Activity Scores of Older Adults based on Inertial Measurement Unit

Data in Everyday Life

Sandra Hellmers

1 a

, Lianying Peng

, Sandra Lau

, Rebecca Diekmann

, Lena Elgert

urgen M. Bauer

, Andreas Hein

and Sebastian Fudickar

1 b

Assistance Systems and Medical Device Technology, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany

Center for Geriatric Medicine, University Heidelberg, 69117 Heidelberg, Germany

Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School,

TU Braunschweig, 38106 Braunschweig, Germany

Keywords:

Activity Level, Sedentary Time, Inertial Measurement Unit, Healthy Aging, Machine Learning, Functional

Fitness, Functional Decline.

Abstract:

The trend of an ageing population is becoming more and more obvious. Staying healthy in old age is an

important social issue. Thereby, physical activity is essential for the preservation of physical function. We

developed an algorithm for determining the activity level of seniors in everyday life. The proposed algorithm

is based on machine learning activity detection using inertial measurement unit data. A series of activity

scores is obtained by executing the algorithm from data on the type of activity, total activity time and activity

intensity. To evaluate the performance of the proposed algorithm, a study with 251 participants aged above

70 (75.41 ± 3.88) years was conducted and the correlation between individual activity scores and clinical

mobility assessments was determined. Results showed a relation between the Six Minute Walking Test and

the total score in terms of activity level as well as the walk score. Additionally, the MVPA- and walk-score

show a clear trend regarding the frailty status of the participants. Therefore, these scores are indicators of the

physical function and hence validate the utility of the developed algorithm.

1 INTRODUCTION

Healthy ageing is becoming increasingly important,

due to the demographic change and the increasing

number of older people. In general, maintaining the

ﬁtness and independence of older adults to stay at

home as long as possible is of high relevance. Early

detection of functional changes allow interventions

which improve or at least maintain physical function,

which in consequence may reduce fall rates or the

level of dependency (Beswick et al., 2008). Sufﬁ-

cient exercise is of main importance for healthy age-

ing. The results of Morey et al. (Morey et al., 2008)

suggest that physical activity predicts functional sta-

tus and negative changes in activity levels can indicate

a reduction in physical functions. Similarly, accord-

ing to Koroukian et al. (Koroukian et al., 2016), func-

tional limitations may indicate poor health in older

adults.

https://orcid.org/0000-0002-1686-6752

https://orcid.org/0000-0002-3553-5131

Early changes in functional status can be detected

by clinical assessments. An alternative approach is

the continuous monitoring of physical activity in ev-

eryday life with sensors, e.g. simple pedometers, ac-

celerometers or presence sensors. By using wearable

accelerometers, duration, intensity and type of activ-

ity can be determined via machine learning. In com-

parison to conventional assessments continuous mon-

itoring reduces personnel and time expenditure and

leads to a higher amount of everyday life results as

technical monitoring avoids an examination situation

and the active contribution of the subject.

This paper presents an unobtrusive approach for

determining the activity level of seniors in everyday

life using an inertial measurement unit. An algorithm

was developed which evaluates the different activities

in daily life. In consequence, the use of this method-

ology may result in early-stage interventions to pre-

vent or reduce muscle loss by developing an individ-

ual training plan as soon as changes of the activity

behavior of older people were identiﬁed.

Hellmers, S., Peng, L., Lau, S., Diekmann, R., Elgert, L., Bauer, J., Hein, A. and Fudickar, S.

Activity Scores of Older Adults based on Inertial Measurement Unit Data in Everyday Life.

DOI: 10.5220/0009095505790585

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 579-585

ISBN: 978-989-758-398-8; ISSN: 2184-4305

 2022 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

579

2 STATE OF THE ART

As already mentioned, changes in the physical ac-

tivity of older adults can be a predictor of muscle

loss and a possible loss of mobility. Thereby, phys-

ical activity can be measured both in terms of ac-

tivity behavior and energy consumption. There are

several methods to estimate the activity. For exam-

ple, the recording of physical activity can be carried

out using movement sensors as well as questionnaires

(Ainsworth, 2009). Acceleration sensors were used

in several studies to measure physical activity (Ya-

sunaga et al., 2017; Loyen et al., 2017; van Ballegooi-

jen et al., 2019) and a method to denote generic physi-

cal activity phenotypes from long-term accelerometer

data was presented in (Marschollek, 2013).

However, Copeland et al. have shown, based on a

literature search, that the sedentary time in the case

of surveys compared to the time measured by de-

vices is estimated too low (Copeland et al., 2017).

When recording activity using questionnaires, the

time of physical activity is often overestimated. The

gold standard for determining energy expenditure is

the double-marked water method using isotopes, but

this is expensive and not suitable for everyday use.

Therefore, a technical measurement of activity supe-

riors questionnaires and clinical measurements and is

preferable.

In literature, the activities were often assigned

by an intensity unit based on their share of energy

metabolism. The intensity unit is deﬁned in MET

(metabolic equivalent of task).

Table 1: Activity classiﬁcation and corresponding MET-

value.

activity MET

sedentary behavior ≤ 1.5

mild physical activity 1.5 to 3.0

moderate to strong physical activity ≥ 3.0

A MET is deﬁned as the energy conversion dur-

ing quiet sitting, which means an average oxygen

consumption of about 3.5ml · kg

−1

· min

−1

or 1 kcal ·

−1

· h

−1

for an adult (Ainsworth et al., 1993). Table

1 shows a basic classiﬁcation, whereby physical ac-

tivity can be classiﬁed according to their intensity in

sedentary behavior, mild physical activity and moder-

ate to vigorous physical activity. For example, For-

tune et al. have developed an algorithm for the classi-

ﬁcation of activity levels for persons with rheumatoid

arthritis, in which the activities are classiﬁed in class

A (3-5 METs), class B (2-3 METs) and class C (1-

2 METs) (Fortune et al., 2011). Class A activities are

walking, stair climbing and cleaning tasks, class B ac-

tivities are dressing, washing, drying dishes and class

C are reading and writing.

Although it is difﬁcult to accurately characterize

the amount of physical activity which is required to

maintain functional independence, it seems that mod-

erate to higher activity levels are more effective than

mild activities. It can be assumed that there might be

a threshold of at least moderate activity for signiﬁcant

outcomes (Paterson and Warburton, 2010). Yasunaga

et al. showed that replacing low sedentary behavior

times (e.g., television and desk work) with medium to

high levels of physical activity can contribute to im-

proving physical function in older adults (Yasunaga

et al., 2017). Loyen et. al have found that one-third

of the participants did not meet the physical activity

recommendations of the World Health Organization

(WHO) based on total time in moderate to vigorous-

intensity physical activity (MVPA), while more than

70% did not meet the recommendations based on

time in MVPA bouts of at least 10 minutes (Loyen

et al., 2017). In most articles with sensor-based ac-

tivity estimation, the time in sedentary behavior (SB),

light-intensity physical activity (LIPA) and moderate

to vigorous-intensity physical activity (MVPA) were

distinguished. Whereby, the wearing period of the

accelerometer was at least four days (including one

weekend day) with at least 10 hours recording (600

minutes) per day.

In most cases, the questionnaires evaluate the fre-

quency, duration and type of activity in order to de-

termine the level of physical activity. In the work

of Huy and Schneider, a tool for measuring activ-

ity was developed for the German-speaking countries

(Huy and Schneider, 2008) and compared following

questionnaires: Dijon Physical Activity Score, Mod-

iﬁed Baecke Questionnaire for Older Adults (MBQ),

Physical Activity Scale for the Elderly (PASE), Yale

Physical Activity Survey (YPAS) and Zutphen Phys-

ical Activity Questionnaire (ZPAQ). Trampisch et

al. compared the MBQ, ZPAQ, PASE, YPAQ and

CHAMPS Physical Activity Questionnaire for Older

Adults (Trampisch et al., 2011). Since the question-

naires MBQ (Voorrips et al., 1991), PASE (Washburn

et al., 1993), YPAS (Dipietro et al., 1993) and ZPAQ

(Caspersen et al., 1991) have been used in both ar-

ticles and are suitable for our target group of older

adults starting at 60 years, they will also be consid-

ered in the present analysis.

These questionnaires identiﬁed the following ar-

eas of physical activity: sports, leisure, household,

gardening, occupation, locomotion and rest periods

(Huy and Schneider, 2008). All questionnaires use an

overall score that summarizes all surveyed physical

activities into a single result. Each activity is assigned

HEALTHINF 2020 - 13th International Conference on Health Informatics

580

a weighting. For YPAS and ZPAQ, this weighting is

based on the MET value, which corresponds to the

energy consumption during the performance of the

physical activity and an intensity score was assigned

to each speciﬁc activity area. MBQ and PASE re-

sult in a total score. MBQ calculates the sum of the

activity areas sports, household and leisure with dif-

ferent weightings and PASE calculates the sum of 12

activity-subareas with different weightings.

3 METHODS

The aim of the present analysis was the calculation

of an individual activity score based on the performed

activities in daily life. The score should hold crucial

information about the functional status and should be

sensitive for changes in the activity behavior of the

older adults to recognize ﬁrst signs of functional de-

terioration occurring with age.

The study design and the algorithm for the score

calculation are presented in the following subsections.

3.1 Study Design and Sample

We conducted a study with 251 participants aged 70

at minimum. The characteristics of our study sample

are listed in Table 2. 148 participants were female and

103 male.

Table 2: Characteristics of the study sample (n=251), SD

stands for standard deviation.

min max mean (SD)

age [years] 70 87 75.41 (3.88)

height [cm] 145.8 192.5 167.10 (9.27)

weight [kg] 46.85 123.45 76.85 (14.15)

The participants performed ﬁrst a comprehensive

clinical assessment, which included besides other

tests the Six Minute Walking Test (6MWT) (Butland

et al., 1982), the Short Physical Performance Battery

(SPPB) (Guralnik et al., 1994), the Timed Up and

Go Test (TUG) (Podsiadlo and Richardson, 1991),

the Frailty Index (FI) (Fried et al., 2001) and the

Stair Climb Power Test (SCPT) (Bean et al., 2007).

All functional tests were measured by physical thera-

pists in a conventional manner as well as technology-

based. The description of the whole study can be

found in (Hellmers et al., 2017). Besides medical

products and ambient sensors like light barriers, an

inertial measurement unit (IMU) integrated into a belt

was also used. The used sensor unit consists of a

3D accelerometer, gyroscope, magnetometer and a

barometer.

Following the assessment, the participants wore

the IMU-based sensor belt for seven days in their ev-

eryday life. To compare the results of the sensor with

a gold standard measure, they were asked to write an

activity diary.

In this analyses, data of 231 participants were in-

cluded. 20 subjects were excluded, due to too short

measurements with a duration below 4 days or a mean

duration of fewer than 10 hours per day, incorrect

wearing of the belt (deviating sensor orientation) or

technical problems. The criterion of a minimum mea-

surement time was chosen on the basis of the litera-

ture research (see Section 2).

3.2 Hierarchical Classiﬁcation Model

and Score Algorithm

To develop an adequate algorithm for the score cal-

culation, the sensor measurements were analyzed via

a machine learning classiﬁer regarding the performed

activities. We used a hierarchical classiﬁcation model

with four classiﬁers. After a low pass ﬁltering of

the raw data, the ﬁrst classiﬁer distinguished between

static and dynamic activities, as well as transitions.

If the state was classiﬁed, the other classiﬁers char-

acterized the activities in detail. The selection of the

trained activities was chosen regarding the activities

in the conducted assessment tests. Figure 1 illus-

trates the hierarchical classiﬁcation model. The state

classiﬁer is based on boosted decision trees, whereby

the other classiﬁers are based on multilayer percep-

trons (MLP). The MLP-classiﬁer for the static activ-

ities consists of 5 hidden layers (HL) and 7 hidden

nodes (HN), the classiﬁer for dynamic activities of 3

HL and 44 HN, and for transitions of 4 HL and 40

HN. Further details of the different classiﬁers, used

window sizes, step widths, ﬁlters and feature sets can

be found in (Hellmers et al., 2018; Hellmers et al.,

2019).

Figure 1: Hierarchical classiﬁcation model. The ﬁrst clas-

siﬁer distinguished between the state. The following classi-

ﬁers recognized the possible activities of each state.

After the classiﬁcation of the performed activi-

ties, an activity score can be calculated. Therefore,

each activity is assigned an intensity weighting. The

weightings are based on the MET-values found in the

literature (see Section 2) and listed in Table 3.

Activity Scores of Older Adults based on Inertial Measurement Unit Data in Everyday Life

581

Figure 2: Average time in minutes of each activities per day over 231 participants.

The total activity score S is calculated by the

whole duration of an activity A

per day

∑

k=1

/T, (1)

with Z

as duration of activity and T number of mea-

suring days, multiplied by the intensity weighting I

S =

∑

i=1

· A

), (2)

with n as total number of recognized activities. Al-

ternatively, speciﬁc scores like the sedentary behavior

score can be calculated by only taking the correspond-

ing activities into account:

= I

sitting

· A

sitting

+ I

lying

· A

lying

(3)

LIPA-, MVPA-Scores and scores of speciﬁc activities

can be calculated analogously.

4 RESULTS

Figure 2 shows the activities per day in minutes over

the total study sample. As expected, the participants

showed most of the time a sedentary behavior with in

average 484.43 ± 126.88 minutes. The second most

frequent activity was standing with 250.30 ± 25.61

minutes per day. The average walking time is about

75.28 ± 25.61 minutes per day. Due to the limited se-

lection of recognized activities, there are some miss-

classiﬁcations identiﬁed from the diaries of the par-

ticipants. For example, housework and gardening are

recognized as standing in most cases.

Table 3: Intensity weightings and categories of each ac-

tivity. The abbreviations stand for: Sedentary behavior

(SB), light-intensity physical activity (LIPA) and moderate

to vigorous-intensity physical activity (MVPA).

activity intensity category

weighting

sitting 1 SB

lying 1 SB

standing 2 LIPA

walking 3.5 MVPA

transitions 5.5 MVPA

climbing stairs 6.85 MVPA

jumping 10 MVPA

Another important activity for our target group is

cycling. Cycling is often classiﬁed as climbing stairs,

walking, sitting or standing. In a self-experiment it

could be shown that the speed of cycling has a sig-

niﬁcant inﬂuence on the classiﬁcation. Thus, very

slow riding is classiﬁed as sitting while fast rid-

ing is recognized as climbing stairs. Since driving

faster means a higher energy consumption, this ”miss-

classiﬁcation” is categorized correctly regarding the

intensity weighting.

In terms of METs, the activities also fall into

the right category: for example, slow-cycling (5.5

mph) corresponds to 3.5 METs, while faster cycling

(10-11.9 mph) corresponds to 6.8 METs (Ainsworth

et al., 2011). These values are similar to the intensity

weightings of 3.5 for walking and 6.85 for climbing

stairs.

In a second analysis, the study participants were

categorized into four different groups regarding the

HEALTHINF 2020 - 13th International Conference on Health Informatics

582

Figure 3: Average MVPA-score, walk-score, stand-score and sedentary behavior-score (SB) for the different frailty groups

regarding the Fried phenotype.

Fried phenotype: robust (0 points) , pre-frail (1 point

and 2 points) and frail (≤ 3 points). The distribution

of the 231 subjects among the groups is as follows:

160 (robust - 0 points), 60 (pre-frail - 1 point), 11

(pre-frail - 2 points). Figure 3 shows the results of the

MVPA-score, walk-score, stand-score and SB-score

for the different frailty groups. There is a clear trend

that seniors with a low MVPA-score (decreasing ac-

tive) are frailer. On average, the participants of the

robust-group have a higher MVPA-score of 4.32 and

(pre-) frail seniors have lower MVPA-scores of 3.74

and 2.88. This trend can also be observed in the walk-

ing score. Therefore, the walking-score could also be

a predictor of frailty. However, the SB-score seems to

be not a sure sign of frailty. Due to the disbalanced

group sizes, these results should be considered with

caution.

The scores also reﬂect the long times spent stand-

ing or in a sedentary behavior.

Figure 4 presents the results of the walking score

in comparison to the results of the Six Minute Walk-

ing Test (6MWT). Correlation analysis shows a light

signiﬁcant correlation with a p-value of p < 0.001

and a correlation coefﬁcient of 0.48. The dashed line

shows the calculated regression line.

Figure 4: Correlation of the walking-score with the results

of the Six Minute Walking Test.

5 CONCLUSIONS

We developed an algorithm for the estimation of an

activity level of older adults based on inertial mea-

surement unit measurements in daily life. We con-

ducted a study with 251 participants aged 70 years

and above and analyzed the activities recognized via

a hierarchical machine learning classiﬁcation model.

Different activity scores were calculated, whereby the

MVPA- and walk-score show a clear trend regarding

the frailty status of the participants. Correlation anal-

yses with the results of clinical mobility assessments

showed a signiﬁcant correlation between the walk-

Activity Scores of Older Adults based on Inertial Measurement Unit Data in Everyday Life

583

score and the Six Minute Walking Test.

Therefore, we presented a promising approach

that provides information on the health status through

unobtrusive everyday measurements. Further investi-

gation about the signiﬁcance of the scores are neces-

sary. Concerning the miss-classiﬁcations, an expan-

sion of the recognizable activities would be valuable

to improve the meaningfulness of the scores.

However, we are optimistic that this approach will

be able to detect changes in the activity behaviour of

seniors and thus is suitable to give early indications of

the necessity of interventions in the event of a begin-

ning functional decline.

ACKNOWLEDGEMENTS

The study is funded by the German Federal

Ministry of Education and Research (Project No.

01EL1822D).

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