Comparison Method of Long-term Daily Life Considering the Manner of

Spending a Day

Takahiko Shintani, Tadashi Ohmori and Hideyuki Fujita

Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan

Keywords:

Daily Life, Activity Data, Comparison Method, Clustering, Healthcare.

Abstract:

Recently, a large amount of physical activity data has been obtained via wearable sensors collected as lifelogs.

The long-term daily lives of users can be understood using such long-term activity data. In this paper, we

investigate a method for comparing two distinct periods of the daily life of a user to understand the long-term

characteristics of that user’s daily life. Our method uses only activity data that can be collected easily and

continuously over the long term using wearable sensors. There are various ways in which humans can spend

a day, and a period of daily life consists of a set of several days spent in several manners. We compare two

periods of daily life by considering the manner in which a day is spent. The manner in which a day is spent

can be distinguished based on the activities that are performed on a given day. The amount of movement

differs depending on the activity, and similar amounts of movement are measured when similar activities are

performed. We focus on this point to classify how each day is spent. Further, we distinguish the manner in

which a day is spent based on similarities in the time series data with respect to the levels of the activities by

noting the main sleeping period, which is an important behavior in daily human life. We propose a method to

compare two distinct periods of daily life based on the distribution of the manner in which a day is spent in

each period. The effectiveness of the methods proposed in this paper is evaluated via experimental evaluations

using real datasets.

1 INTRODUCTION

Data collection technology has become widespread

due to the extensive usage of small and inexpensive

sensors. Human activity data for long periods of

time can be continuously assessed using wristband-

and clip-type wearable sensors(Gurrin et al., 2014).

There are many services for visualizing information

related to sleeping time, steps, and calories burned us-

ing lifelog data obtained from wearable sensors. This

allows the user to keep track of his or her health and

look back on past events(Jacquemard et al., 2014;

Christiansen and Matuska, 2006). In addition, re-

searchers have reported the use of activity data for

medical and health care(Dobbins and Rawassizadeh,

2015). Motion data have been used to monitor and

improve diet,(Amft and Troster, 2009) to monitor

the process of quitting smoking(Stanley and Osgood,

2011), and to analyze the relationship between a par-

ticular disease and various activities(Bravata et al.,

2007).

Many studies have been conducted to recognize

human behavior from activity data acquired by wear-

able sensors(Lara and Labrador, 2013). Human ac-

tivity recognition (HAR) methods for recognizing ba-

sic activities such as walking, running, and climbing

stairs and the recognition of behavior consisting of

multiple basic activities such as cleaning have also

been introduced(Kim et al., 2010; Chen et al., 2012;

Rawassizadeh et al., 2016b; Cheng et al., 2017). In

these methods, HAR models are built using a hidden

Markov model (HMM) and deep learning methods

such as convolutional neural network (CNN) and re-

current neural network (RNN) (Li et al., 2018; Jiang

and Yin, 2015; Rodriguez et al., 2017). However,

annotated data are required for the learning process.

HAR models are not sufﬁcient to understand human

daily life because the types of behaviors that can be

recognized are limited.

One of the uses of lifelogs is to understand long-

term daily life. Social rhythm metric (SRM) is one

method used to understand human life(Monk et al.,

1990; Monk et al., 1991). SRM evaluates the daily

life of a user using the regularity of the user’s daily

life patterns. The SRM method collects the times of

17 behavioral types, such as waking up, breakfast, and

Shintani, T., Ohmori, T. and Fujita, H.

Comparison Method of Long-term Daily Life Considering the Manner of Spending a Day.

DOI: 10.5220/0008163903470354

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 347-354

ISBN: 978-989-758-382-7

347

commuting, using questionnaires; then, it measures

the regularity of a person’s life via the dispersion of

these times. Because a user needs to manually record

the times associated with each behavioral type, this

process is difﬁcult to continue for a long time period.

However, SRM is widely used as an index for under-

standing the relationship between a person’s condi-

tion of life and diseases. Therefore, methods to un-

derstand long-term daily life are needed.

Studies aiming to construct a daily life model us-

ing using various sensor data obtained via smartphone

have also been reported(Mafrur et al., 2015; Sitova

et al., 2016). These studies focus on authentication

and user identiﬁcation using daily life models. Some

algorithms have also been proposed for efﬁciently

mining frequently performed behavioral patterns us-

ing smartphone logs(Rawassizadeh et al., 2016a). A

life model of these methods is a set of routine be-

haviors. These methods identify lifelog event data

that appear in the same time frame for consecutive

two or more days as a routine behavior. The main

lifelog event data is the usage of smartphone applica-

tion. GPS trajectory and Wiﬁ probe are transformed

to location and movement states. Acceleration data

are converted into basic activities. These are also used

as lifelog event data. Even though these methods are

also expected to help understand long-term life pat-

terns, only conscious behaviors by the user are con-

sidered. In our previous study, we have proposed a

long-term daily life comparison method using motion

status data(Shintani et al., 2019). Similar and differ-

ent periods of life can be compared; however, it is

necessary to use motion status data indicating seg-

ments in which the same type of motion continues.

However, common wearable devices do not output

motion status data. Recently, small wearable devices,

such as wristband sensors, have been developed that

can be worn during the daytime and while sleeping

to collect activity data 24 hours a day all year long.

Most sensor devices output activity amounts per unit

time. This data includes not only conscious behavior

but also unconscious behavior. We can use these data

to better understand human daily life.

In this paper, we propose a method to compare

daily life patterns during two time periods using the

activity amount data per unit time. Long-term activity

data gathered using a wristband-type sensor equipped

with an acceleration sensor are used in this paper. We

consider that daily human lives are comprised of peri-

ods of several days that are spent in several ways; we

then compare two such periods of daily life according

to the manner in which they were spent. Even though

it is assumed that the manner in which a day is spent is

characterized by the performed behavior, we examine

a method for comparing daily life without recogniz-

ing the concrete content of the behavior. In addition,

the amount of activity remains similar when similar

behaviors are performed. Therefore, similar amounts

of activity during the same time unit indicate that the

manner in which a day is spent is likely similar. We

treat the activity amount data, which are separated by

one day, as time series data and distinguish the day-

type distribution of how a day is spent. Accordingly,

we propose a method to compare two periods of daily

life using the day-type distribution. In addition, the

effectiveness of the proposed method is evaluated by

performing experiments using real data.

2 COMPARISONS OF DAILY

LIFE PATTERNS

In this paper, we evaluate the similarity between two

distinct periods of daily life for individual users. Ev-

eryone performs several activities during their daily

lives. Human lives are an accumulation of the several

manners in which a day is spent. For example, re-

searching in a laboratory, attending classes, relaxing

at home, performing part-time jobs, or going out with

friends are day-types related to the manner in which

a person, in this case a student, spends their day. Two

periods of daily student life can be compared using

the differences in the day-types in each period, such

as a period spent primarily attending classes and a pe-

riod of going out with friends and taking on part-time

jobs. Furthermore, even on the day of taking classes,

how to spend the day have to be distinguished by how

a user spent the rest of the time. Unconscious activi-

ties also have to be taken into account. Therefore, we

can characterize a period of daily life using the day-

types performed during that period.

In this paper, we compare two periods of daily life

based on the day-types in each period. The compar-

ison of the daily life in the two periods is intended

to evaluate the similarities between the daily lives of

one user in those two periods. By comparing the daily

life of one user in two periods, we can determine the

time at which this user lives similar and different daily

lives. In addition, we can also identify periods of sim-

ilar daily life patterns during speciﬁed periods of time.

Evaluating a period of daily life compared to a stan-

dard period of daily life is also possible. We expect

that a daily life comparison of two periods will be use-

ful to understand long time intervals of daily life.

Two periods of daily life are evaluated to be simi-

lar when the same day-types are frequently observed.

However, these periods are evaluated to be differ-

ent when different day-types are frequently observed.

KDIR 2019 - 11th International Conference on Knowledge Discovery and Information Retrieval

348

Recording the actual behaviors performed in a per-

son’s daily life is necessary to determine if a day-

type is performed. However, it is difﬁcult to manu-

ally record behaviors performed over long time peri-

ods and manual records exhibit poor reliability and

consistency. In this paper, we attempt to address

this problem using activity data. We collected long-

term activity data using a wearable sensor that was

equipped with an acceleration sensor. The activity

data obtained from the wearable sensor directly re-

ﬂect the user’s behaviors and permit the continuous

collection of data over long time periods. The activ-

ity data indicate the amount of activity; however, it is

difﬁcult to obtain detailed behavioral content from the

activity data. By expressing the activity according the

behaviors performed by the user, it is possible to ex-

tract information corresponding to the behavior using

the activity data. In this paper, we detect the day-types

using the activity data and compare two periods of a

user’s daily life based on these data.

3 ACTIVITY DATA

We collected activity data obtained using a wristband

sensor device. We used the life recorder UW-301BT

from Hitachi Systems as the sensor device (Figure 1).

The three-axis acceleration of the arm movement was

Figure 1: The Hitachi System wristband sensor UW-301BT.

measured by attaching the device to the user’s wrist

during the daytime and while sleeping. This device

outputs the amount of activity data in every minute.

One record is comprised of a date and time (in min-

utes) pair and the value of the activity amount. The

activity amount data have a numerical value indicat-

ing the intensity of the activity.

Figure 2 depicts one day of activity amount data

for an example user. Table 1 lists in part the activ-

ity amount data in Figure 2. In this ﬁgure, the verti-

cal axis represents the activity amount, whereas the

Figure 2: Example of activity amount data for one day.

horizontal axis represents time. The data obtained

over one day from 00:00 to 24:00 are graphically rep-

resented. From 02:00 to 08:00, the activity is ob-

served to be minor. Shortly after 10:00, a large activ-

ity amount indicates a transition in the type of activity.

Table 1: Example of activity amount data.

Date and time Activity amount

2018-10-11 10:00 144.6

2018-10-11 10:01 1685.9

2018-10-11 10:02 1728.3

2018-10-11 10:03 237.2

2018-10-11 10:04 2120.4

2018-10-11 10:05 1128.4

2018-10-11 10:06 15566.8

2018-10-11 10:07 15588.1

2018-10-11 10:08 15745.4

2018-10-11 10:09 15582.3

4 MANNER OF SPENDING A

DAY, THE DAY-TYPE

4.1 Clustering the Day-type

The day-type in which a day is spent is character-

ized by the user’s behavior during each time period.

Therefore, we distinguish the manner in which a day

is spent according to the performed behavior and

time. For example, a day of going to school in-

volves waking up at approximately 07:00, dressing

for 1h, traveling to school at 08:00, and attending

classes from 09:00. Meanwhile, a day of enjoying

sports in the morning involves waking up at approx-

imately 06:00, going out at 08:00, and playing. In

addition, even if the same behaviors are performed

on two days, they should be distinguishable as differ-

ent days if these behaviors are performed at different

times or for different lengths of time.

The activity amount data indicate the amount of

activity-related behaviors that are exhibited by a user.

Therefore, it is possible to distinguish between sim-

ilar or different behaviors. In this paper, days with

similar activity amounts at the same time of day are

observed to exhibit similar ways of spending a day,

that is, similar day-types.

Comparison Method of Long-term Daily Life Considering the Manner of Spending a Day

349

The activity amount data for one day are an or-

dered list of the activity amounts from 00:00 to 24:00

and are referred to as the activity vector and is com-

prised of 1440 elements. The distance between two

activity vectors corresponds to the dissimilarity in the

manner in which a day is spent. When the Euclidean

distance is adopted, the distance d(x, y) between two

activity vectors x and y can be calculated as follows:

d(x, y) =

∑

i=0

− y

)

Here, n denotes the number of elements in the activity

vector for one day. x

and y

denotes the i-th element

of the activity vector x and y. The smaller the value of

d(x, y), the higher the probability that the day-types

are similar. In this paper, we use the Euclidean dis-

tance; however, other distance metrics, such as the

Minkowski distance, may be used.

The activity vectors are clustered using a cluster-

ing algorithm using this distance metric. Activity vec-

tors belonging to one cluster are a set of days that ex-

hibit similar day-types. Each cluster corresponds to

a given set of day-types. Days belonging to different

clusters are distinguished as days with different day-

types, or different ways of spending the day. The clus-

tering procedure for the activity vectors can be given

as follows:

1. Convert the activity amount data into activity vec-

tors for each day;

2. Cluster the activity vectors using the clustering al-

gorithm; and

3. Label each day as the day-type, i.e., cluster, to

which the activity vector belongs.

4.2 Level of the Activity Amount Data

The activity amount depends on the user’s behav-

ior. When behaviors with active movements are per-

formed, the activity amount becomes very high. The

value difference in the activity amount differs depend-

ing on the behavior. For example, the difference be-

tween the activity amounts of gardening and play-

ing with children is considerably larger than that be-

tween gardening and deskwork. With respect to be-

haviors, a difference can be observed between gar-

dening, which is standing work, and deskwork, which

is sitting work. Meanwhile, for behavior with gentle

movements, the difference between activity amounts

is small even with different behaviors. A divergence

can be observed between the differences in behaviors

and activity amounts. However, the difference in ac-

tivity amount cannot adequately determine behavioral

differences.

In this paper, the symbolic aggregate approxima-

tion (SAX)(Keogh et al., 2001) algorithm is used to

convert the activity amount data into a symbol string.

SAX is also used to convert numerical time series data

into symbol strings. These symbols have meaning in

their magnitude relationships and differences. The

numerical time series data z = (z

, . . . , z

) of length

n is converted into a vector X = ( ¯x

, ¯x

, . . . , ¯x

) of

length M (≤ n). The i-th element ¯x

of X can be cal-

culated as follows:

¯x

∑

j=1

∗i+ j

This vector can be referred to as a piecewise aggregate

approximation (PAA). Because the activity data used

in this paper are given per minute,

corresponds to

the unit of time. Further, the value range of the PAA

elements is divided via equal frequency discretization

(EFD) into L ranges and each range corresponds to

the level of positive numbers. By converting each el-

ement of the PAA vector into a level, a vector can be

constructed based on the amount of activity data. This

vector is referred to as the activity level data. The ac-

tivity level data are used to denote the level of exercise

during each unit of time. The activity level data can

help us understand the amount of movement that has

been performed in a given unit of time. These data are

also numerical time series data; therefore, identifying

the speciﬁc context of a behavior is difﬁcult.

However, determining the times at which activi-

ties that do not involve considerable movement are

performed and the times at which well-characterized

behaviors are performed in a user’s daily life is pos-

sible. Even for users who are often involved in light

activities, it is possible to distinguish differences be-

tween their behaviors. Therefore, addressing the di-

vergence between behavioral differences and differ-

ences in activity amounts is feasible. The clustering

procedure of the day-type for the activity level data is

the same as the procedure presented in Section 4.1. A

list of activity level data for one day corresponds to

the activity vector.

4.3 Consideration of Sleep

Sleep is an important behavior in human life. When

comparing the manner in which a day is spent, we

need to consider both the sleeping hours and the sleep

time. Speciﬁcally, for the activity level data presented

in the previous section, the duration of the sleeping

time affects the clustering of the day-types. Users

with an average sleeping time of seven hours sleep

approximately 30% of their daily lives. When activity

level data are categorized into 10 levels, 3 levels are

KDIR 2019 - 11th International Conference on Knowledge Discovery and Information Retrieval

350

occupied by sleep. Therefore, the distance of the ac-

tivity amount vector with respect to the activity level

is strongly inﬂuenced by the sleeping time, making it

impossible to properly cluster the day-type.

In this paper, we use the activity level data gener-

ated based on the activity amount data for the entire

day and that excepting the main sleeping period. The

longest sleeping period in a day is considered to be the

main sleeping period. Several studies have been con-

ducted to detect sleep in activity data(Webster et al.,

1982; Kay et al., 2012). Here, sleep was detected us-

ing the Cole equation(Cole et al., 1992) and the ac-

tivity level data corresponding to the main sleeping

period were converted to “NULL”.

While clustering the day-type, it is necessary to

consider the main sleeping period while performing

the distance calculation for an activity vector. While

comparing two days with respect to the manner in

which the day was spent, the time unit, that is the

main sleeping period for both the days, can be re-

garded as exhibiting the same behavior. However, for

the time unit of the main sleeping period on only one

day, we cannot calculate the activity level difference

because the value of the activity level in this time unit

is “NULL”. Therefore, the equation of the distance

(x, y) of the activity vector is modiﬁed to omit the

main sleeping period:

(x, y) = d(x, y) ×

M − M

Here, M

denotes the number of entries in which the

elements of the same time unit for both vectors are

not “NULL” and M

denotes the number of entries

in which the elements of the same time unit for both

vectors are “NULL”. Therefore, the difference corre-

sponding to the time unit in which only one side is

the main sleeping period is rectiﬁed by the time unit

difference can be calculated. There is a case where

= 0. This case is valid when all the time units of

the two days are expressed in the main sleeping pe-

riod of one day only. In this case, the distance cannot

be calculated. Our method marks such cases as out-

liers. Days belonging to outliers are considered to be

in a single cluster.

5 PROPOSED METHOD

In this paper, we compare two periods of daily life

based on the manner in which the day is spent dur-

ing each period. The composition of the manner of

spending a day, the day-types, of a period p denotes

the distribution of the number of days corresponding

to those day-types. The distribution of this number of

days is referred to as the day-type vector. For exam-

ple, in the clustering of the day-types, the days can be

classiﬁed into three types. For a period of 10 days, if 3

days are day-type 1, 5 days are day-type 2, and 2 days

are day-type 3, the day-type vector can be given as

(3, 5, 2). Because the daily life in a certain period can

be represented by such a vector, similarities between

such vectors correspond to similarities in the user’s

daily life. When the cosine similarity is adopted for

the vector similarity, the similarity between two peri-

ods sim(X, Y ) can be calculated as follows:

sim(X, Y ) = cos(X, Y ) =

X ·Y

kXk × kY k

With increasing proximity of sim(X, Y ) to 1, the daily

lives of these periods are observed to become increas-

ingly similar, where X and Y are considered to be day-

type vectors for each period.

Assuming the amount of activity data D, two pe-

riods P

and P

, the length of the activity level vector

M, the number of activity levels L, and the number of

day-types K, the procedure of the proposed method

can be given as follows:

1. Detect the main sleeping period in D and convert

the activity amount of the corresponding record of

D to “NULL”;

2. Convert D into the activity level data D

;

3. Convert D

into activity vectors and cluster them

into the day-types;

4. Generate the day-type vector for each period; and

5. Calculate the similarity from the day-type vectors.

The value outputted by this procedure is the similarity

between the two periods of daily life.

6 EXPERIMENTAL EVALUATION

6.1 Experiments

We examined our method using an actual dataset. We

used activity amount data collected from six exper-

imental participants. The term of each dataset was

approximately 0.8–6.5 years, with a total of 4795

days. The proposed methods, that is, the clustering

of the manner of spending a day (the day-type) and a

comparison of two periods of daily life, were evalu-

ated. While clustering the day-types, a K-means algo-

rithm(MacQueen, 1967) along the Euclidean distance

was used. The cosine similarity was used for the sim-

ilarity of the day-type vector. In all the experiments,

the unit of time of the activity level was set to 10 min,

Comparison Method of Long-term Daily Life Considering the Manner of Spending a Day

351

the number of activity levels was set to 10, and the

number of clusters on the k-means algorithm was set

to K= 15. Similar experimental results were observed

when other clustering algorithms were used and when

parameter settings were changed.

6.2 Evaluation of the Clustering of the

Day-type

We selected dates that were conﬁrmed to exhibit simi-

lar manners of spending a day and calculated the sim-

ilarity of the two periods. We calculated 56 pairs of

such days. Here, every two days for which the simi-

larity was calculated used data belonging to the same

experimental participant. In addition, we selected

days that were observed to have different day-types

and calculated the similarity of the two periods. We

calculated 83 such pairs. Every two days in which the

similarity was calculated used data belonging to the

same experimental participant. Here, the two combi-

nations of different day-types were set to ensure that

the sums of the activity amounts for each day were

nearly the same. Therefore, we avoided comparing

two days that were obviously different day-types.

Figure 3 depicts the calculation results for the sim-

ilarities of the day-types. The two plots on the left

Figure 3: Similarity of day-types.

side of Figure 3 indicate the results of similarity cal-

culations using the activity level data from the entire

day. The two plots on the right side are the results

of similarity calculations using the activity level data

excluding the time unit of the main sleeping period.

The ﬁrst and third plots from the left are the results of

similarity calculations of days exhibiting similar day-

types. The second and fourth plots from the left are

the results of similarity calculations of pairs of dif-

ferent day-types. As depicted in Figure 3, the value

ranges of similarity for similar day-types and that of

different day-types overlap when the value is calcu-

lated using data from the entire day. This result indi-

cates that calculations using data from the entire day

cannot accurately distinguish between day-types. For

each experimental participant, cases where the simi-

larity of different day-types was higher than the sim-

ilarity of similar day-types were considered to be er-

rors when distinguishing between types. This ratio

is referred to as the error ratio of the day-type dis-

tinction. The error ratio of the day-type distinction

for calculations using data from the entire day was

observed to be 29%. Meanwhile, the value range of

the similarity of similar day-types and that of different

day-types do not overlap when the value is calculated

using data excluding the time unit of the main sleep-

ing period. The error ratio of the day-type distinction

in this case was observed to be 0%.

Figures 4 and 5 depict the calculation results using

the activity amount per minute and PAA, respectively.

The unit of time of the PAA data was set to 10 min.

Figure 4: Similarity of the day-type using the activity

amount.

Figure 5: Similarity of the day-type using PAA.

Figures 4 and 5 show that calculations performed

using the activity amounts per min and the calcu-

lations using PAA cannot accurately distinguish be-

tween the day-types. Speciﬁcally, for people who ex-

ercise, several errors were present in the day-type dis-

tinction. For the similarity calculated using the value

of the activity amount, the difference in the activity

amount of a time unit with respect to the active behav-

iors became large, therefore increasing the distinction

errors for the day-type.

KDIR 2019 - 11th International Conference on Knowledge Discovery and Information Retrieval

352

6.3 Evaluation of the Comparison of

Two Periods of Daily Life

We prepared 26 pairs of periods in which similar

daily activities were conﬁrmed and 35 pairs of peri-

ods in which different daily activities were conﬁrmed.

Each period compared durations of two weeks to two

months.

Figure 6 depicts the comparison results of the

daily life for 10 times. The plots of on the left side are

Figure 6: Similarities of daily life activities, or day-types.

the cosine similarity of pairs of periods with similar

day-types, whereas those on the right side are the co-

sine similarity of pairs of periods with different day-

types. Figure 6 depicts the result of calculations using

the activity level data from the entire day and those

excluding the time unit of the main sleeping period.

A cosine similarity value closer to 1 indicates more

similar day-types.

Figure 6 depicts the difference between the aver-

age value of similar day-type pairs and the average

value of different day-type pairs. However, for the

calculation results obtained using the data from the

entire day, the cosine similarity ranges of the simi-

lar and different day-types overlap. For the same ex-

perimental participants, the case in which the similar-

ity of different day-types becomes higher than that of

similar day-types was considered to be an error. The

ratio of this error is referred to as the error ratio of

the daily life comparison. The error ratio of the daily

life comparison that used the data from the entire day

was 48%. Because calculations using data from the

entire day cannot accurately distinguish the day-type,

the daily life comparison was observed to be inaccu-

rate. Meanwhile, the cosine similarity range does not

overlap for results of calculations excluding the time

unit of the main sleeping unit. Further, the difference

in the similarity between similar and different day-

type pairs was also large with an error ratio of 0%.

The same trend was also observed when the number

of activity levels, the unit of time of the activity level

data, and the number of clusters of day-types were

changed. Therefore, the proposed method using the

activity level data, excluding the time unit of the main

sleeping period, can accurately compare two periods

of daily life.

7 CONCLUSIONS

In this study, we proposed a method to compare long-

term daily life patterns using activity data. The pro-

posed method does not detect the context of the be-

havior. The day-type, or the way a day is spent, is

clustered using the activity data of a user over a long

period of time as obtained from a wearable sensor de-

vice. We calculated the similarity between two pe-

riods of daily life based on the distribution of the

day-type in each period. In addition, we addressed

the problem of divergence with respect to the activ-

ity amount and the behavior based on the level of the

activity amount. By considering the main sleeping pe-

riod, which is an important behavior in human daily

lives, a method was proposed to distinguish between

day-types. The results of the experimental evaluation

performed based on actual data indicated that the pro-

posed method can accurately compare two periods of

daily life.

ACKNOWLEDGEMENTS

This work was supported by JST CREST Grant Num-

ber JPMJCR1503, Japan.

REFERENCES

Amft, O. and Troster, G. (2009). On-body sensing solutions

for automatic dietary monitoring. Pervasive Comput-

ing, 8(2):62–70.

Bravata, D., Spangler, C., Sundaram, V., Gienger, A., Lin,

N., Lewis, R., Stave, C., Olkin, I., and Sirard, J.

(2007). Using pedometers to increase physical activ-

ity and improve health: a systematic review. American

Medical Association, 298(19):2296–2304.

Chen, L., Hoey, J., and Nugent, C. (2012). Sensor-based

activity recognition. Transactions on systems, man,

and cybernetics, 42(6):790–808.

Cheng, L., Guan, Y., Zhu, K., and Li, Y. (2017). Recogni-

tion of human activities using machine learnig meth-

ods with wearable sesors. In Annual Computing and

Communication Workshop and Conference. IEEE.

Christiansen, C. and Matuska, K. (2006). Lifestyle balance:

a review of concepts and research. Journal of Occu-

pational Science, 13(1):49–61.

Comparison Method of Long-term Daily Life Considering the Manner of Spending a Day

353

Cole, R., Kripke, D., Gruen, W., Mullaney, D., and Gillin,

J. (1992). Automatic sleep/awake identiﬁcation from

wrist activity. Sleep, 15(5):461–469.

Dobbins, C. and Rawassizadeh, R. (2015). Clustering of

physical activities for quantiﬁed self and mhealth ap-

plications. In International Conference on Ubiquitous

Computing and Communications, pages 1423–1428.

Elsevier.

Gurrin, C., Smeaton, A., and Doherty, A. (2014). Lifelog-

ging: personal big data. Foundations and Trends in

Information Retrieval, 8(1):1–125.

Jacquemard, T., Novitzky, P., O’Brokchaun, F., Smeaton,

A., and Gordijn, B. (2014). Challenges and oppor-

tunities of lifelog technologies: a literature review

and critical analysis. Science and Engineering Ethics,

20(2):379–409.

Jiang, W. and Yin, Z. (2015). Human activity recognition

using wearable sensors by deep convolutional neural

networks. In Internationa Conference o Multimedia,

pages 1307–1310. ACM.

Kay, M., Choe, E., Shepherd, J., Greenstein, B., Watson,

N., Consolvo, S., and Kientz, J. (2012). Lullaby: a

capture & access system for understanding the sleep

environment. In International Conference on Ubiqui-

tous Computing, pages 226–234. ACM.

Keogh, E., Chu, S., Hart, D., and Pazzani, M. (2001). An

online algorithm for segmenting time series. In Inter-

national Conference on Data Mining, pages 289–296.

IEEE.

Kim, E., Helal, S., and Cook, D. (2010). Human activity

recognition and pattern discovery. Pervasive comput-

ing, 9:48–53.

Lara, O. and Labrador, M. (2013). A survey on human ac-

tivity recognition using wearable sensors. Communi-

cations Surveys and Tutorials, 15(3):1192–1209.

Li, F., Shirahama, K., Nisar, M., Koping, L., and Grze-

gorzek, M. (2018). Comparison for feature learning

methos for human activity recognition using wearable

sensors. Sensors, 18(679).

MacQueen, J. (1967). Some methods for classiﬁcation and

analysis of multivariate observations. In Berlekley

Symposium on Mathematical Statistics and Probabil-

ity, pages 281–297.

Mafrur, R., Dharma, I., and Choi, D. (2015). Modeling and

discovering human behavior from smartphone sens-

ing life-log data for identiﬁcation purpose. Human-

centric Computing and Information Science, 5(31).

Monk, T., Flaherty, J., Hoskinson, F., and Kupfer, D.

(1990). The social rhythm metric: an instrument to

quantify the daily rhythm of life. Nervous and Mental

Disease, 178(2):120–126.

Monk, T., Kupfer, D., Frank, E., and Ritenour, A. (1991).

The social rhythm metric (srm): measuring daily so-

cial rhythms over 12 weeks. Psychiatry Research,

36(2):195–207.

Rawassizadeh, R., Momei, E., Dobbins, C., Gharibshah,

J., and Pazzani, M. (2016a). Scalable daily human

behavioral pattern mining from multivariate temporal

data. Transactions on knowledge and data engineer-

ing, 28(11):3098–3112.

Rawassizadeh, R., Momeni, E., Dobbins, C., Gharibshah,

J., and Pazzani, M. (2016b). Scalable daily human

behavioral pattern mining from multivariate temporal

data. Transactions on Knowledge and Data Engineer-

ing, 28(11):3098–3112.

Rodriguez, M., Orrite, C., Medrano, C., and Makris, D.

(2017). One-shot learning of human activity with an

map adapted gmm and simlex-hmm. Transactions on

cybernetics, 47(7):1769–1780.

Shintani, T., Ohmori, T., and Fujita, H. (2019). Method

for comparing long-term daily life using long-duration

episodes. In International Workshop on Data Analyt-

ics solutions for Real-Life Aplications.

Sitova, Z., Sedenka, J., Yang, Q., Peng, G., Zhou, G.,

Gasti, P., and Balagani, K. (2016). Hmog: New be-

havioral biometric features for continuous authentica-

toin of smartphone users. Transactions on information

forensics and security, 11(5):877–892.

Stanley, K. and Osgood, N. (2011). The potential of sensor-

based monitoring as a tool for health care, health

promotion, and research. The annuals of Family

Medicine, 9(4):296–208.

Webster, J., Kripke, D., Messin, S., Mullaney, D., and

Wyborney, G. (1982). An activity-based sleep mon-

itor system, for ambulatory use. Sleep, 5(4):389–399.

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