A Visual Computing Approach for Estimating

the Motility Index in the Frail Elder

Chiara Martini

, Nicoletta Noceti

, Manuela Chessa

, Annalisa Barla

, Alberto Cella

Gian Andrea Rollandi

, Alberto Pilotto

, Alessandro Verri

and Francesca Odone

Department of Informatics, Bioengineering, Robotics and System Engineering,

Universit

a degli Studi di Genova, Genova, Italy

E.O. Ospedali Galliera, Genova, Italy

Keywords:

Visual Computing, Skeleton Data, Motility Index, Frailty, Aging.

Abstract:

The accurate estimation of frailty is an important objective to assess the overall well-being and to predict

the risk of mortality of elderly. Such evaluation is commonly based on subjective quantities both from self-

reported outcomes and occasional physicians evaluations, leading to possibly biased results. An objective and

continuous frailty screening tool may be more appropriate for routine assessment. In this paper, we present a

data driven method to evaluate one of the main aspect contributing to the frailty estimation, i.e. the motility

of the subject. To this aim, we deﬁne a motility index, estimated following a visual computing approach

analysing streams of RGB-D data. We provide an extensive experimental assessment performed on two sets

of data acquired in a sensorised facility located within a local hospital. The results are in good agreement with

the assessment manually performed by the physicians, nicely showing the potential of our approach.

1 INTRODUCTION

According to the World Bank, Italy has the second-

highest share of population aged over 65 worldwide,

i.e., 22% in 2014, and statistics related to G20 coun-

tries are becoming increasingly similar. Our region, is

among the highest in this ranking worldwide. Aging

causes, in general, the reduction of the individual’s

potential, leading to a state of vulnerability and in-

stability of the clinical condition. To highlight this

condition, recent medical literature has proposed the

notion of frail elderly, an individual with an eleva-

ted risk of complications that may result in loss of

functional autonomy or death (Fried et al., 2004). The

accurate estimation of the frailty of an elderly is there-

fore an important objective to assess the overall well-

being and to predict the risk of mortality (Pilotto et al.,

2008) (Angleman et al., 2015). Moreover, an hospital

stay, especially if prolonged, can lead an elderly per-

son to develop new disabilities (Volpato et al., 2007)

and dramatically worsen the risk of mortality (Volpato

et al., 2016). Therefore, it is of particular clinical in-

terest to correctly quantify the frailty of the patient

just before being discharged. One of the most com-

monly accepted operational deﬁnition of frailty is the

classiﬁcation proposed by Fried et al. (Fried et al.,

2001). In this study the authors deﬁne frailty as a cli-

nical syndrome in which three or more of the follo-

wing criteria are present: unintentional weight loss,

exhaustion, decrease grip strength, slow gait speed,

low physical activity. The criteria of weight loss,

exhaustion, and grip strength are usually self-reported

measures and may be prone to bias. An objective frai-

lty screening tool may be more appropriate for routine

assessment.

Another universally shared strategy for the estima-

tion of patients frailty index and related risk of morta-

lity is the Multidimensional Prognostic Index (MPI)

score (Pilotto et al., 2008), that is based on the eva-

luation of the clinical, cognitive, functional, nutritio-

nal, and social domains, as deﬁned in the International

Classiﬁcation of Functioning, Disability, and Health

The evaluation is mostly carried out through question-

naires and self-reported outcomes.

Recently, with the advent of the assistive

technologies, various approaches for the automa-

tic estimation of frailty have been proposed (Cao

et al., 2009),(Zouba et al., 2010),(Liu and Liu,

http://apps.who.int/classiﬁcations/icfbrowser/

Martini, C., Noceti, N., Chessa, M., Barla, A., Cella, A., Rollandi, G., Pilotto, A., Verri, A. and Odone, F.

A Visual Computing Approach for Estimating the Motility Index in the Frail Elder.

DOI: 10.5220/0006602004390445

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP, pages

439-445

ISBN: 978-989-758-290-5

439

2010),(Bathrinarayanan et al., 2013).

In this paper, we present a data driven method to

evaluate the motility index, one of the main aspect

contributing to the frailty estimation, with visual fe-

atures.

We aim to perform a continuous motility asses-

sment of frail persons and to produce reports infor-

ming medical staff in case of medical assistance is

required. We target elderly and people with mild cog-

nitive impairments, partially autonomous, but in need

of a light assistance, possibly in a post-hospitalisation

stage. This study is a part of a larger project whose

aim is to design and implement a model of protected

discharge, in which the patient, after being discharged

from the hospital, is hosted for few days (about one

week) in an apartment. This is novel with respect to

the state of the art, since current literature is based on

long observation of the patient (6-12 month)(Scanaill

et al., 2006), our new challenge is instead to infer the

frailty of the patient in a short time. The facility is lo-

cated within the Galliera Hospital a local hospital in

Genova (Italy) and equipped as a comfortable apart-

ment, where the patient can be monitored by a system

of sensors, while physicians and nurses have the op-

portunity of monitoring the patient remotely. We re-

port an extensive analysis on two sets of data, acqui-

red within the facility. The obtained estimates are in

line with the geriatricians assessment, even if the two

evaluations have been carried out in a different way

and consider different aspects of the overall health

status. The structure of the paper can be summarised

as follows. Firstly, we describe the facility and our

research objectives (Sec. 2). Then we present data

analysis and the obtained results (Sec. 3). The paper

is closed with conclusion and future works (Sec. 4).

2 CONCEPT

In this section we ﬁrst describe the apartment, then

we summarise the sensors installed in the facility and

the corresponding measurements. The experimental

set up is rich of sensors, we will brieﬂy illustrate all

the devices but, for the purpose of the study, we will

use only a subset of them.

The aim of our project is the continuous monitoring

of the patient’s motility. In particular, we focus on the

automatic estimation of the motility index (see Sec.

3.3) based on walking time and physical activity that,

according to (Fried et al., 2001), are strongly related

to the patients frailty.

As shown in Figure 1 and Figure 2, the apart-

ment consists of two bedrooms, one with a bed and a

sofa-bed (for an accompanying person) and one with

Figure 1: Apartment plan. Blue rectangles represent the

RGB-D sensors, the small red squares correspond to the ca-

meras. Green circles represent the localisation tags, while

purple rectangles indicate the Passive Infra-red (PIR) sen-

sors and their ﬁeld of view (through purple lines). They are

all wired to the workstation (green square), placed in the

gym.

Figure 2: Left panel shows one of the two bedrooms, right

panel shows the common room.

two beds (patient and caregiver), a gym, and a com-

mon room with kitchenette and living room. To make

the atmosphere homely, an architectural study was

conducted to choose appropriate colors, arrangements

and furniture, leading to an environment similar to a

regular apartment rather than a hospital room.

The common room, where most of the daily acti-

vities take place, hosts the majority of the sensors,

including cameras and RGB-D sensors, localisation

anchors, presence sensors, occupancy sensors for the

chairs (pressure sensors), usage sensors on some cabi-

net door of the kitchenette (luminosity sensors). He-

alth monitoring devices are located on a console table

in the same area. Bedrooms and bathrooms, for ob-

vious privacy concerns, are only equipped with pre-

sence sensors, which detect whether there is any mo-

vement in the room.

Lastly, presence sensors have also been placed for

monitoring speciﬁc meaningful disjointed locations,

such as: the kitchen table, the desk, the bed and the

shower. Similarly, an additional luminosity sensors

has been installed to monitor the status of the TV set.

The redundancy of sensors and measures to moni-

tor similar activities is a design choice that guarantee

the robustness of the results.

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

440

2.1 Distributed Sensors and Health

Devices

For the sake of completeness we ﬁrst describe all the

non-vision sensors installed in the apartment.

The localisation system, Eliko KIO RTLS

, is a

Real Time Locating System (RTLS) based on the Ul-

tra WideBand (UWB) technology, which allows for a

positioning precision of about 30cm. The system is

based on the “tag and anchor” paradigm, which assu-

mes the tag to be always attached to the person and

the anchors to be in ﬁxed, a-priori known locations in

the environment (green dots in Figure 1). The system

allows for a continuous and unambiguous tracking of

the monitored person.

The presence sensors, Aeotek MultiSensor 6

, are

devices integrating six channels,incuding the Passive

Infra-Red (PIR) and the light sensors. They have been

placed in different locations (in purple in Figure 1)

and calibrated in such a way to monitor disjointed lo-

cations.

Chair occupancy sensors, SparkFun Force Sensi-

tive Resistor, detect whether there is a load or not on

the chair by monitoring the pressure level measured

below its legs.

For gesture recognition purpose, we endow the pa-

tient with a LG G Watch R5 equipped with a triaxial

accelerometer.

Health Monitoring sensors allow to acquire a mi-

nimal set of vital parameters including: weight, blood

pressure, heart rate, Oxygen saturation SpO2 level,

glucose. To this aim, we identiﬁed a set of wearable

and non-invasive devices, selected to guarantee the

patient complete freedom of movement (no cables,

data are transmitted via wireless communication). All

devices are provided by iHealth Labs

2.2 Vision Sensors

Figure 3 shows the arrangement of visual sensors in

the living room of the apartment, highlighting their

ﬁelds of view and overlaps. The RGB-D sensors are

Asus Xtion Pro, acquiring a depth stream with VGA

resolution (640 × 480 pixels, at 30 fps). They co-

ver a ﬁeld of view of about 58deg horizontal, 45 deg

vertical and 70 deg diagonal, with a range of opera-

tion between 0.8m and 3.5m. The ﬁrst RGB-D sensor

(RGBD

) is located over the kitchen’s sink. Its Field

Of View (FOV) is highlighted in blue in Figure3,

right, and it covers all the kitchen and table area, i.e.

http://www.eliko.ee/products/kio-rtls/

http://aeotec.com/z-wave-sensor

https://ihealthlabs.com/

Figure 3: A sketch of the visual sensors ﬁelds of view and

their overlap. Left panel shows the ﬁeld of view of the two

cameras, while right panel shows the ﬁeld of view of the

two RGB-D sensors.

where patients are supposed to have breakfast, lunch

and dinner. The second one (RGBD

) is located near

the TV in front of the sofa, its ﬁeld of view is high-

lighted in red in Figure 3 right, and it covers the li-

ving room, i.e. the sofa, the armchair, the library,

and the area of the vital monitoring devices. The ca-

meras, henceforth referred to as CAM

and CAM

are high resolution mini-dome IP cameras acquiring

1920 ×1080 pixels frames at 25 fps. They are located

in the two opposite corners of the room, indicated in

green in Figure 3 left. The mutual position of RGB-D

sensors and cameras is intended to provide a partial

overlap of the ﬁelds of view while covering comple-

mentary areas.

3 VISUAL DATA ANALYSIS AND

RESULTS

Figure 4 shows the pipeline of our monitoring sy-

stem, from the acquisition and processing stages, to

the computation of the motility quantities, and to the

estimation of the motility index. Finally, all evaluated

motility quantities and associated statistics are made

available to physicians on a daily report. In the re-

mainder of this section we introduce the dataset, the

motion analysis pipeline and discuss the results.

3.1 Dataset

The dataset we consider in our experimental analysis

is composed of two batches of data acquired with the

help of 10 volunteers.

The ﬁrst batch is used for validating the system,

the other to assess its performance. All subjects had

not constraints in the apartment, and spontaneously

performed common daily-life activities.

The ﬁrst batch includes 5 young volunteers (3

male and 2 female, mean age 27±4) who spent at le-

ast 3 days in the facility, alone or in pairs, for a total

of about 123 hours of data. The collected data include

A Visual Computing Approach for Estimating the Motility Index in the Frail Elder

441

Figure 4: A visual sketch of the pipeline of our system. Vi-

deo acquisition is triggered by the RGB-D depth sensors

which detect the presence of individuals in the common

room. Acquired data are stored online in a Data Base that

makes data available for ofﬂine analysis.

simpler and more complex scenarios (single or mul-

tiple persons in the apartment), and they represent a

suitable test-bed for the evaluation of our algorithms.

Our current analysis considers three activities types:

walking, sitting, standing. To provide a quantitative

evaluation of the proposed methods, we selected 5 se-

quences from the data set in which a single subject is

observed, and we carefully annotated them by exploi-

ting the available video sequences. A coarser annota-

tion is provided also for the rest of the data, allowing

us to present a broader quantitative analysis.

The experimental results comparing the geriatric

assessment and the automatic analysis are presented

on data acquired from 5 healthy elder subjects (3 male

and 2 female, mean age 72.4±5.2) who spent at le-

ast two hours each inside the facility alone. During

their stay clinical test were performed by physicians

and data were collected and manually analysed. This

ground truth that incorporates both the geriatric asses-

sment and the manual annotation of the data is sum-

marised in Table 1.

3.2 Localisation

The ﬁrst task we need to address, prior higher level

analysis, is localisation. The goal of localisation is

to determine, at each time instant, the position of a

person in the apartment. For this task we used the

information coming from the RGB-D depth sensors

providing (X,Y, Z) coordinates of the body joints.

Figure 5 provides an overall visual impression of

the localisation obtained by RGB-D sensors installed

in the common room, considering measures obtained

on a temporal span of 30 minutes. The ﬁgure clearly

shows the complexity of the trajectories collected in

Figure 5: A visual impression of the trajectories collected

in the environment. A volunteer was asked to perform nor-

mal daily activities for 30 minutes (points are color-coded

according to the acquisition sensor).

Figure 6: Estimation of the velocity of a person moving at

different speeds.

the environment by the RGB-D sensors. The maps

are computed automatically and incorporate informa-

tion from the two different sensors, one of which is

considered as a reference frame (blue dots), while the

other is related to the reference frame through a rigid

roto-translation transformation which is learnt from

data (red dots). As expected (see also Figure 3 right),

there are a few blind spots. This issue will be easily

overcome by integrating data from the cameras.

Notice how from a simple analysis of these data it is

immediate to identify regions of the common room

where the volunteers spend most of the time, e.g. sit-

ting at the bottom-right corner of the table, at the rig-

htmost part of the sofa, or standing at the kitchen.

3.3 Motility Analysis

In this section we aim at automatically analyse pa-

tient’s motility and postural transfers.

We ﬁrst consider a low-level motion feature, i.e.,

an estimate of instantaneous velocity which we derive

directly from localisation measurements. In Figure

6 we provide an example of a velocity estimation in

which a subject has been asked to walk at different

speeds. As it can be noticed, the different dynamics

are nicely estimated.

At a higher level, we compute the following mo-

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

442

Table 1: Geriatric evaluation of 5 elder subjects in good health: summary of the geriatricians assessment, in terms of MPI

(Multidimensional Prognostic Index), gait speed measured during test by physicians in a 4 meters walk. The last three columns

report a manual annotation carried out by the authors on the percentage of time spent standing still, sitting or walking.

ID Gender Age MPI Measured Gait Speed [m/s] % STILL % SIT % MOVING

#v1 M 68 0.063 1.299 20 54 26

#v2 M 71 0.063 1.026 15 71 14

#v3 M 66 0.125 1.556 26 50 24

#v4 F 79 0.125 0.875 19 62 19

#v5 F 78 0.188 1.084 4 89 7

Table 2: A summary of the quantitative analysis on the motility quantities we performed on 5 annotated sequences. When

appropriate, we report the ground truth value between brackets. The ﬁnal column reports the estimates of the motility index.

Seq. Age Time T

sit

[s] T S[s] T M[s] T R

2st

T R

2sit

W S MI

[min]

#u1 23 90 496 (478) 74 (79) 283 (296) 10 (8) 8 (8) 6 (5) 13 (14) 0.65

#u2 22 150 5239 (5260) 752 (755) 1189 (1165) 20 (23) 21 (23) 37 (38) 57 (48) 0.83

#u3 24 120 202 (224) 164 (174) 213 (181) 5 (5) 5 (5) 11 (13) 10 (12) 0.60

#u4 36 30 128 (126) 84 (79) 377 (384) 9 (10) 8 (10) 9 (9) 18 (14) 0.34

#u5 40 30 92 (99) 81 (77) 167 (196) 3 (3) 3 (3) 7 (7) 7 (7) 0.46

tility quantities, identiﬁed with the help of geriatrici-

ans:

- Number of postural changes, i.e. from sitting

to standing (T R

2st

) and vice-versa (T R

2sit

): this

is done by looking at the variation in heights of

the detected skeletons (through RGB-Ds) in the

scene;

- The total time spent moving (T M), standing still

(T S), and sitting (T

sit

): this is done by checking

the variation in the distribution of the velocity mo-

dulus;

- Number of instances of walk (W ) – i.e. how many

times, in a given observation period, people start

walking – and stop (S) events;

- Longest walk distance;

- Longest walk time.

Such quantities are empirically estimated accor-

ding to (Chessa et al., 2017) analysing the instanta-

neous measures or series of temporally adjacent ob-

servations. More speciﬁcally we follow an approach

based on thresholding the y coordinate of the skeleton

representation and the velocity (see Figure 6). Then,

some of them are used to compute the motility index

which we see as a ﬁrst quantitative continuous contri-

bution to the frailty index.

We formalise the estimation of the motility index

MI on the time period T as follows

MI(T ) = (1 − α)



sit

+ T S

T T



+ α





1 −

T R

2sit

+ T R

2st

+W + S

T T



where the ﬁrst term quantiﬁes the percentage of

inactivity time, while the second determines the

relative amount of postural and dynamic transitions

with respect to the entire time period (T T ). The

parameter α is a value to be chosen to weight the

importance of the two terms of the equation, while

C is a factor to make the second term numerically

comparable with the ﬁrst one. The motility index

takes values between 0 and 1, approaching 1 when

the motility of the subject is not satisfactory.

A coarse quantitative analysis carried out on all

the sequences of the young volunteers (for which we

have a partial annotation available) shows an accu-

racy in estimating the overall moving time of ∼ 95%.

Users are correctly associated with a sitting state with

an accuracy of ∼99%, and the percentage of correctly

detected sit-to-stand transitions is ∼ 79%. Table 2

reports a more detailed experimental analysis perfor-

med on the 5 fully annotated sequences of young vo-

lunteers. All the above mentioned measurements have

been assessed, and the estimate of the overall MI is

reported. The latter can not be associated with an ob-

jective ground truth, but we can comment on the ap-

propriateness of the estimate with respect to a diary of

activities maintained by the volunteers. For instance,

in sequence #2 the volunteer spent most of the time

sitting (about the 83% of the total time of observation)

and this corresponds to a high value of MI. Conver-

sely, the dynamism of subject for sequence #4 is ri-

cher (the volunteer spent about the 64% of the total

time walking around the apartment), thus the motility

index is much lower.

Table 3 reports an analysis on the set of data acqui-

red with elderly healthy patients. We ﬁrst observe

A Visual Computing Approach for Estimating the Motility Index in the Frail Elder

443

Table 3: Automatic evaluation of 5 elder subjects in good health: the observation time, the estimated percentage of time spent

standing still, sitting, or walking, the estimated average speed, and the overall estimated motility index (MI).

ID Time [min] % STILL % SIT % MOVING Avg. Velocity MI

#v1 150 22 52 26 0.39±0.21 0.52

#v2 90 12 70 18 0.27±0.19 0.62

#v3 90 24 51 25 0.31±0.21 0.70

#v4 30 19 61 20 0.19±0.12 0.82

#v5 120 9 86 5 0.26±0.18 0.91

how our estimate, in percentage, of the amount of

time spent by patients in standing, sitting, moving

state is very coherent with the manual annotation per-

formed in Table 1. We can also notice that our estima-

ted average speed, albeit difﬁcult to compare with the

speed estimated by geriatricians in a single walk, pro-

duces the same relative ordering among volunteers.

Lastly, the motility index produces a result which is

very much in line with the reported MPI: in particu-

lar, the healthier volunteer is #v1, the weaker is #v5.

From this analysis it appears that the MI could effecti-

vely complement and enrich the MPI estimation.

Finally, Figures 7 and 8 report for each volunteer

the details on some of the estimated motility quanti-

ties carried out on the elderly volunteer data. Figure 7

shows the average number of state transitions (T R

2sit

from stand to sit and T R

2st

from sit to stand) and the

number of walk instances in the observed time span.

Figure 8 reports for each subject the longest walk dis-

tance and the longest walk time: here in particular

we notice how #v1 walks faster and spans longer dis-

tances than other subjects, conﬁrming the conclusions

obtained from the motility index.

4 CONCLUSION AND FUTURE

WORKS

In this paper we presented a visual computing appro-

ach to estimate frailty in elderly based on the evalua-

tion of the motility index. The experimental setup is a

protected discharge facility which has been planned,

implemented, and validated within the Galliera Hos-

pital. After being discharged from the hospital, the

patients are hosted in the apartment for a few days.

Here, a system, based on vision sensors, continuously

assesses patients’ motility index while physicians and

nurses have the opportunity of monitoring them re-

motely.

The system was validated on 5 volunteers and tes-

ted on 5 healthy elder subjects. The results are very

encouraging, as they show correlation between the au-

tomatic motility evaluation and the corresponding cli-

nical analysis performed by the physicians. Hence,

Figure 7: Number of state transitions evaluated as the

average of T R

2sit

and T R

2st

and walk instances W for each

volunteer (#v1 to #v5).

Figure 8: Longest walk distance and walk time for each

volunteer (#v1 to #v5).

the motility index may be used as a valid integration

to the frailty index, with the advantage of a continu-

ous, automatic and objective assessment.

In the next future we aim at adding further dimen-

sions to the assessment of frailty, by integrating data

coming from all different devices installed in the fa-

cility. The investigation of other physical and cog-

nitive domains will allow the evaluation of frailty in

the widely used context of the multidimensional as-

sessment according to the International Classiﬁcation

of Functioning, Disability and Health.

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

444

ACKNOWLEDGEMENTS

The authors are indebted to the Associazione Volon-

tari Ospedalieri (A.V.O) association for their precious

help in the data collection process.

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