A Wearable Embedded System for Detecting Accidents while Running

Vincenzo Carletti, Antonio Greco, Alessia Saggese, Mario Vento and Vincenzo Vigilante

Dep. of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, Italy

Keywords:

Fall Detection, Embedded Systems, Wereable Devices.

Abstract:

Every year 424,000 fatal accidents occur, they are the second cause of unintentional death after road trafﬁc

injuries. The difference between fatal and not fatal accidents often is the presence of other people able to

promptly provide ﬁrst aid or call for help. Unfortunately, even during the practice of group activities (e.g.

team sports) an accident can happen when a person is alone or out of sight; thus, the availability of devices

able to detect if a serious accident is occurred and consequently arise an alarm to other people is an important

issue for the safety of people. Starting from these considerations, in this paper we propose a wearable device

able to detect accidents occurring during the practice of running. The device uses a one class SVM trained only

on the normal activity and classiﬁes as anomalies all the unknown situations. Then, in order to avoid alarms

related to non dangerous events, the output of the classiﬁer is analyzed by an additional stage responsible to

detect if the person is or not unconscious after an abnormal event. In the former case an alarm is arisen by the

system.

1 INTRODUCTION

The number of fatal accident occurring each year has

been estimated around 424,000, and this is the second

cause of unintentional death after road trafﬁc injuries.

Approximately 37.3 million falls are severe enough to

require medical attention (Sadigh et al., 2004; Kalace

et al., 2008; Igual et al., 2013), and the seriousness of

these accidents is higher if they happen when people

are alone or out of sight, situation that typically occurs

when people are doing some sports.

Therefore, the availability of a device that can be

easily worn by the sportsman and able to instantane-

ously alert other people when an accident occurs can

considerably reduce the consequences. Of course, the

detection of falls strongly depends on the monitored

activity. Indeed, a fall can be considered as an abnor-

mal pattern in a traditional pattern recognition pro-

blem, and something that can be considered normal

in a sport could be abnormal in a different sport.

For instance, the problem of detecting falls of

skying person is completely different if compared

with the same task on people who are running. The

complexity and the diversity of fall detection is con-

ﬁrmed by the growing interest, in the recent years,

of the scientiﬁc community ((Delahoz and Labrador,

2014; Brun et al., 2014; Habib et al., 2014; Koshmak

et al., 2016; Khan and Hoey, 2017)). From the above

papers, we can note that it is possible to identify two

main classes of fall detection systems: context-aware

systems and wearable systems. In the ﬁrst case the sy-

stem is installed in the environment where the people

to be monitored acts and uses sensors (like cameras,

acoustic sensors, pressure sensors, infrared sensors,

lasers and Radio Frequency Identiﬁcation) that are

properly deployed inside the monitored area. The ad-

vantage in this case is that the targeted person does not

need to wear or carry any special device. However,

they are suitable for those situations where the envi-

ronment to monitor is well deﬁned and circumscribed

such as hospitals, nursing house or other indoor envi-

ronment. If the activity is performed in a wide and un-

controlled area, this kind of systems becomes unsuit-

able due to the restrictions imposed by the mobility

of the person. As an example, if the aim is to moni-

tor running people, we should limit the activity inside

a gym avoiding the people to run outside. Wearable

systems are evidently the solution to the above issues

when the monitored environment is not restricted or a-

priori known, and the activity can be practiced every-

where. In general, there are specialized devices com-

posed of an elaboration unit and a batch of sensors

like accelerometers, gyroscopes and magnetometers

used to analyze people motion. In addition, these de-

vices provide some kind of wireless connectivity to

communicate with a smartphone in case of accidents,

so as to phone some sets of emergency number. Dif-

ferently from context-aware systems that can be in-

Carletti, V., Greco, A., Saggese, A., Vento, M. and Vigilante, V.

A Wearable Embedded System for Detecting Accidents while Running.

DOI: 10.5220/0006612805410548

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP, pages

541-548

ISBN: 978-989-758-290-5

541

stalled on servers, the hardware in wearable systems

is strongly limited by the size and the consumption.

Indeed, they must be comfortable to wear and gua-

rantee to be active for all the duration of the monito-

red activity (often a couple of hours). For this reason,

most of these systems use very simple fall detection

threshold-based algorithms aimed to provide a good

trade-off between the accuracy and the computational

requirements. A ﬁrst impact based system has been

proposed by (Chen et al., 2005); falls are detected by

estimating the posture and by evaluating if the magni-

tude of the accelerometer is higher than a ﬁxed thres-

hold. Other interesting methods using accelerometer

and gyroscope have been proposed by (Bourke et al.,

2008; Bourke and Lyons, 2008; Bourke et al., 2010).

Such methods differ in terms of used sensors, sen-

sor positioning, and threshold. In particular, (Bourke

et al., 2010) have proposed a system placed on the

waist that uses thresholds in velocity, impact and pos-

ture. However, as highlighted in (Bagal et al., 2012),

most of these systems are designed and tested to work

in very simple situations such as monitoring elder pe-

ople who are walking so as to detect falls, thus when

they are applied in real environments or on more com-

plex tasks their performance drastically collapses.

Only recently, more complex approaches based

on machine learning techniques have been proposed

by (Choi et al., 2011; Abbate et al., 2012; Albert et al.,

2012; Shi et al., 2012; Fahmi et al., 2012; Khan and

Hoey, 2017). Among them, the most interesting met-

hods by (Jie Yin et al., 2008; Medrano et al., 2014;

Micucci et al., 2017) are based on the observation that

obtaining a large amount of training data of falls is un-

reasonable, but it is possible instead to obtain a large

amount of data for Activity of Daily Living (ADL)s,

corresponding to normal patterns. Thus, the detection

of falls happening during the execution of daily living

activities can be faced as an anomaly detection pro-

blem where the system is trained only on the ADLs

and considers them as normal patterns, while classi-

fying every other situation (e.g. falls) as an abnormal

pattern. As evident, a similar assumptions can hold if

we move from falls of elder persons to falls of sports-

man.

In this paper, we will focus on the detection of

falls happening during the practice of sports, with par-

ticular reference to running. In more details, we de-

sign and develop a device to be worn by the sportsman

on the wrist, like a watch. The main advantage in this

choice lies in the fact that such a device is unobtru-

sive, since it is comfortable to wear (e.g. no need to

take them off while sleeping or changing clothes) and

require little or no maintenance (e.g. charging once a

month, no other interaction required). However, this

Figure 1: General structure of the proposed fall detection

algorithm.

position makes the fall detection problem more com-

plex to be solved. Indeed, differently with respect to

the head or to the waist (positions typically adopted

in the literature), which are in-built with the body of

the person and thus move by following the movement

of the body, the wrist is not in-built with the body, but

have instead some random movements (different with

respect to the ones of the body) that need to be taken

into account.

Another important contribution of this paper is

that both the data acquisition and the processing steps

are performed in real time directly on board of the

embedded device; however, the method has been de-

signed so as to avoid paying the limited resources of

the hardware with a decreasing in the accuracy. In-

deed, a ﬁrst stage (fall detection) is performed by a

multi-stage classiﬁcation using a One-Class Support

Vector Machine (OC-SVM) trained on the data that

have been collected during different normal running

sessions. Furthermore, a second classiﬁcation stage

(consciousness veriﬁcation) is performed so as to also

evaluate the temporal information and to effectively

understand the unconsciousness of a falling person.

The paper is organized as follows: in Section 2 the

details of the proposed approach are provided, while

in Section 3 some preliminary results have been re-

ported. Finally, some conclusions and future works

are drawn in Section 4.

2 THE PROPOSED SYSTEM

The proposed system has been designed so as to be

independent on other devices and comfortably weara-

ble on the wrist, combined with bracelet or a smart-

watch. Such a choice imposes several limitations on

the hardware resources available to perform detection

and classiﬁcation stages, so it is important to ﬁnd out

the best trade-off between the expected accuracy of

the system and the resources required to achieve it. In

Figure 1 we show the structure of the proposed sy-

stem.

We designed and realized an hardware prototype

equipped with a low power MCU Cortex-M4 and

two inertial sensors: a three-axes accelerometer and a

three-axes gyroscope. In order to deal with the com-

plexity of the task and to achieve good performance,

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

542

we propose a multi-stage fall detection system, wor-

king as follows: the ﬁrst stage, namely detection

stage, is based on a One-Class SVM (OC-SVM) and

is responsible to detect the abnormal patterns with

respect to the given normal model; the second stage

(consciousness veriﬁer), activated only after that the

detection stage ﬁres an alarm, aims to establish whet-

her the user is still moving after the detected falls or

if he can be considered as unconscious. The two sta-

ges are connected each other by an interstage ﬁlter,

aimed to eliminate spurious detections. Since they

have different goals, the two stages work on diffe-

rent features, computed on a sliding time window of

two second. The sampling rate considered is 100Hz,

so that each window contains 200 measures per each

of the six axes (3 from the accelerometer and 3 from

the gyroscope). Due to the nature of the problem, it

is important to note that an event of interest can be

contained in more than one window, thus we conside-

red overlapped windows so that two consecutive time

windows share a subset of their measures.

2.1 Fall Detector

The core of our system is represented by the fall de-

tection module. It is responsible to continuously ana-

lyze the feature vectors extracted form the sensors

and to detect if a fall occurs during the normal acti-

vity. As mentioned before, this task is performed by a

OC-SVM trained on the expected patterns of a normal

activity. In order to reduce the battery consumption,

the detection stage is activated each 250 milliseconds

(4Hz). Furthermore, in order to avoid losing impor-

tant patterns, successive sliding windows share the

87,5% of their measures. Another important consi-

deration which allows us to have an energy efﬁcient

system is related to the feature vector used by the

SVM. Indeed, we have reduced as much as possible

the number of features and at the same time we have

avoided the use of features that are expensive to com-

pute, such as spectral features. Therefore, according

to the current scientiﬁc literature, for each sensor, we

have computed the following features:

• Average value of the magnitude computed on the

vector resulting from the three axes.

• The maximum and the minimum value of each

axis.

• The distance between the maximum and the mini-

mum value of each axis.

• Speed of variation (SPV) in the time interval t

min

max

from the minimum value x

min

to the maxi-

mum value x

max

for each axis:

spv

max

− x

min

max

−t

min

(1)

• The maximum instantaneous variation of each

axis in two consecutive time instants k − 1 and k

(a.k.a. Slope):

slope

= max

k=1:N

|x(k) − x(k − 1)|, (2)

being N the number of samples in the window un-

der analysis.

• The Signal Magnitude Area (SMA) that is a mea-

sure of the magnitude of a single sensor over the

three axes x, y and z. It is deﬁned as:

sma =

(

∑

x(i) +

∑

y(i) +

∑

z(i)

)

(3)

Note that for brevity we have shown only one axis for

the SPV and the slope. Since different sensors have

different scales, after the collection we have ﬁrstly

standardized the features by using the Z-Score and

then normalized with respect to the norm of the vec-

tor.

One drawback of OC-SVM algorithm, compared

to simpler approaches like KNN, is that the selection

of parameters can be quite tricky and a wrong choice

could have serious effects on the overall ﬁnal perfor-

mance of the system. In our case, we have used an

RBF kernel, thus we have to select both the regulari-

zation factor ν and the kernel size γ. Firstly, in order to

get a lightweight classiﬁer that ﬁts the memory avai-

lable on the device, we have limited ν to have no more

than 300 support vectors in the model. The SVM pa-

rameter optimization has been performed via genetic

algorithm (GA-SVM). Typical GA-SVM approaches

use genetic evolution just to set the regularization and

the kernel size. Differently form the latter, we ex-

ploited the GA to obtain an optimal shaping of the

kernel. This shaping is achieved using ad-hoc fea-

ture scaling by assigning different weights to the each

feature before training. In this case, the prediction

will correspond to project the vector in feature space

with a non-spherical kernel. So that, a chromosome is

composed by the two parameters ν and γ followed by

one weight for each feature, while the ﬁtness function

is represented by the area under the Precision-Recall

curve. The choice of considering the Precision-Recall

curve instead of the ROC curve is due to the fact that

the a-priori distribution of the data sets is not balan-

ced. The number of negative samples in the dataset is

much larger than the number of positive ones. The-

refore, the false positive rate becomes an optimistic

index, while the precision will be much more suitable

to control the real performance of the classiﬁer.

A Wearable Embedded System for Detecting Accidents while Running

543

2.2 Interstage Filter

The interstage ﬁltering is an extension of the concept

of conﬁdence window. In real world, events evolve

with continuity; in other words, if a person is falling at

12:00:00.000, he will be still falling at 12:00:00.250

and at 12:00:00.500. According to this consideration

we can ﬁlter false alarms that appear as isolated ano-

malous classiﬁcations by taking the mean value of

last decisions. The effect of this mean operation is

that decisions will appear low pass ﬁltered and shif-

ted ahead in time, so that events are recognized with

delay. A more accurate way of performing this ﬁlte-

ring is using conﬁdence values instead of binary clas-

siﬁcation results: samples that are clearly anomalous

or clearly normal to the classiﬁer will have a larger

weight with respect to those that are borderline. We

use a conﬁdence window one second large (4 classiﬁ-

cation results) and consider anomalous only the ones

containing at least 50% of anomalous samples. In ot-

her words, we use a 50% of conﬁdence. We did not

use a stricter criterion in order not to affect recall: in-

deed, remaining false alarms will be ﬁltered by the

consciousness veriﬁcation stage.

2.3 Consciousness Veriﬁcation

The aim of the second stage is to verify if the user is

still conscious after a fall. If the ﬁrst stage algorithm

detects a fall, then the second stage will be in charge

of checking if the user is still lying on the ground after

a certain amount of time or if he got up to continue the

activity. This allows to distinguish serious accidents

that may lead to injuries from those with no conse-

quences, in which persons stand up just after the fall.

The second stage also allows to ﬁlter out false alarms

coming from the ﬁrst stage.

Note that, since the second stage algorithm will

ﬁlter out false alarms coming from the previous sta-

ges, we can tune the fall detector in order to achieve a

high recall (no real alarms lost) even at the expense of

precision; that is a more sensitive detection stage that

will hardly ignore a real fall but will produce some

false alarms, since the second stage is expected to ﬁl-

ter them out.

The consciousness veriﬁer uses its own features,

i.e. the average motion index (m − index). This mea-

sure is intended to summarize the quantity of motion

of the used perceived by the sensor and represents the

average acceleration sensed in a 1 second window be-

fore the current sample minus the module of gravity.

More formally:

m(kT

) =

∑

i=k−

a(iT

)| − |

g|| (4)

where |

g| = 1000mG is the estimated module of

the gravity vector,

a is the acceleration vector measu-

red at time t and T

is the sampling time.

It is easy to understand that such a measure can

be used to discern whether the user is moving or is

almost still using as only input the module of sen-

sed acceleration. The idea is that, when the user is

moving, some produced acceleration will sum to the

constant gravity module and, on average, will give a

considerably high value of the m − index; when the

user is not moving, on the other side, only gravity and

noise will ﬁgure in sensed acceleration value and, if

we subtract |

g| to |

a| we will get a value quite close

to zero, i.e. just the noise. For this stage, a window

of 6 seconds is considered after the fall. The user is

considered unconscious if the m − index index is lo-

wer than a given threshold (in our case 100 mG) for

more then the 50% of the window. An example of this

stage at work is shown in Figure 3.

3 EXPERIMENTS

In this section we are going to show the results

achieved by the proposed approach. We ﬁrst intro-

duce the protocol used in our experimentation (Sub-

section 3.1); then, in Subsection 3.2 the dataset used

for testing the proposed approach will be described

before presenting the results in Subsection 3.3.

3.1 Experimental Protocol

The performance of the proposed appriach has been

evaluated in terms of Precision (P) and Recall (R):

P =

T P

T P + FP

(5)

R =

T P

T P + FN

(6)

where TP, FP and FN represent, respectively, the

number of True-Positives (TP), False-Positives (FP)

and False-Negatives (FN).

Precision and recall are evaluated by analyzing

both the sample (at the window level, after the ﬁrst

stage) and the event. On the one hand, the analysis

on the samples gives a rough idea of the capacity of

the classiﬁer to give the correct answers. On the ot-

her hand, the analysis on the events gives an idea of

the user feeling about the usage of the application. In-

deed, in this last case a TP event is counted if at least

one positive sample (classiﬁed as fall) overlap with a

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

544

Figure 2: Example of sliding windows with 75% of overlap. Note that only in third and fourth windows the fall peak can be

seen entirely. If there was no overlap, only windows one and ﬁve would have been considered.

Figure 3: How the consciousness validator works: few seconds following the detected fall are evaluated to check that m −avg

is lower than a given threshold (the dashed line).

Table 1: MIVIA-Fall Dataset; the running times are repor-

ted in minutes, while the falls in terms of number of times.

Training Validation Testing

Running 76 mins 13 mins 13 mins

Falls - 17 21

ground truth fall. Viceversa, a FN is counted only if

there are not any positive samples associated to the

consecutive ones of a ground truth fall.

3.2 Dataset

In order to test the proposed approach, we acquired

a dataset (hereinafter MIVIA-Fall Dataset) that we

made publicly available for benchmarking purposes

The aim of our dataset is to capture real life situations.

Indeed, running data comes from real running sessi-

ons, while falls have been simulated on a soft mattress

for safety reasons. The subject runs through an aisle,

then stumbles and falls on the mattress as naturally as

possible; that means that it falls frontally putting its

hands forward. Note that we decided to capture real

life data so as to ensure that the ﬁnal real prototype

performance would be similar to the one achieved by

the testing algorithm on the recorded data.

Some details of the MIVIA-Fall Dataset have been

reported in Table 1. The device, whose sensor’s featu-

The dataset will be made publicly available on our web-

site at the following link http://mivia.unisa.it after the publi-

cation of the paper.

Figure 4: Data axes position and orientation with respect to

the subject arm in the MIVIA-Fall Dataset.

res are reported in Table 2 (in terms of range and sen-

sitivity), has been placed on the wrist of the runner,

with the sensors mounted so as to acquire the data as

shown in Figure 4. An example of the acquired data

is plotted in Figure 5.

Table 2: MIVIA-Fall Dataset: main characteristics of the

sensing equipment. Note that the sensitivity is expressed in

LSB/unit. It means, for example, that a measure of 1G will

give a raw measure of 2048, and then the minimum change

in gravity that one will be able to appreciate will be 1/2048.

Range Sensitivity

Gyroscope ±2000

deg

secs

16.4

LSB

deg/secs

Accelerometer ±16G 2048

LSB

3.3 Results

The choice of the parameters to be used in our expe-

rimentation has been performed by optimizing the re-

A Wearable Embedded System for Detecting Accidents while Running

545

Figure 5: An example of the data in the MIVIA-Fall Dataset.

Table 3: Performance achieved on the test set. S refers to the fact that Precision and Recall are computed on the samples,

while E refers to the fact that Recall is computed on the Events. False alarm rate is computed in terms of number of false

positives (FP) for each minute.

Precision (S) Recall (S) False alarm rate Recall (E)

Fall detector 58.7% 89.8% 1.26 FP/min 100%

Interstage ﬁlter 69.3% 89.8% 0.58 FP/min 100%

Consciousness veriﬁcation 100.0% 89.8% 0 FP/min 100%

sults on the validation set. The Precision-Recall curve

for the ﬁrst stage is reported in Figure 6, where the

chosen operating point is reported in red. The choice

has been guided by the following two points: ﬁrst, in

the particular application at hand, as mentioned in the

previous section, it is required to have an high recall,

even at the expense of precision. It depends on the

fact that the second classiﬁcation stage is able to ﬁl-

ter out some potential false alarms, but it could be not

able to recover missed event at ﬁrst stage. Thus, it

becomes important at the ﬁrst stage to maximize the

recall, while postponing at the successive stage the

increasing of the precision. Furthermore, we need a

point that yields an appreciable stability: points on

a vertical edge of the curve indeed may represent a

risky choice, since they may achieve rapidly descen-

ding values of precision at a ﬁxed recall. It is worth to

remember that this is a curve estimated on the valida-

tion set and that the points could move toward worse

performance when evaluated on the test set.

The performance achieved on the test set is repor-

ted in Table 3. As we can see from the table, the in-

troduction of the interstage ﬁlter helps improving the

performance of the proposed approach, both in terms

of precision (ﬁxed the recall to 89.8%, the precision

is improved from 58.7% to 69.3%) and of false alarm

rate (from 1.26% to 0.58%). However, the further in-

creasing is due to the introduction of the conscious-

ness veriﬁcation stage. Indeed, on the test set no false

alarms have been detected at all, and the precision is

100%. The results obtained by the proposed approach

are thus very encouraging, opening to the possibility

to use such system in real environments.

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

546

Figure 6: Precision-Recall curve obtained on the validation

set at the ﬁrst stage and used for the operating point se-

lection. Red point identiﬁes the selected operating point.

4 CONCLUSION

In this paper we have proposed a method for detecting

falls while sporting, with particular reference to the

running. The method is optimized so as to run di-

rectly on board of a wearable embedded device, wit-

hout any additional external server in charge of the

elaboration. The experimental results, conducted over

a dataset made publicly available for benchmarking

purposes, conﬁrm the effectiveness of the proposed

approach, where the possibility of running on embed-

ded devices is not payed in terms of accuracy.

Although the method has been though for de-

tecting falls while running, its architecture is general

enough to also deal with other sports. In the future,

we plan to extend the proposed approach so as to deal

with other typologies of sports. Future works also in-

clude an extension of the dataset and then of the expe-

rimentation, so as to conﬁrm the effectiveness of the

proposed approach.

ACKNOWLEDGEMENTS

This research has been partially supported by

A.I.Tech s.r.l. (www.aitech.vision).

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