Indoor RFID Gait Monitoring System

for Fall Detection Yung-Chin Chen*, Member, IEEE, Vi-Wen Lin

Asia University

ycchen@asia.edu.tw, gamepeare@hotmail.com

Abstract�Due to home accidents occurring within the

world's aging population, especially with the accidental stumble

and falling of the elderly, mortality and morbidity from falls

have become one of the major issues in the health care system.

In most developed countries, most frequent stumbles and falls

occur in and around the community or at home. A number of

wearable devices integrated with accelerometers have been

tested to detect accidental stumbles and falls. Of those tested

using waist or chest-attached devices, however, were not only

user unfriendly but also deficient in 100% detection accuracy. In

this paper, a RFID-based gait monitoring system (RGMS) is

proposed, which helps caregivers detect stumbles and falls of

the elderly without any restriction to time and place. The

proposed RGMS consists of a pair of slippers with a dual band

RFID module, several readers and a computing system. The

RGMS provides quantitative and graphical feedback to

caregivers for gait monitoring and the assessment of gait

abnormality by tracking the elder's cadence, stride length, and

gait. This gait tracking system is responsive, accurate, and most

of all user friendly.

Index Terms�RFID, aging, falls, stumbles, gait.

1. INTRODUCTION

Home accidents occurring within the world's aging

population, especially with the accidental stumble

and fall, have become one of the major causes of

mortality and morbidity in the elderly[I]. Consequently,

leading to an ever increasing care burden with the burden

of disease [2,3]. According to the data provided by

Department of Health, accidental stumbles and falls

death in Taiwan over the past few years [4]. Injuries due

to falls have been ranked second highest of the leading

reasons. In the United States alone, the percentage of fatal

home accidents from stumbles and falls have also been on

the rise. More than 86% of the victims are 65 years old or

older. An estimated 30% of persons who are 65 years or

older have been affected by a serious fall. In addition,

elderly persons residing in nursing home facilities,

hospitals, and elderly care centers have a higher

probability of stumbles and falls than that at home [5].

According to a study released by the Centers for

Disease Control and Prevention's Morbidity and

Mortality Weekly Report (MMWR), fall-related death

rates among Americans ages 65 and older increased

greatly from 1993 to 2003. More than 13,700 seniors died

from falls in 2003, making falls the leading cause of

injury-related deaths among elderly persons. Fall-related

fatalities increased by 55% from 1993 to 2003. In 2003

nearly 1.8 million elderly persons received treatment in

emergency rooms for fall-related injuries and 460,000

were hospitalized. The costs for fall-related care among

seniors in 2000 totaled approximately $19 billion.

"Fall-related death rates have increased faster than that of

fall-related injury rates" [6]. On the other hand, the

leading causes for increasing elderly death rates may no

longer be dominated by chronic diseases, such as heart

disease, cancer, and diabetes, but by fatal falls as well.

According to researchers in the United States, elderly

people are obviously prone to stumbles and falls due to

the progressive deterioration in their balancing

leading to injury occurring with people ages from 65 reflexes[7]. Besides, behaviors of trying to get up and

years and above are ranked 7th as the leading causes of walking on a slippery surface such as wet bathroom floor

978-1-4244-8314-3/10/$26.00 ©20l0 IEEE 207

are also among the main causes of elderly falls.

According to one survey, one in three elderly people

living within a community are at risk of likely falling one

or more times in a year; with one in four falling ending up

with serious or fatal injuries. Moreover, stumble and falls

constitutes a significant portion of health care cost [8,9].

On the other hand, aging population and public health

care services are the driving factors for moving toward

the adoption of a home tele-care system.

During the last decade, technological advances in

microcontrollers, wireless communication, and sensor

miniaturization have provided for the development of

wearable devices for home-based healthcare monitoring

systems. A number of wearable sensor devices integrated

with a two-axis accelerometer [10-11] or a three-axis

accelerometer [12-25] have been widely developed to

detect stumble and fall activities by measuring the

changes in instantaneous acceleration values and attitude

angles. Sensor devices already in the market which were

designed either to be attached to their wrists or waist were

not only user unfriendly but also lacked 100% detection

accuracy.

We therefore propose a novel RFIO-based gait

monitoring system for stumble and fall detection without

any restriction to time and place. The ROMS consists of a

pair of slippers with dual-band RFID module built inside,

several readers, and a computing system that provides

quantitative and graphical feedback for gait monitoring

and the assessment of gait abnormality. This system is

designed to adjust stumble and fall activities by tracking

the elder's gait and stride length.

2. RFID GAIT MONITORING SYSTEM

As far as the RFID-based positioning or tracking

technology is concerned, there have been several

investigations involved in the localization and tracking

applications for various purposes [26-33], while paying

insignificant amount of observation to track the elderly

person's gait and stride length for fall detection have been

made. The possible reasons for lack of attention to this

208

area of research are as follows; (l) The accuracy of such

proposed RFIO-based indoor positioning systems have

been insufficient to measure and mark the stride length.

(2) Indoor RFID positioning accuracy highly depends on

the signal strength, which is easily dissipated by way of

mUlti-path fading. (3) And, the relatively high number of

active readers which are required leading to high capital

costs.

Therefore, we propose a ROMS system that consists

of several 2.450Hz active readers, a pair of slippers

attached with dual-band RFID module incorporated

internally and a computing system. Thus, the degree of

gait abnormality is quantified based on the measured

stride length.

The 2.450Hz active reader is shown in Figure 1. The

reading range is up to five meters and its specification is

listed in Table 1.

Fig 1. 2.450Hz active reader

Table 1 Specification of Active Reader

Feature Description

Frequency 2.4�2.45 OHz

Size 92(W)X66(H)X20(D) (mm)

Interface USB Vl.1

Antenna Built-in 1 dBi omnidirectional antenna

The dual-band RFID module combines a 2.450Hz

active tag and a 13.56MHz passive reader (ISO 15693),

see Figure 2. Power supply for the 13.56MHz passive

reader is an ER2450 3.6V battery. A motion sensor is also

built-in for power saving. The dual-band RFID module is

designed to be attached inside the slipper as shown in

Figure 3.

I I

I I I I I \ \ \

'- '" ..... _- ,

U.56 MHz ..... e<

I I

I Battet')'

3.6V

Fig 2. RFID dual-band module

The function of the dual-band RFID module is to read

the low-cost 13.56MHz passive tags mounted on the floor

for measuring stride length while transmitting the signal

to the 2.45GHz active reader through the 2.45GHz active

tag. The system will then be able to calculate the possible

stumble and fall activity as a result of gait abnormality.

The architecture of the indoor RFID gait monitoring

system is shown in Figure 4.

Fig 3. A slipper attached with a fitted dual-band module

RFlO 2.45G Tag & 13.56 Reader

•

Active Moving path

Fig 4. Indoor RFID gait monitoring system

For precise stride length measurement, many

13.56MHz passive tags are to be mounted onto the floor

using a triangular array separated by 20cm, as illustrated

in Figure 5. By taking advantage of the unique ID-(x. y

• z) axes correspondence, we can not only track elder

person's position but also measure their stride length.

The RGMS allows elders to take advantage of the gait

209

monitoring slippers in their daily lives in an unconscious

and responsive manner. A decision algorithm for real-time

stride length monitoring embedded in the RGMS is

illustrated in Figure 6. It is able to measure various stride

length and just gait useful for stumble and fall detection.

The algorithm developed is based on normal stride length

of >50cm and abnormal stride length of <25cm for

elderly people. The system will be alerted once the stride

length falls below 25cm. Stride lengths ranging from

25cm to 50cm will then be calculated by averaging ten

read times to find these values. If the average value is less

than 35cm, system will alert and inform care givers

through the visual feedback information displayed on the

RGMS.

Fig 5. Triangular array

Fig 6. An algorithm for real-time stride length monitoring

3. EXPERIMENTAL RESULTS

The performance of RGMS have been tested within a

living room with a triangular tag array separated by 20cm

while mounted on the floor (see Figure 7). Read rate of

the mounted tags were tested first by wearing slippers

while walking around the living room. Test resulted in a

relatively high read rate as listed in Table 2.

Fig 7. Triangular tag array mounted on the floor

Table 2. Read rate of the mounted triangular tags

time read successful read rate

interval No times read times (%)

(min:sec)

1 100 86 86% 4:09 2 100 95 95% 4:05 3 100 100 100% 2:33 4 100 91 91% 3:09 5 100 98 98% 2:50

As we can see from the main interface of the RFID gait

monitoring system (see Figure 8), it shows a layout for

the living room, a list table for those mounted tags, some

control buttons, a list table for measured stride length, and

two ID numbers used in the identification of both left: and

right foots.

Figure 9 demonstrates the quantitative and graphical

feedback for gait monitoring for both left: and right foot.

If the stride length of the mounted tags read by the slipper

is less than 25cm, the gait monitoring system will be

alerted and caregivers will be informed to give aid, see

Figure 10.

210

Table 3 shows that this monitoring system can also

provide physical activity information by collecting their

overall daily stride length. This table is helpful for in

physical activity health care monitoring.

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Fig 10. Abnormal Gait Warning Interface

Table 3. Physical activity

stride length Average velocity time interval (cm) (cmls) (5:1' : fj))

5823 34.85 2:47 6234 33.69 3:05 5689 37.18 2:33

4. CONCLUSION

Since there are very few resesarch focusing on RFID

gait monitoring system for indoor tracking to detect

stumbles and falls, aRFID-based gait monitoring system

(RGMS) consisting of a dual-band RFID module built

into a pair of slippers, several readers, and a computing

system is proposed. The RGMS provides quantitative and

graphical feedback to caregivers for gait monitoring and

the assessment of gait abnormality by tracking the

elderly's stride length and gait. The RGMS is not only

helpful for caregivers to detect stumbles and falls of the

elderly, but also to possibly predict stumbles and falls in a

responsive, accurate, and most of all user friendly

manner.

ACKNOWLEDGMENT

This work was supported by National Science

Council, Republic of China, under Grant NSC98-2221

-E-468-0 14.

[1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

REFERENCES

Downton JH. (1993) Falls in the elderly. Kent, UK: Edward

Arnold, a division of Hodder & Stoughton Limited.

Murray CJL and Lopez AD (1996a) The global burden of

disease: a comprehensive assessment of mortality and disability

from diseases, injuries, and risk factors in 1990 and projected to

2020. Cambridge, USA: Harvard University Press, on behalf of

the World Health Organization and the World Bank.

Murray CJL and Lopez AD. (1996b) Global health statistics: a

compendium of incidence, prevalence, and mortality estimate for

over 200 conditions. Cambridge, USA: Harvard University

Press, on behalf of the World Health Organization and the World

Bank.

Department of Health, http://www.doh.gov.tw/ chtlindex.aspx.

Kenneth D. Kochanek, M.A., Sherry L. Murphy, B.S, "National

Vital Statistics Report", Volume 53, Number 5 (October 2004»)

CDC Morbidity and Mortality Weekly Report; 55(45):1221-1224

(2006).

Andy Coghlan, "Why do elderly people fall over so often?",

New Scientist Magazine, issue 1715, 1990.

M. E. Tinetti, M. Speechley, and S. F. Ginter, "Risk factors for

falls among elderly persons living in the community," N Engl J

Med., vol. 319, pp. 1701-7, 1988.

(9) S. R. Lord, e. Sherrington, and H. B. Menz, Falls in older

people:Risk factors and strategies for prevention. Cambridge:

Cambridge,University Press, 200l.

(10) Chen, J.; Karric Kwong; Chang, D.; Luk, 1.; Bajcsy,

R.;"Wearable Sensors for Reliable Fall Detection", Engineering

in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th

Annual International Conference of the, pp.3551 - 3554, Jan.

2006.

[II) Youngbum Lee; Jinkwon Kim;Muntak Son; Myoungho Lee,

"Implementation of Accelerometer Sensor Module and Fall

211

Detection Monitoring System based on Wireless Sensor

Network", Engineering in Medicine and Biology Society, 2007.

EMBS 2007. 29th Annual International Conference of the IEEE,

pp. 2315 - 2318 ,Aug. 2007.

[I 2) Karantonis, D.M.; Narayanan, M.R.; Mathie, M.; Lovell, N.H.;

Celler, B.G;"lmplementation of a real-time human movement

classifier using a triaxial accelerometer for ambulatory

monitoring", Information Technology in Biomedicine, IEEE

Transactions on, Volume 10, Issue 1, pp.156 - 167,2006.

(13) Tamura, T.;"Wearable accelerometer III clinical use",

Engineering in Medicine and Biology Society, 2005.

IEEE-EMBS 2005. 27th Annual International Conference of the,

pp.7165 - 7166, Jan. 2006.

(14) Ali-young Jeon; Soo-young Ye; Jun-mo Park; Kwang-nyeon

Kim; Jae-hyung Kim; Dong-keun Jung; Gye-rok Jeon;

Jung-hoon Ro;"Emergency Detection System Using PDA Based

on Self-Response Algorithm", Convergence Information

Technology, 2007. International Conference on, pp. 1207 - 1212,

2007.

[15) Purwar, A; Do Do Jeong; Wan Young Chung;"Activity

monitoring from real-time triaxial accelerometer data using

sensor network", Control, Automation and Systems, 2007.

ICCAS '07. International Conference on, pp. 2402-2406, 2007.

[16) Do- Un Jeong; Se-Jin Kim; Wan-Young Chung;"Classification of

Posture and Movement Using a 3-axis Accelerometer",

Convergence Information Technology, 2007. International

Conference on, pp. 837 - 844, Nov. 2007.

[17) Jeon, AY; Kim, J.H.; Kim, I.e.; Jung, 1.H.; Ye, S.Y; Ro, 1.H.;

Yoon, S.H.; Son, 1.M.; Kim, B.e.; Shin, B.1.; Jeon,

GR.;"Implementation of the Personal Emergency Response

System using a 3-axial Accelerometer", Information Technology

Applications in Biomedicine, 2007. ITAB 2007. 6th International

Special Topic Conference on, pp.223 - 226, Nov. 2007.

[18) Kangas, M.; Konttila, A; Winblad, I.; Jamsa, T.;"Determination

of simple thresholds for accelerometry-based parameters for fall

detection", Engineering in Medicine and Biology Society, 2007.

EMBS 2007. 29th Annual International Conference of the IEEE,

pp.1367 - 1370, Aug. 2007.

[19) Narayanan, M.R.; Lord, S.R.; Budge, M.M.; Celler, B.G; Lovell,

N.H.;"Falls Management: Detection and Prevention, using a

Waist-mounted Triaxial Accelerometer", Engineering in

Medicine and Biology Society, 2007. EMBS 2007. 29th Annual

International Conference of the IEEE, pp.4037-4040, Aug. 2007.

(20) Van Wieringen, Matt; Eklund, 1. Mikael;, "Real-time signal

processing of accelero-meter data for wearable medical patient

monitoring devices", Engineering in Medicine and Biology

Society, 2008. EMBS 2008. 30th Annual International

Conference of the IEEE, pp.2397 - 2400, Aug. 2008.

[21) Anania, 0.; Tognetti, A; Carbonaro, N.; Tesconi, M.; Cutolo, F.;

Zupone, 0.; De Rossi, D.;"Development of a novel algorithm for

human fall detection using wearable sensors", Sensors, 2008, pp.

1336 - 1339, Oct. 2008.

[22) Barth, AT.; Qiang Li; Gang Zhou; Hanson, M.A.; Lach, J.;

Stankovic, 1.A; "Accurate, Fast Fall Detection Using

Gyroscopes and Accelerometer- Derived Posture Information",

Wearable and Implantable Body Sensor Networks, 2009. BSN

2009. Sixth International Workshop on, pp.138 -143, June 2009.

[23) Bianchi, Federico; Redmond, Stephen 1.; Narayanan, Michael

R.; Cerutti, Sergio; Celler, Branko 0.; Lovell, Nigel H.;"Falls

event detection using triaxial accelerometry and barometric

pressure measurement", Engineering in Medicine and Biology

Society, 2009. EMBC 2009. Annual International Conference of

the IEEE, pp. 6111 - 6114, Sept. 2009.

[24) Sposaro, F.; Tyson, G;"iFall: An android application for fall

monitoring and response", Engineering in Medicine and Biology

Society, 2009. EMBC 2009. Annual International Conference of

the IEEE, pp. 6119 - 6122, Sept. 2009.

[25] Jiewen Zheng; Guang Zhang; Taihu Wu;"Design of Automatic

Fall Detector for Elderly Based on Tri-axial Accelerometer",

Bioinformatics and Biomedical Engineering , 2009. ICBBE

2009. 3rd International Conference on, pp.I-4, June 2009.

[26] Sung-Tsun Shin ,Kunta Hsieh, Pei-Yuan Chen: An

Improvement Approach of Indoor Location Sensing Using

Active RFiD 2006; I 0: pp.453-4560

[27] N.Krahnstoever, J.Rittscher, etc., " Activity Recognition using

Visual Tracking and RFlD", Proceedings of the Seventh IEEE

Workshop on Applications of Computer Vision (WACV/

MOTION'05),2005.

[28] Loc Ho, Melody Moh & Zachary Walker, Takeo Hamada &

Ching-Fong Su, A Propotype on RFiD and Sensor Networks

for Elder Healthcare:Progress Report, 20050

[29] Scooter Willis and Sumi Helal, "RFID Information Grid and

Wearable Computing Solution to the Problem of Wayfinding for

212

the Blind User in a Campus Environment", pp.I-8, Florida

University Technical Report, 2005.

[30] Yao-Jen Chang, Chien-Nien Chen, Li-Der Chou, Tsen-Yung

Wang: "A Novel Indoor Wayfinding System Based on Passive

RFID for Individuals with Cognitive Impairments" Pervasive

Computing Technologies for Healthcare, 2008. Pervasive Health

2008. Second International Conference on 2008: pp.l08-111.

[31] Guang-yao Jin, Xiao-yi Lu, Myong-Soon Park, "An indoor

localization mechanism using active RFID tag", Sensor

Networks, Ubiquitous, and Trustworthy Computing, 2006. IEEE

International Conference on 2006.

[32] Jeffery Hightower, CaetaNo Borriello and Roy Want, "SpotON:

An Indoor 3D Location Sensing TechNology Based on RF

Signal Strength," University of Washington, Department of

Computer Science and Engineering, Seattle, WA, Feb. 2000.

[33] L. M. Ni, Y. Liu, Y.C. Lau and A. P. Patil," LANDMARC :

Indoor Location Sensing Using Active RFlD," Wireless

Networks, vol. 10, no. 6 , pp.701-71O, Nov. 2004.