AN EXPLOITATION OF THE SELF-ORGANIZING MAP FOR

HUMAN MOTION ANALYSIS

W. Kurdthongmee

Division of Computer Engineering, School of Engineering and Resources, Walailak University

222 Thaibury, Tha-sa-la, Nakorn-si-thammarat 80160, Thailand

P. Kurdthongmee

Center for Scientific and Technological Equipment, Walailak University

222 Thaibury, Tha-sa-la, Nakorn-si-thammarat 80160, Thailand

Keywords: Kohonen Self-organizing map, Motion analysis, Gyroscope, Accelerometer.

Abstract: Falls are the most common type of home accidents among elderly people and are a major threat to their

health and independence. Evaluating the risk of falling is important because it enables the provision of

adapted assistance and of taking preventive measures with subjects deemed at risk of falling. Currently, the

risk of falling has been evaluated by using questionnaires with their associated problems of subjectivity and

limited accuracy in recall. The Kohonen Self-Organizing Map (SOM) has found applicability in a wide

range of application areas. Our research as a whole has a final objective to

employ the concept SOM to

implement an adaptive fall risk detection and warning system. In this paper, we present the preliminary

results from our research to utilize SOM to analyze the motion parameters from a miniature sensor with

integrated gyroscopes and accelerometers attached to the chest of an individual. The results clearly indicate

that SOM can be successfully used to cluster the activities by means of their motion parameters. This is very

promising results to extend the concept to implement our final objective system.

1 INTRODUCTION

Falls are the most common type of home accidents

among elderly people and are a major threat to their

health and independence (Najafi, 2002). Thirty-two

percent of a sample of community dwelling persons

75 years and older fell at least once a year. Among

them, 24% sustained serious injuries (Tinetti, 1988)

In addition, falling can dramatically change an

elderly people’s self-confidence and motivation,

affecting their ability to function independently.

Considering the growing proportion of old people

(over 75) in the populations of industrial countries,

falls will be one of the major problems of this

important part of the population (Askham, 1990) In

2050, 16.4% of the world population and 27.6% of

the European population are projected to be 65 years

and above, and in 14 countries, including nine

European countries, more than 10% of the total

population will be 80 years or older. Most cases of

falls sustained by elderly people appear to result

from the cumulative effect of multiple specific

disabilities. Among these, balance and gait disorders

play a key role (Tinetti, 1986).

Our research has the following major goals: (1)

to investigate the changes of an individual’s motion

parameters over a period of time, (2) to

experimentally proof that SOM can be used to learn

an individual’s motion parameters and make the

decision for an unsafe motion that could be a fall

risk, and (3) to present the prototype of a SOM-

based adaptive system for monitoring and warning

of the fall risk motions. In this paper, we present the

results that fulfil part of the second objective. We

have a strong postulation that different persons have

different styles of motion, different activities to

perform in the daily life. In addition, it is very likely

that the styles of motion and activities are likely to

change over time. This calls for an automatic system

that is capable of learning along with the wearer and

warning if the motion parameters are out of normal

conditions previously and continuously learnt by the

system.

151

Kurdthongmee W. and Kurdthongmee P. (2009).

AN EXPLOITATION OF THE SELF-ORGANIZING MAP FOR HUMAN MOTION ANALYSIS.

In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 151-156

DOI: 10.5220/0001225901510156

 SciTePress

Evaluating the risk of falling is important

because it enables the provision of adapted

assistance and of taking preventive measures with

subjects deemed at risk of falling. The risk of falling

has generally been evaluated by using questionnaires

with their associated problems of subjectivity and

limited accuracy in recall (Cummings, 1988). Risk

of falls can also be evaluated by clinical and

functional assessment including posture and gait,

independence in daily life, cognition, and vision

(Tinetti, 1986). However, no simple objective

method is available.

A method of evaluating the characteristics of

postural transition (PT) and their correlation with

falling risk in elderly people is described in (Tinetti,

1988). With respect to the report, the time of sit-to-

stand and stand-to-sit transitions and their duration

were measured using a miniature gyroscope attached

to the chest and a portable recorder placed on the

waist. The comparison between two groups of

elderly subjects (with high and low fall-risk) showed

that the computed parameters were significantly

correlated with the fall risk as determined by the

record of falls during the previous year, balance and

gait disorders, visual disorders, and cognitive and

depressive disorders. From our point of view, the

drawbacks of the proposed system are three folds.

Firstly, the differences in the collected data among

different persons, or even within the same person,

but different time are not taken into account.

Secondly, the history of falls in the part was used as

an input parameter for the system. This could be an

incomplete data due to the limitation of memory of

the studied group. Finally, the proposed monitoring

and warning system is in a class of a pre-

programmed system. This is in contrast to our

proposed final system that will rely on using SOM to

make it adaptable. The exploitations of gyroscopes

and accelerometers for monitoring and warning

applications were also described in several patents

(Patent, 2008). They, however, lacked the

adaptability features.

Javanov et al. (Javanov, 2005) present a

prototype wireless body area network (WBAN)

system with unobtrusive, commercially available

sensor platforms that have minimum size and

weight. The prototype relies on using their proposed

algorithms to monitor wearer’s activity with a

minimal number of accelerometers to reduce the

price of future systems. The obvious difference

between this prototype and our proposed one is that

the prototype, itself, does not perform any adaptive

feature on board at all. Its main function is only to

transfer the accelerometer and electrocardiogram

(ECG) data to be analyzed by the remote server.

In (Hwang, 2004), a novel algorithm and real-

time ambulatory monitoring system for fall detection

in elderly people were described. The system

comprised of accelerometer to measure kinetic force,

tilt sensor and gyroscope to estimate body posture.

The BlueTooth® module was integrated to the

system to send real-time data to a personal computer

for data analysis and warning. The system was

evaluated by attaching to the chest for fall detection

on three people aged over 26 years. The experiment

of four cases; forward fall, backward fall, side fall

and sit-stand, was repeated ten times and the

experiment in daily life activity was performed one

time to each subject. The results showed that the

system and the proposed algorithm could distinguish

between falling and daily life activity. Moreover, the

accuracy of fall detection is 96.7%. From our

opinion, the system required adjustment to be suited

for different persons since it lacks the feature to

automatically adapt the decision making rules.

The objective of this paper is to present the

experimental results of applying SOM to learn an

individual’s motion parameters and correctly cluster

the motion parameters by means of the activities.

The remaining of this paper is organized as follows.

Our method is presented in Section 2, and

experiments and results are detailed and analyzed in

Section 3. Section 4 draws conclusions and

introduces future work.

2 METHODS

2.1 Data Gathering System

As mentioned earlier that individuals are likely to

have different styles of motion and different daily

activities to perform. In addition, it is very likely that

the styles of motion and activities change over time

and age. In our research, we developed an

experiment to confirm this postulation. A simple

low-cost data logger was developed in-house to

fulfil this purpose. The prototype of the system,

illustrated in Figure 1, consists of a microcontroller

(dsPIC30F2010), a couple of EEPROMs with total

capacity of 256 KB (2 of AT24C1024) and a special

purpose chip to serve as a voltage level converter

between TTL and RS232 standards. The RS232 is

used as a communication channel between a

personal computer and the data logger. The Analog

Devices Inc’s ADIS16350 tri-axis inertial sensor

(triple axes gyroscopes plus triple axes

accelerometers) sensor is used to measure both

angular and linear accelerations.

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A Windows based application software on a

personal site was specially developed to control the

operations of the data logger; i.e. time stamp setting,

sample time setting and data capturing for analysis.

Figure 2 shows the screenshot of the results from the

data logger during the period of 12:43:30 –

12:49:00. The first three columns are the time stamp

of the recorded data which are arranged in the

following format: the angular accelerations in the x,

y and z axes and the linear accelerations in the x, y,

and z axes. It is noted that the data logger was

programmed to sample data every 1 second period

but it is capable of immediate recording the new

incoming data if and only if all parameters change

more than the predefined acceptable delta which are

6 units in this case. Also, it is noted that in order to

get the true value of both angular and linear

accelerations the recorded values must be multiplied

by 0.07326

and 2.522 mg, respectively.

Figure 1: A photograph of the data logger system (right)

and a miniature gyroscopes and accelerometers (left).

Figure 2: The screenshot of the results obtained from the

data logger during the period of 12:43:30 – 12:49:00.

Figure 3 show two snapshots of the experimental

results from recording the motion, time and activity

of a male volunteer of 39 years old. The legends of

the activities are as follows: (0) Walking, (1) Sitting,

(2) Walking Downstairs, (3) Walking Upstairs, (6)

Jogging and (7) Sleeping. It is noted that during the

period of our experimentations, these activity types

were approximately inserted offline after the data

had already been transferred to a personal computer.

This was done in order not to interrupt and disturb

the normal activities of our volunteers. It can be

observed that there are some patterns in the graphs

which have some relations with the activities. These

are very promising input data for training SOM.

Raw Data from Accelerometers at Different Times & Activities

-300

-200

-100

100

200

300

400

500

3:43 PM 3:50 PM 3:57 PM 4:04 PM 4:12 PM 4:19 PM

Time Stamp

Accel X

Accel Y

Accel Z

Activity

Raw Data from Gyroscopes at Diffe rent Times & Activities

-1500

-1000

-500

500

1000

1500

3:43 PM 3:50 PM 3:57 PM 4:04 PM 4:12 PM 4:19 PM

Time Stamp

Gy ro X

Gy ro Y

Gy ro Z

Activity

Figure 3: The snapshots of the experimental results from

recording motion, time and activity of a volunteer from the

accelerometers (top) and the gyroscopes (bottom).

2.2 A Brief Introduction to SOM

A 2-D map is defined by k locations or k cells

arranged as a 2-D lattice. Each location contains an

n-dimensional model vector which comes to

resemble n-dimensional input (teaching) data during

the unsupervised learning process, the self

organization (Joutsiniemi, 1995). As a result of

SOM process, the distribution of the model vectors

in the n-dimensional space will approximate the

probability distribution of the input vectors. The

topographic organization of the map will also

approximate the matrix ordering relations in the

input space. Thus, similar inputs project near each

other onto the map. Increasing the number of

locations k increases the accuracy of the

approximation, which means that k should be chosen

according to the computing power available. In our

experiment, we chose k = 500. (a hexagonal lattice

with dimensions 25 and 20).

In our experimentation, the maps were

initialized, taught, and evaluated using the routines

in the SOMPAK package. The learning consisted of

two phases. In the first phase, the learning

coefficient

(t) decreased from 1.0 to 0 in 100,000

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153

steps, while the radius of the neighbourhood

decreased from 15 to 1. In the second phase,

(t)

decreased from 0.125 to 0 in 10,000 steps, while the

radius of the neighborhood decreased from 3 to 1.

The data retrieved from the sensors were modified to

have zero mean and one standard deviation

(Zachrison, 2006). This was down in order to

restrain one of the input dimensions from becoming

too dominant. The time stamps were not used

directly for self organization process but they were

instead transformed into the “period of change” of a

consecutive pair of motion parameters. By doing this

way, it made the resulting map to be in a more

readable form with highly cluster groups of data.

3 RESULTS AND DISCUSSIONS

The resulting map after training process with an

individual’s input data of 7 dimensions, which are

the period of change, the angular accelerations

and the linear accelerations in the x, y, and z

axes, is shown in Figure 4(a) (lines separating

different clusters and labels within the clusters were

manually inserted). In total, the data used during the

learning stage consisted of 1,044 records. In order to

get as general results as possible with the limited

data, a leave-one-out procedure was used. This was

done by taken out 10 percents of the records of the

motion parameters for each activity from the training

dataset and analyzed using a map trained with the

parameters of the remaining records. The activities

of the former set of data records were known to us

but they were invisible with respect to SOM. It can

be seen that the self-organization process produced

the final map with different distinguishable clusters.

In Figure 5(a), the clusters are grouped and the

corresponding activities are labeled manually with

the guidance of the data resulting from the labeling

process of the SOMPAK program.

It is noted that with respect to the results after the

SOMPAK labeling process, some cells in the map

were labeled by many activities. Also, some cells

were completely left unlabelled. For the former case,

this means that some activities share common

motion parameters. These activities are “walking

upstairs” and “downstairs.” For the latter case, such

cells could be matched to a group of motion

parameters which could be a fall risk. The

investigation of the properties of these cells is

beyond the scope of this paper.

At this point, consider the leave-one-out testing

results which was performed by querying the trained

SOM with an unknown motion parameters with

respect to the SOM (the one which was taken out

from the training dataset), the results are presented

in the middle column of Table 1 (within group). It is

clear that, with respect to the proposed set of motion

parameters for SOM training, all activities could be

correctly classified with the mean and the standard

deviation of 73.45 percents and 16.08, respectively.

It is noted that the hundred percents could not be

achieved because some activities were matched to a

group of cells of the SOM map which were

previously labeled by many activities.

Table 1: The results from the leave-one-out testing

procedure: within group and cross validation.

Activities

Percent Correctness

Within

Group

Cross

Validation

Walking (W)

61.54 56.24

Sitting (S)

86.67 62.00

Walking Downstairs (D)

64.29 42.85

Walking Upstairs (U)

61.54 50.00

Jogging (J)

50.00 42.71

Sleeping (SL) 100.00 87.00

Table 2: The probability of being matched of all activities

after presenting to SOM with the known activity motion

parameters (Use the abbreviations from Table 1).

Act

The Probabilities of Matched to Activities

W S D U J SL

0.62 0.07 0.07 0.08 0.10 0.00

0.15 0.87 0.29 0.00 0.20 0.00

0.18 0.05 0.64 0.08 0.15 0.00

0.00 0.00 0.00 0.62 0.05 0.00

0.03 0.00 0.00 0.15 0.50 0.00

SL 0.03 0.02 0.00 0.08 0.00

1.00

The rightmost column of Table 1 presents the testing

results after performing the similar testing

procedures with the dataset of the motion parameters

from different volunteer: the cross-validating test. It

was expected prior to performing the test that the

results could not be as good as the previous testing.

The outcomes show that the mean of correctness is

only 58.97 percents with a standard deviation of

15.19. This confirms that different persons have

their own set of motion parameters. To correctly

classify the activities of an individual, SOM is

required to be trained with the motion parameters of

the individual.

There are some interesting results if we

reconsider the probability of being matched to all

activities after presenting the unknown motion

parameters to SOM. The results are shown in Tab. 2.

Column 2 – 7 are the types of activity whose motion

parameters were presented to SOM for querying and

all rows are the resulting probabilities that SOM

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Figure 4: (a) The resulting map after clustering and labelling, the projection maps in (b) – (h) show the relationships

between the activities and the training parameters: period of change, angular and linear accelerations in the x, y, and z axes.

matched those parameters to the activities. It can be

seen that (“walking”, “walking downstairs”),

(“sitting”, “walking downstairs”) and (“jogging”,

“walking downstairs”, “sitting”) share some

common motion parameters. It is quite clear for the

first group but question could be arisen for the

second and third one. This can be explained that the

volunteer could be moving while sitting.

After all activities were clustered, it is now

possible to analyze the relationships between

different training parameters and the activities.

Figure 5(b) – (h) show the projection maps with

respect to individual motion parameters. Consider

the map of the x and y axes of gyroscope, Figure

5(c) and (d), both maps show almost the similar

clusters and the scales of the motion parameters

which are reflected from their colour appearances

(see the legend on the right side of the maps.) It is

surprised to see that only the activity of type

“walking” can make a prominent high value to the

sensors in these axes. This could come from the fact

that our volunteer’s walking style causes a more

forward movement comparing to “jogging” and

“walking upstairs and downstairs”. Also, the

projection map of the z-axis gyroscope in Figure

5(e) which clearly shows different clusters of

activities, comparing to the previous 2 maps,

indicates that “walking” and “jogging” cause this

sensor to output a very high level.

The maps with respect to the accelerometers

shown in Figure 5(f) – (h) obviously indicate that

SOM is capable of clustering “sleeping” which

differs from the rest activities in the way that the

motion parameters in all axes are completely

changed. Also, the maps in Figure 5(f) which

projects onto the x axis accelerometer clearly show

the clusters of “walking.” There are some variations

in the motion parameters of “sitting” with respect to

the accelerometers which could be interpreted that

our volunteer could be moving while sitting. It is

noted that these variations could not be correctly

recorded during the period of our experimentations.

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Lastly, consider the last projection map, the period

of change, in Figure 5(b). The map reveals some

interesting points in the “jogging” cluster on the

bottom left part, also the “sitting”, the “walking

upstairs” and the “sleeping” clusters. These points

can be interpreted that there were suddenly changed

of the motion parameters during performing such

activities. It is easy to understand these cases for

“jogging” and “walking upstairs” which always

cause the sudden changes of these motion

parameters. For “sleeping” and “sitting”, such the

changes could be resulted from the immediate

change of motion patterns; i.e. move backward and

change to forward immediately during sitting or

quickly lie before sleeping.

4 CONCLUSIONS

In this paper, we presented the results that partly

fulfilled an objective of our overall research project

which is intended to develop an adaptive system to

detect motion parameters that are fall-risk. We

experimentally proved that SOM could be trained

with an individual’s motion parameters: the period

of change of a consecutive pair of parameters, the

angular and linear accelerations in (x, y, z), resulting

in clustering of similar motion parameters. Also,

SOM could match between normal activities and the

clusters of motion parameters on the maps with as

high as 73.45 percents of correctness. However, the

matching between abnormal motion parameters that

could be a fall risk still needs more efforts to pursue.

From the experiment results, it can be concluded that

different activities of an individual have different

motion parameters (period of change is also

included). SOM can successfully and correctly

cluster these activities in relation to the motion

parameters. It is worth noting that in order to

classify the activities of a person with a high degree

of correctness, SOM needs to be trained with the

motion parameters of that person. With positive

experimental results, we expect that SOM can be

utilized to make the decision for an unsafe motion

that could be a fall risk in an adaptive way.

ACKNOWLEDGEMENTS

The author would like to thank Analog Devices Inc

for providing ADIS16350 sample. This work is

supported by the Assistive Technology Program of

Thailand’s National Electronics and Computer

Technology Centre (NECTEC).

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