods of rest is shown in Fig.

ild around a binary al networks. Using

on nodes of the tree nts will be ordered the binary decision

perform complex attern recognition, ntrol systems. decision nodes are network. Pattern

vector and an input array that contains re “0” (number of

er of classes). periods of rest and y is separated into ng. Rest branch is ds of lying. Upright ing, standing and

ying with the face on one side. The

e postures: sitting, wn, lying with the eeling. to activity and rest inal data and then s of 50%. For each rage energy were sholds to find the

we need the input on neural network.

extraction vector ach axis (a total of ithout applying the has a large amount analysis to reduce

periods of rest and get vector of each first feature of the

s of rest we apply e separated periods

We used the features wer part of the leg ng, standing, lying

ng on one side the ounted on the right target vector. The first seven for the

the hip. ctor for periods of 2.

Fig. 2. Principle for computing target v

C. Data Analysis The posture of the subject an

important factors for the outputand the place where they areoutput. The output of thedeterminate by its orientationvector if the subject is in a recomponent of the acceleration different accelerations that are grotational body movements.

Magnitude and duration of taccount in order to be able to cperiods of rest. Also the energyinto account.

In order to compute target veperiods of rest gravitational removed from the signal by apthe resulting data were window50%. For each window, signacalculated after the formula (module of the integrals, normwindow over the three axemounted on the right part of the

1 1 1

1 w w w

i ii i i

SMA x y zw = = =

⎛= + +⎜⎝∑ ∑ ∑

where x, y and z are the acaxis given by the tri-axial accelpart of the hip.

For each window average ethe sum of the squared discretof the signal in a window oveaccelerometer mounted on the r

SMA was compared with a palso compared with a preset thSMA value was bigger than thbigger than threshold 2 than fwas [1 0], representing an activsmaller than threshold 1 and thrthat window was [0 1], represen

As input vector for classifyifeatures SMA and AE were use

For postures we used featuand target vector was calculatefrom horizontal and vertical axi

Features extraction consistwindow of the following featacceleration data, correlation binterquartile range (4) represenmean absolute deviation (5), rdeviation (7) and variance (8).

vector for periods of activity and rest.

nd the activity of the subject are t of the tri-axial accelerometers e mounted also influence the e tri-axial accelerometer is n relative to the gravitational esting posture. The movement signal is composed of several

generated due translational and

the signals must be taken into classify periods of activity and y of the signals must be taken

ector for periods of activity and acceleration component was

pplying the high pass filter and wed in overlapping windows of al magnitude area (SMA) was (1) defined as the sum under malized to the length of the es of tri-axial accelerometer e hip [7]:

iz ⎞⎟⎠ (1)

cceleration components on the lerometer mounted on the right

energy (AE) was calculated as te FFT component magnitudes er the tree axes of the tri-axial right part of the hip. preset threshold 1 and AE was reshold 2. For each window if hreshold 1 and AE value was for that window target vector vity state. If SMA and AE was reshold 2 than target vector for nting a resting state. ing activity state and rest state ed. ures extraction as input vector ed using thresholds to features is of both accelerometers. ts in extraction from each tures: the mean value of the between axes (2) , energy (3), nting the dispersion of the data, oot mean square (6), standard

377

Fig. 3. A block diagram of the proposed algorithm.

cov( , )( , )X Y

X Ycorrelation X Yτ τ

= (2) where X, Y are the variables for x- and y-axes, τ is

standard deviation and cov is covariance between X and Y. 2

1

w

ii

Fenergy

w==∑ (3)

where w is the length of the window, i is the ith FFT component, Fi is the discrete FFT component magnitudes of the signal .

( )INT iqr x= (4) 1

1 w

ii

MAD X nw =

= −∑ (5) where X is acceleration instance and n is the mean value of

the iX .

2

1

1 w

ii

RMS Xw =

= ∑ (6) ( )2

1

1 w

ii

Variance X nw =

= −∑ (7) . varSt Dev = (8) where var is the variance Now the features data set is computed. Because the amount

of data is too large, when the feature data set is processed memory error might occur during processing program. The feature data set is composed of 48 columns of features (3 axes x 2 accelerometers x 8 features). In order to reduce the dimension of the features data set common principal component algorithm was applied. The common principal component is known as a very good method for multivariate statistical analysis and can reduce the dimension of the features data set into a lower dimensional space. The only

problem is the constraint of the principal component analysis that all data groups must be arranged into one group. This constraint was removed by applying a supervised feature subset selection method [8].

Four steps were taken into account to reduce the amount of data for the feature data sets [7]:

1. On each item of features data set was applied principal component analysis to obtain the loading matrix.

2. To represent the kth item of the features data set we need only the first kp principal components, which are determinate based on the cumulative contributions of the principal components calculated by (9)

1

1

( )( ) 100

( )

p

iin

ii

kcumContribution k

k

λ

λ=

=

= ×∑

∑ (9)

where kp is the number of the first k principal components whose cumulative contribution ε≥ and usually ε is within 70-90%. A new loading matrix was obtained where the columns are the first columns from the original loading matrix.

3. Singular value decomposition was applied to the eigenvectors of the matrix obtained by applying common principal component. Results were a diagonal matrix whose elements represent the singular values and a matrix with the corresponding eigenvectors of common principal component loading matrix.

4. The row vectors of the first p columns of common principal component are taken as data points to perform the support vector

Acceleration Data

Highpass Filter

Windowing

SMA

FFT AE

Vector Tag Windowing

Dynamic

Dynamic Feature Subset Extraction

Static Feature Subset Extraction

Dynamic Classifier Static Classifier

Recognition Result

Yes No

378

clustering method using Gaussian kernel transformation to find the number of clusters. The corresponding points closest to the centroids of the clusters were selected and identifying their corresponding features.

In this case the first 14 features (mean value and correlation between axes for all three axes and energy for the horizontal axis) for static postures were found. For dynamic postures only 10 features (mean value for all tree axis and correlation between axes: x and y; y and z) were found.

The target vector was calculated by applying thresholds to the mean value for both accelerometers.

D. Experimental Procedure Three different experiments were conducted in which four

healthy subjects (1 female, 3 males; age 23-27) performed a sequence with specific postures or movements while were wearing the system (one time each sequence). The procedure was the same for all the subjects, each subject carried out eight different postures or movements at the sampling frequency of Fs=10Hz.

First experiment has a sequence of standing (1min), sitting (1min), kneeling (1min), crawling (1min), walking (1min), lying with the face down (1min), lying with the face up (1min) and lying on one side (1min). This protocol took around 9 minutes to complete.

The second experiment has a sequence of standing and moving the hands (3min), sitting at the office and working (4min), kneeling and taking out objects from a box and putting them back in the box (2min), crawling (2min), walking and moving the hands (3min). The protocol took around 15 minutes to complete.

The third experiment has a sequence of sitting and working at the office (1min), walking and moving the hands (1min), kneeling and taking things from a rucksack and putting them back (1min), crawling (1min), standing and moving the hands (1min), lying with the face down (1min), lying with the face up (1min) and lying on one side (1min). The protocol took around 9 minute to complete.

Using pattern recognition neural network we were able to classify all the postures. As we mentioned before pattern recognition neural network classification was made and before to process the data an input vector and a target vector were computed in order to be able to apply pattern recognition neural network. Following a part of the method from previous work [9] we were able to compute target vector with the threshold methods and the input vector was calculated using feature extraction and reducing the amount of features data set with principal component analysis.

III. EXPERIMENTAL RESULTS Four healthy subjects, one female and three males, were

wearing the system, each of them following 3 different sequences. First sequence has all 8 postures, 1 minute each, a total of 32 postures in 36 minutes. Second sequence includes 5 different postures from daily movements for different periods of time. The rest postures have daily movements included, when the subject was sitting he was working at the office, or when he was kneeling he was arranging his stuff in the rucksack. This sequence has 5 different postures, a total of

20 postures in 50 minutes. The third sequence comprise all 8 postures with daily movements for 1 minute each posture, a total of 32 postures in 36 minutes. Activity postures were classified with an accuracy (total number of correct classified data divided to total number of data) of 99.23% and rest postures were classified with an accuracy of 99.26%. Dynamic postures were classified forward and for 7 neurons in the hidden layer, 29 iterations (epoch), 2 seconds of processing data, 30% of data were used by software to the network during training, and the network was adjusted according to this error, 35% of data were used by software to measure network generalization, and to halt training when generalization stops improving, at validation and 35% of data used for testing. The performance of 99,3% for crawling activity and 99.8% for walking activity from dynamic postures were obtained. The accuracy for training, testing, validation and total accuracy are represented by the confusion matrix where number 1 represent crawling activities and 2 walking activities in Fig. 4, Fig. 5, Fig. 6 and Fig. 7. In confusion matrix of training data for dynamic postures column 1, row 1 represent 413 windows (41.8% of dynamic data) correct and column 2, row 1 is 1 window (0.1% of dynamic data) incorrect used for training crawling data and column 1, row 2 are 0 windows incorrect and column 2, row 2 are 575 windows (58.1% of dynamic data) correct used for training walking data.

Static postures were classified using the classification with pattern recognition neural networks with 5 neurons in the hidden layer, 200 iterations (epoch) in 32 seconds. Like in classification for dynamic postures 30% of data was used for training, 35% of data was used for testing and for validation was used 35%. The confusion matrix of the static classification is shown in Fig. 8. In this matrix the accuracy shown in the last line at number 1 corresponding to the kneeling postures, number 2 corresponding to the lying with the face down postures, number 3 represents the accuracy for

Fig. 4. Confusion matrix for training data for dynamic postures.

Fig. 5. Confusion matrix for testing data for dynamic postures.

379

Fig. 6. The confusion matrix for validation data for dynamic postures.

Fig. 7. The confusion matrix for all the data for dynamic postures.

Fig. 8. The confusion matrix for all the data of static postures. lying with the face up postures, number 4 represent the accuracy for lying on one side postures, number 5 represent the accuracy for sitting postures and number 6 represent the accuracy for standing postures.

The accuracy for data correct classified is represented with the green color in the confusion matrix and with the red color data incorrect classified. From confusion matrix a total accuracy of 99.8% was obtained.

The percentage of the accuracy in the confusion matrix represent the total accuracy for each posture from data classified as rest postures and dynamic postures, so the final accuracy of each posture is: kneeling 99.16%, lying with the face down 98.96%, lying with the face up 98.96%, lying on one side 99.26%, sitting 98.46%, standing 99.26%, crawling 98.53% and walking 99.03%.

IV. CONCLUSION In this work the problem of accurately classification of

postures based on measurements from two tri-axial accelerometers were explored. The positions at which the accelerometers are mounted on the human body are important in measurements of body movements. With only one tri-axial accelerometer good accuracy was obtained for all the posture except the kneeling and sitting ones which were confused with standing postures. Because of this problem another tri-axial accelerometer was placed on the lower part of the right leg.

For periods of rest and activity Mathie et.al [10] conducted a study, investigating three parameters: length n of a smoothing median filter, width w of the average moving window and the value of the SMA. She obtained best results for control group of 100% and for test group 96% while we obtained for training data 99.65%, and testing data 99.65%

Data collected with both accelerometers were classified with pattern recognition neural network classifier giving a better accuracy than previous work [9] of a total average of 98.95% for eight postures, from a total data collected in 122 minutes, from four subjects (1 female and 3 male, age 23-27), a total of 84 postures in three different sequences.

The processing time for classifying posture with pattern recognition neural network took 2 seconds for periods of activity and 32 seconds for periods of rest, a total of 34 seconds which is very fast considering the amount of data of features extraction used by classifier.

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