dynamic fall detection and pace measurement in walking sticks

7/31/2019 Dynamic Fall Detection and Pace Measurement in Walking Sticks

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Dynamic Fall Detection and Pace Measurement in Walking Sticks

Oscar Almeida, Ming Zhang, Jyh-Charn Liu

Dept. of Computer Science, Texas A&M University

{oscar10, zhangming, jcliu} @tamu.edu

Abstract

Falls are increasingly among the leading causes of

elderly injuries and deaths each year. Several of these

victims depend on a walking stick or cane for support

while walking. Rendering aid more quickly to those who

fall may decrease the severity of injury in several cases.

In this paper, we propose a dynamic fall detection system

embedded into walking sticks and canes. By using a

gyroscope chip to measure angular velocity of the stick,

we can detect when a user may have fallen. Also

monitored by the detection system is the users walkingpace, such that the user will be warned when traveling at

paces above his or her normal speed. With different

polling frequency levels to preserve energy, we present a

low-power device that can potentially improve safety

among the elderly.

1. Introduction

Historically, walking sticks have provided a means of

wardrobe accessories, protection, and aid to the balance-

impaired. Today, walking sticks are overwhelmingly used

by the elderly who need assistance in walking. By holding

a stick or cane either in the dominant hand, or in the hand

opposite the weakness or injury, the user can shift his or

her weight away from the weaker side of the body.

According to the National Center for Injury Prevention

and Control, more than one-third of adults over the age of

65 fall each year. In addition, falls are increasingly the

leading cause of injury deaths and nonfatal injuries of the

elderly. Falling down can result in bone fractures and or

brain trauma [1].

Several risk factors increase the probability of an

elderly person becoming a victim of a fall. Osteoporosis,

a disease that causes bones to become more fragile, allows

bones to be more susceptible to fractures. In fact,

osteoporosis can progress through the body painlessly

until such a fracture occurs [2]. Next, impaired vision and

environmental hazards can cooperatively cause harm to

the unaware walker. At least one-third of all falls in the

elderly involve environmental hazards in the home [3].

Another risk factor, glaucoma, results in a loss of vision

by damaging the optical nerves. About half of the people

in the United States that have glaucoma do not know it.

Another type of visual impairment is a cataract. These are

experienced by more than half of Americans over the age

of 80 [4]. Lastly, a lack of physical exercise becomes a

risk factor, especially when one of the previous risk

factors is also met [3].

Due to the high frequency and severity of falls in the

elderly population, it is vital that a fall be detected and

reported immediately. In this paper, we propose that

embedding a dynamic fall detection system into walking

sticks and canes will significantly reduce the time

necessary to render aid to a fall victim. Section 2

describes the survey of technology and high-level system

description. Following is the signal processing algorithmfor the gyro output, leading to our methodology behind

the proposed fall detection. Section 5 illustrates the user-

specific pace measurement benefits of this device, and

section 6 describes the power considerations taken.

2. System Overview

Our proposed dynamic fall detection device consists

of two main parts: the fall detection device and the

alarming device. When a fall is detected, the user is asked

to push a reset the stick via a switch. If this action is not

carried out in a timely manner, the alarming device will

become activated. This paper focuses only on the fall

detection itself, while the alarming procedure is left as a

future area of research.

The underlying concept behind dynamic fall detection

is that while the walking stick is in use, the sticks angular

velocity is constantly being polled. Consider a static fall

detection system based on orientation alone [5]. A false

negative may occur if a victim falls but the stick never

reaches a horizontal position. Also, a false positive will

occur every time the stick rests unused in a horizontal

position. Our proposed dynamic methodology for fall

detection is more likely to see the data signal cross some

angular velocity threshold during any fall, even if the stick

does not fall completely to the floor. The second

objective of our design is to monitor the users walking

pace to identify abnormalities.Our testing prototype is driven by an Atmel EB63

microprocessor evaluation board and a Gyration MG1101

MicroGyro. The gyroscope chip (gyro), which measures

angular velocity, is polled at an appropriate frequency to

detect if the user may have fallen. The EB63 must poll

the gyro for input data, process the data, and when


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triggered, signal for help in the event that a fall is

detected. The board is also responsible for power

management. Because our algorithms require only a small

amount of calculations, a small processor chip would be

capable of performing the required operations.

3. Signal Processing

A noisy gyro output signal can occur due to wiring,

along with vibrations of the gyro as the stick makes

contact with the ground. Figure 1(a) shows an example of

raw data output from the gyro. Point A represents the first

of two parts of a step with a walking stick. This includes

the user placing the sticks base on the ground in front of

him or herself. Point B represents the step taken by the

user to stand beside the stick. We observe that the local

minima and maxima are not very well defined in this

signal, and attribute the cause to noise.

In order to eliminate minor signaling errors, we

propose the use of a small, weighted running average,low-pass filter. While this filter is targeted specifically at

local minima and maxima, the integrity of the global

maxima and minima are preserved. This smoother

processed output signal is more representative as a whole

of the walking sticks motion than the raw data.

Figure 1(b) shows the same data after being processed

by the running average low-pass minima/maxima filter.

This smoother representation of the gyro output is used

for fall detection.

(a)

(b)

Figure 1: (a) Raw and (b) filtered gyro data.

Figure 2: Walking stick prototype with gyro and Atmel

EB63 evaluation board. Orientation of the gyro, located

at the base of the stick, is displayed on the left.

4. Fall Detection

Because its main function is to generate vertical

support to the user, a canes angular range of motion is

extremely limited. We define T to be the maximum angle

from vertical that is achievable during normal operation.

Additionally, we assume the user is stable after each step

successfully taken. Movement away from a stable point

represents the walking sticks stability. Mathematically,

we define stability as a summation of sequential gyro

outputs, such that each output is greater than a given

threshold value Treset. Another reason the stability is reset

with every step is that the gyro does not provide thesystem with a point of reference in terms of displacement.

It is impossible to maintain a net sum of angular velocities

accurately enough to determine the exact net displacement

of the stick. Displacement would also be dependent on

the frequency at which the gyro is polled.

The most current readings of the angular velocities in

axes A and B are represented by At and Bt respectively.

Calibration of the stick occurs by creating a physical

mapping of T to a data threshold T fall. Another data

element, R, as shown in Eq. 1 represents the current

magnitude of the resultant angular velocity from axes A

and B combined. Eq. 2 then uses the value of R to define

the stability S. If S is greater than or equal to Tfall at anypoint in time, then it is assumed that the user may have

fallen, and the alarming device should be activated.

2 2

t tR A B= + Eq. 1

0,

,

reset

reset

R TS

S R R T


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5. User-specific Design

In addition to fall detection, we present a method of

characterizing users based on their walking pace. If a user

is determined to be walking above their normal average

pace, the stick can warn the user to slow down. Angular

velocity data is polled at a sampling rate of 15 Hz. Eachvelocity datum has two orthogonal components, along

axes A and B. One axial component may be significantly

larger than the other. In such a case, we consider only the

axis with a larger magnitude for the pace calculation. The

first 50 data points taken are used to make the decision to

choose the axis to be used.

A moving average over five data points is used to form

a smoother data curve. Let V(k) denote the angular

velocity at a point in time, k. We consider the time period

between two adjacent peaks of angular velocity as one

step. To find peaks, we use a moving average over 100

data points as the threshold. A search procedure is

initiated to find the peak point from another point with avalue larger than the threshold. The procedure is

terminated if the next points value drops below the

threshold. This peak point is marked with a vertical black

line, as shown in Figure 3. To get the pace between two

adjacent peaks, we first calculate the distance and then

divide by the time interval (Eq. 3). Here, p1 and p2

represent the two adjacent peaks. A moving average over

N steps is calculated and used to determine how fast the

user is walking.

2

12 1

1( )

1

p

k p

v V kp p =

= +

Eq. 3

We performed four experiments, walking at different

paces. Two were slower, while two were at a faster pace.

The summary of these experiments are show in Table 1.

One can see that there is a large difference between a slow

and fast walking pace. The normal pace can be achieved

by a simple calibration procedure.

Table 1

Exp. IDWalking

speed

#Data

pointsAvg. pace

1 Very Slow 2736 4012 Slow 1926 551

3 Fast 1049* 751

4 Very Fast 1246 1200

(* distance walked was 25% less than in Exp. 1, 2 and 4)

0 20 40 60 80 100 120 140 160 180 200-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1x 10

4

Data point

Angular

Velocity

Figure 3

6. Low-power Design

In order to maintain the walking stick as a low-powerdevice, we implement different modes of operation. Each

mode corresponds to a unique polling frequency. For

normal operation, we chose a low sampling rate of 15Hz.

If the stick is assumed to be idle, we reduce the polling to

1Hz. This frequency is still enough to detect a step. If the

stick remains idle, the stick enters a deep sleep mode,

polling only once every ten seconds until motion is

detected. Of course, signal processing will be disabled

while in either sleep mode.

7. References

[1] Centers for Disease Control and Prevention, FallsAmong Older Adults: An Overview, Center for Disease

Control and Prevention, 2007. [Online]. Available:

http://www.cdc.gov/ncipc/factsheets/adultfalls.htm.

[2] National Osteoporosis Foundation, Osteoporosis: A

debilitating disease that can be prevented and treated,

National Osteoporosis Foundation, 2007. [Online].

Available: http://www.nof.org/osteoporosis/index.htm.

[3] K. R. Tremblay Jr. and C. E. Barber, Preventing Falls

in the Elderly, Colorado State Univerity Cooperative

Extension, 2006. [Online]. Available: http://www.ext.

colostate.edu/PUBS/CONSUMER/10242.html.

[4] National Eye Institute, Cataract,National Eye

Institute, 2006. [Online]. Available:

http://www.nei.nih.gov/health/cataract/cataract_facts.asp

[5] MedGadget, i-Stick, and Intelligent Walking Stick,

MedGadget, 2006. [Online]. Available: http://medgadget.

com/archives/2006/11/istick_help_ive.html

dynamic fall detection and pace measurement in walking sticks

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