Perception of Human Activities

A Means to Support Connectedness Between the Elderly and Their Caregivers

Kadian Davis

, Evans Owusu

, Carlo Regazzoni

, Lucio Marcenaro

, Loe Feijs

and Jun Hu

Department of Industrial Design, Eindhoven University of Technology, Den Dolech 2, 5612 AZ, Eindhoven,

The Netherlands

Independent Researcher, Eindhoven, The Netherlands

Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department, University of Genoa,

Via Opera Pia 11a, 16145, Genova, Italy

Keywords:

Activity Recognition, Monitoring, Sensors, Smart-phone, Perception, Connectedness.

Abstract:

This position paper describes a smart-phone based activity recognition system for improving social connect-

edness between caregivers and their elderly relatives. Sensing technologies can enable real-time monitoring

to provide activity recognition in order to support health and safety among the elderly who are living indepen-

dently. However, most existing activity recognition systems are focused on using sensors for unidirectional

monitoring of emergency cases in particular, fall detection. Motivated by the desire to utilize bidirectional

activity recognition to improve connectedness between an ageing population and their caregivers, we describe

our planned approach to investigate how the perception of a caregiver’s activities by a senior citizen and

vice versa can aid in improving social connectedness. To investigate this perception, activity states will be

transformed into an information visualization into the caregiver’s home and vice versa without overt commu-

nication from participants. Findings are expected to provide further insight on the extent to which perception

of human activities increase social connectedness.

1 INTRODUCTION

With the growth of demographic ageing within the

European Union, formal and informal care services

are increasingly becoming concerned about the ﬁscal

burden and demands of the ageing population. Conse-

quently, these institutions are in favour of home care

solutions. As discussed in Davis et al. (2015), there

are inadequate resources such as nurses, which induce

cost; and consequently independent living is forced.

In contrast, despite wanting to live ‘separate’ lives

from their elderly counterparts, caregivers often ex-

perience worry or uncertainty about their elderly rel-

ative’s health and well-being.

For many years, a number of scholars have as-

sociated social isolation and loneliness with old age

(Sheldon et al., 1948; Halmos, 2013). In general,

retirement, mobility-impairment, increased isolation,

death of loved ones and kin-separation due to glob-

alization may cause loneliness in ageing societies.

Furthermore, research has found a number of health

risks associated with loneliness and social isolation

including high mortality risks, cardiovascular and in-

fectious diseases, cognitive deterioration and depres-

sion (Becker et al., 1998; Stafford et al., 2011; Steptoe

et al., 2013). However, epidemiological research sug-

gests that strong social ties play a critical role in en-

hancing the elderly’s psychological and physiological

well-being (Umberson and Montez, 2010).

Social participation has been incorporated into the

research and public policies of ageing societies as

shown in (Landabaso and Letter, 2013). As a result,

this enables social cohesion through active involve-

ment in volunteering services, clubs and associations.

In turn, this increases a sense of belongingness and

also inﬂuences quality of life, which is critical for the

sustainable development of ageing societies.

Human activity recognition (HAR) systems can

help in reducing the challenges of supporting el-

derly independent living and reducing the burden of

their caregivers. Moreover, HAR systems provide

a plethora of opportunities for automatic recognition

of activities of daily living (ALDs) in the context of

health and elderly care such as fall detection (Bourke

et al., 2007), classifying activities of Parkinson’s dis-

ease patients (Rodriguez-Martin et al., 2013) and en-

couraging physical activity (Consolvo et al., 2008).

However, most of the existing applications are fo-

194

Davis K., Owusu E., Regazzoni C., Marcenaro L., Feijs L. and Hu J..

Perception of Human Activities - A Means to Support Connectedness Between the Elderly and Their Caregivers.

DOI: 10.5220/0005490601940199

In Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AgeingWell-

2015), pages 194-199

ISBN: 978-989-758-102-1

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

cused on detecting potentially dangerous situations

and generating automatic alarms in the case of an

emergency (Rashidi and Mihailidis, 2013).

1.1 Research Goals

Social Connectedness can be described as a sense

of belongingness based on the appraisal of having

enough close contacts (Van Bel et al., 2008). In this

position paper, we hope to improve social connectiv-

ity through the perception of activity states generated

from smart-phone sensors. This work forms part of

the proposed experiment of Davis et al. (2015), which

aims to induce social presence through subtle aware-

ness of activity and emotional states between the care-

givers and their elderly relatives.

The ubiquitous cell-phone provides a rich plat-

form for human activity recognition as shown in (An-

guita et al., 2012). The inclusion of sensors namely

the camera, accelerometer and gyroscope, its unobtru-

siveness and communication features (WIFI, 3G and

Bluetooth) add to the smart-phone’s appeal for en-

abling human activity recognition (Ustev et al., 2013).

For the purposes of this research, we hope to apply the

notion of abstraction (i.e. highlighting key informa-

tion contained in the data and suppressing irrelevant

details as discussed in (Cavaco, 2014) to investigate

how the elderly and their caregivers will perceive raw

activity data versus classiﬁed data.

2 RELATED WORK

In perceiving human activity, we rely on several in-

formation sources from various modalities including

sensory, motor and affective processes (Blake and

Shiffrar, 2007). Speciﬁcally, the human visual sys-

tem seemingly plays a signiﬁcant role in understand-

ing human actions and intentions. Furthermore, it is

extremely sensitive to human movement and as such

it is able to extract socially relevant details (Troje,

2002). This implies that human motion could pro-

vide reliable information for discerning affect (Blake

and Shiffrar, 2007). For instance, Kaye et al. (2005)

conﬁrmed that virtual intimate objects gave a sense

of peripheral presence and activity awareness; thus

enhancing intimacy in long distance relationships.

Therefore, we believe that the perception of human

activities can improve social connectedness. For

instance, imagine you have an information display

showing your distant elderly parent’s activities in the

periphery of your living room, which is placed in

your living room and you suddenly discern that some-

thing is wrong and you are prompted to call your

mother. Could you recognize speciﬁc patterns in the

daily activities of your mother? Could you recognize

that something was wrong based on the dynamics of

the visualization in the absence of direct communi-

cation? Would this improve social connectedness be-

tween yourself and your elderly parent? We believe

that this is possible and in this section we will explore

a number of studies focused on awareness systems for

elderly care.

2.1 Awareness Systems Supporting

Elderly Care

Naturally, awareness of others and their activities can

directly inﬂuence social connectedness by addressing

social and emotional needs and possibly initiate com-

munication through other media (Markopoulos and

Mackay, 2009; Romero et al., 2007). Early concep-

tions of awareness systems to support ageing in place

were introduced through the Digital Family Portrait

(Mynatt et al., 2001) and the CareNetDisplay (Con-

solvo et al., 2004). The Digital Family Portrait used

sensors to collect information (eg., weather and ac-

tivities) to display a qualitative sense of a senior citi-

zen’s daily activities to their families over a mediated

environment. The CareNetDisplay was an extension

of the previous ideas of the Digital Family Portrait

and consequently; the researchers ampliﬁed an older

person’s photograph with details about their daily life

including activities, moods, medication, falls, meals

and outings (Consolvo et al., 2004). Later, the Daily

Activities Diarist sought to address the weaknesses of

the previous awareness systems through its narrative

presentation of awareness information (Metaxas et al.,

2007). These earlier systems demonstrated great po-

tential for facilitating awareness for distant family

members who were worried about their elderly rel-

atives or members of an elderly care network offer-

ing day-to-day care. However, like the Digital Family

Portrait and CareNetDisplay, the Diarist reﬂected an

unbalanced communication channel and in most cases

portrayed unidirectional monitoring where the care-

giver observed their elderly relatives for notiﬁcation

of alarming situations. Unidirectional monitoring in-

volves the deployment of wearable devices and other

intrusive sensors all around a senior’s home. This

is invasive and consequently violates the privacy and

dignity rights of the elderly population.

More recent communication mediated systems

that support peripheral awareness for connecting re-

mote families include the following: the ASTRA -

awareness system for connecting households and mo-

bile family members Romero et al. (2007) and SoP-

resent - an awareness system for connecting remote

PerceptionofHumanActivities-AMeanstoSupportConnectednessBetweentheElderlyandTheirCaregivers

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households Dadlani et al. (2014). In sum, both Astra

and SoPresent attempted to address the asymmetrical

communication concerns of the previously discussed

research by sharing moments and experiences. How-

ever, they were designed to support connectivity be-

tween families of remote households, which though

related is not the speciﬁc context of this research. In

our research, we are interested in understanding how

the elderly and their caregivers will perceive the ac-

tivity data presented in the periphery and how this ac-

quired knowledge improves social connectivity.

3 PROPOSED ACTIVITY

RECOGNITION PLAN

We have chosen an Android based platform as a

means for activity recognition because of its em-

bedded sensors such as the accelerometer and gy-

roscope. In addition, Android’s openness provides

existing resources (source modiﬁcations and open

tools), which facilitate the inexpensive development

of context-awareness tools. Therefore, we have devel-

oped the Accelerometer Gyrometer Logger (available

on Google Play) to collect accelerometric and gyro-

scopic sensor readings at a frequency of 50Hz. Spe-

ciﬁc properties will be extracted (eg. mean, standard

deviation, entropy etc.) from this data to detect the

following activities: walking, standing, laying, sit-

ting, walking upstairs and downstairs, running and cy-

cling for both the elderly and their caregivers.

Several studies have shown that the Support Vec-

tor machine (SVM) is the most popular classiﬁcation

method in comparison to the quadratic classiﬁer, k-

nearest neighbor algorithm and artiﬁcial neural net-

works. For instance, Ravi et al. (2005) demonstrated

that SVM gives one of the best accuracies for rec-

ognizing human activities achieving over 99.4% ac-

curacy for boosted SVM. Moreover, Anguita et al.

(2012) adapted the standard SVM to exploit ﬁxed-

point arithmetic to reduce computational complex-

ity. This approach enabled them to deploy the smart-

phone inbuilt accelerometer and gyrometer sensors

while maintaining normal battery lifespans for other

shared resources on the phone. The adapted multi-

class SVM obtained similar accuracy compared with

the traditional SVM. In this position paper, we pro-

pose to adopt the multi-class SVM algorithm inspired

by Anguita et al. (2012) to train and classify our data

for recognizing the activities we have mentioned ear-

lier.

3.1 Data Collection

Anguita et al. (2012) have made publicly available,

a HAR training dataset (accelerometer and gyrome-

ter) of 30 volunteers in the age range of 19-48 years.

In their experiment, each volunteer was instructed

to perform the following activities: sitting, standing,

walking, lying, walking upstairs and walking down-

stairs while wearing a smart-phone on their waist.

The experiments were video recorded to enable them

to label the data. We will adopt a similar approach

to collect additional datasets to include activities such

as running and cycling. Our dataset together with the

existing dataset will be used to train the multi-class

SVM classiﬁer for activity recognition. However, our

study will include a larger sample space with our par-

ticipants ranging between 18-90 years, who are living

mostly in the Netherlands. Two sets of training data

will be used, one for participants aged 18-59 years

and the other for participants aged 60-90 years. We

assume the elderly population will exhibit a slower

speed of motion in comparison to their caregivers.

The autographer wearable camera will be mounted

unto the participant’s bodies to capture daily activi-

ties for labelling and interpreting the data. Also, we

will utilize the caregivers and their elderly relatives

own homes as test-beds for our data collection. For

the purposes of this experiment, we assume that the

homes of the caregivers and their elderly relatives are

furnished with staircases, which is typical of Dutch

family homes. The selected activities include typ-

ical activities of daily living, which are commonly

found in validated scales such as (Reisberg et al.,

2001). Also, we used the following articles (Chavar-

riaga et al., 2013; Cook et al., 2009) as references for

compiling our scenario.

3.1.1 Scenario Script

For our experiment, we will ask each participant to

perform the following activities in sequence.

• Telephone Use (Sitting): The subject will dial a

speciﬁc number listed on an instruction sheet and

write down the instructions given on the recorded

message. The stationary and cellular phone will

be located on the dining room table.

• Cleaning (Walking): Participants will be asked to

vacuum the ﬂoor of their living rooms.

• Exercising (Running and Cycling): Outside of

their own homes, able bodied participants will be

asked to jog on the spot for a few minutes. More-

over, those who are able to cycle will be asked to

ride a bicycle for a few minutes. Note well that

this scenario is more applicable to the caregivers.

ICT4AgeingWell2015-InternationalConferenceonInformationandCommunicationTechnologiesforAgeingWelland

e-Health

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• Hand Washing (Standing): Participants will be

asked to wash their hands in the bathroom face

basin using their own toiletries.

• Collect Ingredients (Walking Upstairs and Down-

stairs): Each Participant will be asked to collect

some ingredients, which will be placed on the

stair-head.

• Preparing a Snack (Walking and Standing): Par-

ticipants will be asked to prepare a snack and

beverage in accordance with the recorded instruc-

tions.

• Resting (Lying): Participants will be asked to lay

on the couch in their living rooms.

Moreover, the data sets generated from the above

mentioned activities will serve as ground truth for val-

idating the perceptions of both the caregivers and their

elderly relatives.

4 THE EXPERIMENT

In this paper, we will investigate the extent to which

the perception of the activities of the elderly by their

caregivers and vice versa help to improve bonding.

Moreover, we seek to understand how much hidden

information would be perceived from the unclassiﬁed

accelerometer data and the classiﬁed data, which has

a direct correlation between the level of activity and

the jagged edges of the output graph.

4.1 Proposed Methodology

We will conduct a preliminary study applying the

within subjects design to a group comprising of ten

caregivers and ten elderly participants. To reduce the

carry-over effects of this experimental design, we will

divide the group into two groups A and B. This im-

plies, that we will show Group A the classiﬁed ac-

tivity display and subsequently show the unclassiﬁed

activity display. For Group B, we will ﬁrst show the

unclassiﬁed activity display and then the classiﬁed ac-

tivity display. Figures 1 and 2 summarize the experi-

mental approaches.

In the classiﬁcation approach, we will extract and

select these features (standard deviation, mean, en-

tropy etc), which will be used to train the multi-class

SVM classiﬁer. Signals from the both the elderly and

the caregivers will be streamed to a server, which will

perform feature extraction and selection, and subse-

quently classify the activities. The activities will be

grouped into low, medium and high levels according

to table one. These levels of activities will be relayed

to android tablets, positioned like a photo-frame in

Figure 1: Set-up I showing the classiﬁcation approach.

Figure 2: Set-up II showing the unclassiﬁed approach.

Table 1: Table showing activity classiﬁcation levels.

Activities Activity Levels

lying low

sitting low

standing medium

walking medium

walking downstairs medium

running high

cycling high

walking upstairs high

the homes of both the caregivers and their elderly rel-

atives and will be rendered using simple line graphs

(Figure 3).

Figure 3: Line graph showing the activity levels.

The dual display of the activities of both the care-

giver and their elderly relatives shows a bidirectional

communication channel, which we believe is a nec-

essary component for reducing the ‘Big Brother’ role

of caregivers. As shown in set-up II, the experiment

is repeated but this time, the original raw accelerom-

eter data is ﬁltered, smoothened and approximated to

a similar line graph as displayed in the set-up I.

Our choice of simple line graphs to display the

activities is not random. Line graphs are one of the

most common means of visualizing time-series data.

PerceptionofHumanActivities-AMeanstoSupportConnectednessBetweentheElderlyandTheirCaregivers

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They are able to easily display data, facilitate com-

parison and reveal trends within the data. As shown

in Figure 3, we believe it will be easier to associate

low, medium and high slopes with low, medium and

high activity levels respectively. Therefore, this visual

rendering will portray the same aesthetic quality and

visual complexity for both displays.

4.2 Evaluation

To determine the usefulness of the proposed approach

we hope to investigate the following:

• How does the experimental subjects perceive both

displays and their preference?

• Did the elderly participants recognize certain pat-

terns in the activities of their caregivers and vice

versa?

• When they observe the display, what are their per-

ceptions of their caregiver’s activities?

• Will the application trigger social behaviour? For

instance, will we see an increase in phone calls?

Furthermore, to investigate social connectedness we

will utilize the validated scales such as the Inclusion

of Other in the Self scale (IOS) as discussed in (Aron

et al., 1992) and the IPO Social Presence question-

naire detailed in (De Greef and Ijsselsteijn, 2001).

5 CONCLUSIONS AND FUTURE

WORK

We have presented our proposed approach for per-

ception of activities from smart-phone generated data

to increase bonding relations between the elderly and

their caregivers. Furthermore, we have described two

techniques for investigating the participants’ percep-

tions of the activity displays and how their percep-

tions would support social connectivity between the

generations. Firstly, we proposed the popular SVM

technique for the activity classiﬁcation display and

secondly we proposed an abstraction of the original

raw accelerometric and gyroscopic data for the un-

classiﬁed display. Provided that the results of this

experiment are favourable, we have a variety of ac-

tivity displays in mind and we hope to implement

them in the near future. Our major long term goal

is to improve bonding and care by designing a system

in which the elderly and their caregivers can interact

through other signals (mainly physiological signals),

among them unconscious signals. In subsequent ex-

periments, we will implement a covert lighting appli-

cation, which will provide contextual awareness of

activity and emotional states of both the caregivers

and their elderly counterparts. Therefore, we believe

this subtle contextual awareness will facilitate the un-

derstanding of the affective and activity states of both

parties and in turn will help them to respond with the

appropriate behaviour. Consequently, this could con-

tribute to improving social connectedness.

ACKNOWLEDGEMENTS

This work was supported in part by the Erasmus

Mundus Joint Doctorate (EMJD) in Interactive and

Cognitive Environments (ICE), which is funded by

Erasmus Mundus under the FPA no. 2010-2012.

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