Evaluation of Threshold-based Fall Detection on Android Smartphones

Tobias Gimpel, Simon Kiertscher, Alexander Lindemann, Bettina Schnor and Petra Vogel

Department of Computer Science, University of Potsdam, August-Bebel Str. 89, Potsdam, Germany

Keywords:

Fall Detection, Development of Assistive Technology, Sensors-based Applications.

Abstract:

This paper evaluates threshold-based fall detection algorithms which use data from acceleration sensors that

are part of the current smartphone technology. Different detection algorithms are published in the literature

with different threshold values. This paper presents the evaluation of 5 different algorithms which are suited

for Android smartphones. In contradiction to prior work, our experiments indicate that the Free Fall detection

Phase is necessary for a low False Positive Rate. Further, we present an empirical evaluation of currently

available fall detection apps in the Google Play store.

1 INTRODUCTION

The probability of falls increases for older people.

Approximately, about 33 % of older persons fall un-

intentionally each year (Mellone et al., 2012). Falls

with the loss of consciousness are of special harm.

Therefore, several groups are working on automatic

fall detection, for example (Bourke et al., 2007;

Sposaro and Tyson, 2009; Dai et al., 2010; Fudickar

et al., 2014).

In this paper, we will only consider fall detec-

tion systems using Android based smartphones due to

their widespread use and availability. Current smart-

phones are equipped with a tri-axial accelerometer

sensor which periodically collects a vector of axis-

speciﬁc acceleration data. Typically, this is used to

re-orient the screen as a user moves the device. But

this sensor has also potential to be used for fall de-

tection. Smartphone operating systems such as An-

droid aim to save energy and typically sample with

low rates (between 20 and 100 Hz). Prior studies have

shown that these sampling-rates are suited for fall de-

tection (Mehner et al., 2013; Fudickar et al., 2014).

An optimal fall detection algorithm should com-

bine a high sensitivity with a high speciﬁcity. The

sensitivity is a measure for the number of correctly

detected falls:

Sensitivity =

TruePositives

Number of all falls

where TruePositives is the number of correctly de-

tected falls.

The speciﬁcity measures the rate of

TrueNegatives, i.e. the percentage of correctly

classiﬁed non-fall situations:

Speci f icity =

TrueNegatives

Number of all non − falls

A high speciﬁcity means a low number of false

alarms. This is also a very important metric for the

usability of the system.

We analyzed the already published algorithms for

fall detection and discovered that there exist differ-

ent variations of the algorithms which differ for ex-

ample in the calculation of the orientation change

and in their threshold settings. Since these algo-

rithms were not compared before, we implemented

and tested these different algorithms. As a result from

these tests, we delivered new threshold values which

perform better than the ones published before.

In September 2014, there are already about 13

apps available in the Google play store which claim to

perform fall detection. The second part of this work

is a survey about the quality of those applications.

The remainder of the article is structured as fol-

lows: In Section 2 related work is discussed. The ba-

sic algorithm of threshold-based fall detection is in-

troduced in Section 3. Section 4 presents the evalu-

ated algorithms and parameter settings. The results

of our evaluation are shown in Section 5. Further,

we tested current available fall detection applications

from the Google App Store. The results are presented

in Section 6. The article ends with a conclusion.

598

Gimpel T., Kiertscher S., Lindemann A., Schnor B. and Vogel P..

Evaluation of Threshold-based Fall Detection on Android Smartphones.

DOI: 10.5220/0005280805980604

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2015), pages 598-604

ISBN: 978-989-758-068-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

2 RELATED WORK

Several groups have already investigated fall detec-

tion on smartphones.

In 2010, Dai et al. proposed the PerFallD proto-

type for the ﬁrst available Android smartphone, the

G1 phone (Dai et al., 2010). The detection algorithm

uses four thresholds to detect an impact and a sta-

ble phase and the change of orientation. The chosen

thresholds are not applicable for current Android ver-

sions anymore, but the study already shows the fea-

sibility of smartphone based fall detection. For the

evaluation, data from 450 falls were collected with

different directions (forward, lateral and backward),

different speeds (fast and slow) and in different en-

vironment (living room, bedroom, kitchen and out-

door garden). Further, data of activities of daily living

(ADL) including walking, jogging, standing and sit-

ting were gathered. Fall and ADL data were provided

by graduate students from 20 to 30 years old. The au-

thors present evaluations for wearing the smartphone

in a shirt pocket, on the belt, or in the pant pocket. The

presented evaluation shows that PerFallD achieves the

best performance when the device is attached with

the belt with an average False Negative value being

2.67 % and the False Positive value being 8.7 %.

While this is a promising result, no evaluation with

ADLs of older people is given. Further, the authors

report that the system keeps running about 33.5 h un-

til the battery is exhausted which also conﬁrms the

usability of these devices for fall detection.

Dai et al. compare their system with a commercial

product provided by Brickhouse (Brickhouse, ) . This

fall detector consists of a teleassist base and a portable

sensor. Since the base device needs to be installed

indoors and the signal transmission distance between

the sensor and the base is limited, the system is only

useful in this environment. The experiments reported

in (Dai et al., 2010) show that this system has a high

false negative rate in backward falls (29.9 %) and a

high false positive rate (21.9 %).

In (Hwang et al., 2012), a smartphone running

Android 2.3.3 was used to record 100 Hz bandwidth

signals from the three-axis acceleration sensor. The

saved data were processed to detect falls using Mat-

lab 7.0 not on the device itself, but on a PC. Based

on data from 200 experimental falls, obtained by fas-

tening a smartphone to a belt worn around the waist,

an overall detection rate of 95% was achieved, cor-

responding to direction-speciﬁc rates of 94% for for-

ward falls, 100% for backward falls, 94% for leftward

falls and 92% for rightward falls. Based on the exper-

imental results of 6 ADL scenarios, the threshold for

acceleration was established at 2.2 g and the threshold

for angular displacement was set at 50

◦

Since Android based smartphones use sampling-

rates of maximum 100 Hz, different sampling rates

were investigated in a trace-driven simulation ((Fu-

dickar et al., 2014)). The results conﬁrm that low

sampling rates of at least 50 Hz can be used and have

a sensitivity of 99% for the collected fall records.

Further, the speciﬁcity of the threshold-based fall-

detection algorithm was tested with ADLs of elderly

people. In the simulation, no false positive occurred.

The threshold settings were taken from (Karth, 2012).

Mehner et al. (Mehner et al., 2013) also published

results from experiments with threshold based fall-

detection on smart phones which conﬁrm that the

lower sampling rates (such as 50 Hz) that are sup-

ported by Android are uncritical. Their results indi-

cate that the exclusion of the free-fall detection phase

may increase the detection accuracy by 27 % from

56% with free-fall detection to 83% without free-fall

detection. Overall the proposed algorithm achieved a

maximal sensitivity of 83% and a speciﬁcity of 100%

in the presented experiments. Results from real falls

and ADLs from elderly people are not presented.

3 THRESHOLD BASED FALL

DETECTION

For a tri-axial accelerometer, fall situations are char-

acterized by multiple sequential phases. The ac-

celerometer collects a vector (x,y,z) of the axis-

speciﬁc acceleration forming together the accelera-

tion. The length of the acceleration vector corre-

sponds to the power of the acceleration. Since we use

gravity as our metric, we have to divide the length of

the acceleration vector by the power of the gravity on

earth which is 1g = 9, 81

. This leads to equation

(1):

−→

v | =

+ y

+ z

9, 81

(1)

This value is the so-called G-Value used in the thresh-

old based fall detection algorithm.

The different phases of the fall detection algorithm

are shown in Figure 1. The fall detection algorithm

starts with the FreeFall Test Phase. Within a fall

situation, the falling body experiences zero-gravity.

The exact gravity appeals on a body (or mass in gen-

eral) depends on where you are on earth. This is due to

gravity anomalies and the fact that the earth is not a perfect

sphere. One can say in general, that gravity is stronger near

the poles. The differences in gravity is still small enough to

set a general value for gravity in a fall detection application

EvaluationofThreshold-basedFallDetectiononAndroidSmartphones

599

Figure 1: The different phases of the fall detection algo-

rithm.

The free fall is the initial event of each fall situa-

tion and therefore has to be recognized ﬁrst. To de-

tect a free fall, the gravity value must drop below the

free fall threshold LOWER THRESHOLD for a min-

imal duration of TIMER-FREEFALL. If this is not

the case and the threshold is exceeded again before

TIMER-FREEFALL has expired, the algorithm stays

in the FreeFall Test Phase. Other algorithms (such as

(Bourke et al., 2007; Mehner et al., 2013; Fudickar

et al., 2014)) exclude a minimal duration for free fall

detection since without the free fall detection phase

they observed a higher fall detection rate in their ex-

periments.

Once a free fall was detected, the Impact Test

Phase starts in which the algorithm tries to detect an

impact, which is given if the G-Value exceeds the so-

called UPPER THRESHOLD. The maximal duration

between free fall and impact recognition may not ex-

ceed the duration deﬁned in TIMER-IMPACT. If this

period expires and no impact was detected, the algo-

rithm resets and starts again with the FreeFall Test

Phase.

In a fall situation the impact is followed by a sta-

ble phase, thus the algorithm switches into the Stable

Test Phase. During this Phase, the G-Values has to

drop below the VARIATION threshold (1st value of

the Stable A row of Table 2), for a minimal duration

of TIMER-NOMOVEMENT (2nd value of the Sta-

ble A row of Table 2). All this must happen within

another time window called TIMER-STABLE (3rd

value of the Stable A row of Table 2) to pass this

phase. Mehner et al. (Mehner et al., 2013) divide this

phase into two sub-phases (depicted by the Stable B

row of Table 1 and 2). The ﬁrst sub-phase is only

for omitting ﬂuctuations in the G-Value which may

occur right after an impact and its duration is equal

to TIMER-NOMOVEMENT. The second sub-phase

tests, if the VARIATION threshold is exceeded by the

G-Value within the time of that sub-phase which is

equal to the TIMER-STABLE parameter. If it is ex-

ceeded the Stable Test Phase has failed and the algo-

rithm resets.

The correct distinction between a fall situation and

an Activity of Daily Life (ADL) is one of the most im-

portant features of a fall detection algorithm, since

even a small false positive rate will make the algo-

rithm useless for real world scenarios. To distinguish

a fall situation from an ADL, some researcher pro-

pose an Orientation Test Phase (Karth, 2012; Mehner

et al., 2013). During this phase it is tested, if the de-

vice, and thus its carrier has changed its orientation

signiﬁcantly. This can be done in different ways as

depicted in Table 1 and 2 by the A, B and C in the

Orientation row.

• Version A, from (Karth, 2012), takes the last ac-

celeration vector prior to a possible fall situation

and compares it with the acceleration vector given

after the Stable Test Phase. If the difference is 45

◦

or more, the fall is veriﬁed.

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• Version B, from (Mehner et al., 2013), compares

the mean of the last 100 values before the fall sit-

uation with the mean of the ﬁrst 100 values after

the detected fall. This is done separately for every

axis. If one of the axis has a deviation of 0,4g or

greater, the fall is veriﬁed.

• Version C which is proposed in this paper com-

bines Version A and B and compares the mean ac-

celeration vector

−→

v of the last 100 acceleration

vectors given before the fall situation occurred

with the mean acceleration vector

−→

w of the last

100 acceleration vectors given after Stable Test

Phase is passed. The angle is calculated as:

α = arccos

−→

v ∗

−→

v | ∗ |

−→

w |

(2)

If the angle is greater than 66

◦

, the fall is veriﬁed.

After a fall was detected by passing all previous

phases, the Alert Phase begins. In this phase, a timer

is started and the device informs the user that a fall

was detected. Typically the user can conﬁrming or

aborting the alert, so an emergency call can be either

initiated or prevented. In case the timer expires and

no action was chosen, an emergency call is initiated.

It often happens to elder people that they will not lose

its consciousness after a fall, but will still be unable to

interact with the device. If this is the case, the timer

approach will work ﬁne due to an activation of the

hands-free phone system. Alternatively or in addition

to an emergency call, a SMS message, TCP message

or other message systems can sent alerts to a conﬁg-

ured phone number, IP address etc.

4 EVALUATION ENVIRONMENT

While fall detection with smartphones seems to be a

promising approach and different groups have pub-

lished work in this direction, an analysis with real falls

of elderly people is unrealistic and still missing.

In (Fudickar et al., 2014), results from a simula-

tion with ADLs of elderly people was presented. It

was shown that fall detection is also possible with the

low sampling rates used on smartphones. But the sim-

ulated fall detection algorithm was adapted for a spe-

cial sensor, the ADXL345, which is not used in cur-

rent smartphone technologies.

Therefore, we decided to repeat the experiment for

Android smartphones. The following steps were exe-

cuted:

1. implementation of the fall detection algorithm (as

described in Section 3) for Android smartphones,

2. integration of the new implemented fall detection

algorithm into the simulation testbed,

3. collecting falls with an Android smartphone

4. collecting ADLs from elderly people with an An-

droid smartphone.

During the implementation phase, we considered

the already published algorithms and discovered the

different variations presented in Section 3. Since it

was still unclear which algorithm and parameter set-

ting will give the best results, we implemented and

tested the following conﬁgurations for Android smart-

phones:

1. Karth FF: The parameter values for thresholds

and timer settings were taken from (Karth, 2012).

2. Karth: The same as Karth FF, but the Free Fall

Phase is omitted.

3. Mehner FF: The parameter values for thresholds

and timer settings were taken from (Mehner et al.,

2013).

4. Mehner: The same as Mehner FF, but the Free

Fall Phase is omitted.

5. Gimpel: The fall detection algorithms

from (Karth, 2012) and (Mehner et al., 2013)

were implemented by Tobias Gimpel in (Gimpel,

2014). He also developed a combination of the

former two settings.

An overview of the different variants and the relat-

ing parameter settings are given in the Tables 1 and 2.

5 RESULTS

The sensitivity and speciﬁcity of the threshold based

fall detection algorithms is evaluated with the fall sets

and ADLs that are described in the following subsec-

tions.

5.1 Fall Set and Sensitivity

Since, the risk of injuries in case of older test persons

is obviously much too high, the fall set was recorded

by three test persons between 23 and 55 years old.

The fall set covered frontal falls, falls to the left

and to the right. The test persons used two types of

smartphones: HTC Desire 816 and Sony Xperia V.

The device was put into a funny bag at the hip in front.

The ﬁrst test person (29 years old) simulated 30

falls: 10 frontal, 10 to the left side, and 10 to the right

side (prob29). The second test person (23 years old)

simulated 12 falls: 4 frontal, 5 to the left side, and 3

to the right side (prob23). The third test person (55

EvaluationofThreshold-basedFallDetectiononAndroidSmartphones

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Table 1: Different versions of the fall detection algorithm.

Karth FF Karth Mehner FF Mehner Gimpel

Free Fall X X X

Impact X X X X X

Stable A X X X

Stable B X X

Orientation A X X

Orientation B X X

Orientation C X

Table 2: Parameter settings of the different algorithms.

Karth Mehner Gimpel

Free Fall 0,75g/30ms 0,5625g/30ms 0,75g/30ms

Impact 2g/500ms 2,3g/300ms 2g/500ms

Stable A 0,4375g/1sec/3,5sec - 0,2g/1sec/3,5sec

Stable B - 0,4g/1sec/1sec -

Orientation A 45

◦

- -

Orientation B - 0,4g -

Orientation C - - 66

◦

years old) simulated 10 falls: 4 frontal, 3 frontal left,

and 3 frontal right (prob55). The different fall char-

acteristics and number of falls are due to the different

fall capabilities of the test persons. All falls recorded

were critical falls, with loss of consciousness (where

the test person did not move after the falls).

We used the data from our simulated falls as input

to test the algorithms and parameter settings described

in Section 4. The results are shown in Table 3. In the

column NOF the number of falls is given. The detec-

tion rate using the Karth FF, Karth, and Gimpel

settings are very good. The Mehner settings show an

inconsistent behavior. For all three test persons, the

algorithm without Free Fall Phase has a higher sen-

sitivity, which correlates with prior published results

in (Mehner et al., 2013). But for the falls from prob29,

the sensitivity for both variants is only about 50 %.

While these variants have a good detection rate for

falls ahead, they have difﬁculties to detect falls to the

left or right side.

5.2 ADLs of Elderly People

To evaluate the speciﬁcity of the algorithms, we

recorded the acceleration characteristics of ADLs of

two test persons, 55 resp. 72 years old. As before, the

test persons were equipped with a fanny pack, worn at

the test persons’ hip in which the device was carried.

The considered recording duration per test person

ranged from 11 (prob72) to 286 (prob55) hours. The

older test person carried the device during one day,

while the younger test person carried it several days

during her daily activities covering typical daily activ-

ities including eating lunch, walks, activities at work

and also homework.

The maximal recorded acceleration in the cropped

recordings was at 3.17 g.

Table 4 shows the results for the different

versions and parameter settings. The most ro-

bust versions are Mehner with the Free Fall

Detection Phase (no False Positives) and Gimpel

(only 2 False Positives). Further, the Karth FF ver-

sion including the Free Fall Phase also performs better

than the simple Karth version. But both Karth ver-

sion show much higher False Positives than the other

settings. Obviously, the Fall detection Phase is impor-

tant for a low False Positive Rate.

5.3 Interpretation of the Results

While (Mehner et al., 2013) proposed the exclusion

of the Free-Fall Detection step to enhance the sensi-

tivity, our experiments with ADLs show that the free-

fall detection phase is important for a low False Pos-

itive Rate. Therefore, we consider in the following

only those algorithms which use this phase: Karth

FF, Mehner FF, and Gimpel. From these, Mehner

FF has a signiﬁcantly worse sensitivity (see Table 3).

The sensitivity of Kart FF and Gimpel is compara-

ble. Since regarding the speciﬁcity, Gimpel is much

better than Kart FF in our experiments, we conclude

that the settings from Gimpel are a good compromise.

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Table 3: Sensitivity: Detected Falls.

fall direction NOF Karth FF Karth Mehner FF Mehner Gimpel

prob29 overall 30 30 28 13 15 26

ahead right 5 5 4 5 5 4

ahead left 5 5 5 5 5 4

left side 10 10 10 3 4 10

right side 10 10 9 0 1 8

prob23 overall 12 10 9 3 10 9

left side 5 4 4 1 5 3

right side 3 2 2 2 2 3

ahead 4 4 3 0 3 3

prob55 overall 10 9 10 2 8 9

ahead right 3 3 3 1 2 3

ahead left 3 3 3 0 3 3

ahead 4 3 4 1 3 3

Table 4: Speciﬁcity: Results from ADLs.

duration Karth FF Karth Mehner FF Mehner Gimpel max g min g

prob55 286 h 24 57 0 5 2 3.179 0.061

prob72 11 h 0 3 0 1 0 2.555 0.041

6 EVALUATION OF APPs FROM

THE GOOGLE APP STORE

The second part of this work is a survey about useful

fall detection applications in the Google play store. In

September 2014, the search for fall detection, within

the Google play store, results in 22 hits for applica-

tions in different categories (e.g. medicine, health &

ﬁtness, lifestyle and social). 13 out of these 22 appli-

cations are related to real fall detection and thus were

considered in the following. 2 out of the 13 remaining

applications were commercial applications and avail-

able for 4 C and 120 C respectively. Due to the high

price, the 120 C application was not tested any fur-

ther.

First, we tested their user interface and the usabil-

ity. The following characteristics of an application

resulted in an exclusion for further tests:

• a failed or impossible installation

• no reaction of the application after installation

• the need to register for a phone call in a foreign

country

• the phone call destination is not obvious

Only 8 applications passed these initial tests and

were further tested regarding their sensitivity and

speciﬁcity.

The speciﬁcity was tested with a ﬁxed set of ac-

tivities of daily living (ADL) like walking around,

climbing stairs or sitting down on a chair. All this was

done at varying speeds in a time window of about 10

minutes per application. During these tests, the smart-

phone was in a trousers’ pocket.

The sensitivity of the applications was tested with

10 falls in forward direction performed by two test

persons (23 resp. 55 years old). During the fall tests,

the smartphone was worn in a belt bag in front.

The results of the tests are shown in Table 5. The

FP column indicates observed false positives within

the ADL tests. Columns prob23 and prob55 refer to

the 23 and 55 years old test persons. 50 % of the appli-

cations did not pass the ADL test and indicated falls

while executing. Regarding the sensitivity, the best

application was iCare Personal Emergency Alert with

a detection rate of 70 %, followed by Fade: fall de-

tector and SecureMe Active with 60% detection rate

each. All other applications had a very low sensitivity

even for the easiest category of falls in forward direc-

tion, or low speciﬁcity.

7 CONCLUSIONS

Automatic fall detection is one of the most important

assistive technologies for elderly to lead a self deter-

mined life. An old person who lives alone is sup-

ported by the automatic fall detection system and feels

safer.

EvaluationofThreshold-basedFallDetectiononAndroidSmartphones

603

Table 5: Test results for applications from the Google play store.

name published in app store by FP prob23 prob55

T3LAB Fall Detector T3LAB-Technology Transfer Team no 1/5 3/5

iCare Personal Emergency Alert Fansoft Labs no 5/5 2/5

Smart Fall Detection Hamideh Kerdegari no 0/5 0/5

Emergency Fall Detector Socaplaya21 no 0/5 0/5

Fall Detector Spantec GmbH yes 0/5 0/5

Fade: fall detector ITER S.A. yes 3/5 3/5

iFall: Fall Monitoring System FSU Mobile Solution yes 0/5 2/5

SecureMe Active Orion Systems yes 2/5 4/5

Threshold-based fall detection on smartphones

has shown to be a promising approach. In this study,

we evaluated different parameter settings and also

tested fall detection applications from the Google play

store.

The results of our experiments are:

1. We propose a variant of the known threshold-

based fall detection algorithms which performs

better than the algorithms published before in the

literature. The so-called Gimpel variant has a

high sensitivity (44 of 52 falls detected) and a

high speciﬁcity (only 2 false positives in 297 h

ADLs). Regarding sensitivity, the Karth FF al-

gorithm performed better (49 of 52 falls detected),

but suffers from a lower speciﬁcity (24 false posi-

tives).

2. While (Mehner et al., 2013) proposed the exclu-

sion of the Free-Fall Detection step to enhance the

sensitivity, our experiments with ADLs show that

the free-fall detection phase is important for a low

false positive rate.

3. The tests with the applications from the Google

App Store show that the currently available appli-

cations have to be considered with care. Only one

application performed satisfying in our tests (no

false positive and 4 of 10 falls detected).

4. From the experiences with the applications from

the Google App Store, we conclude that collect-

ing fall data is a critical factor for the evaluation:

different test persons tend to have different simu-

lated fall characteristics.

In the next step, we will investigate the usability

of our system in cooperation with a nursing home.

While younger people are used to the smartphone

technology, this may be a burden for elderly people.

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