SMART HOME SERVICE TERMINAL DESIGN
FOR ELDERLY FAMILIES INTEGRATING THE
KANO MODEL AND PERCEPTUAL
ENGINEERING
Wei Chen*
College of Arts, Chongqing Technology and Business University, Chongqing,
400067, China.
px13677699899@126.com
Hong Tang
College of Arts, Chongqing Technology and Business University, Chongqing,
400067, China.
Tingting Yan
College of Arts, Chongqing Technology and Business University, Chongqing,
400067, China.
Reception: 04/04/2023 Acceptance: 02/06/2023 Publication: 30/06/2023
Suggested citation:
Chen, W., Tang, H. and Yan, T. (2023). Smart home service terminal design
for elderly families integrating the KANO model and perceptual
engineering. 3C Tecnología. Glosas de innovación aplicada a la pyme, 12(2),
220-235. https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
220
SMART HOME SERVICE TERMINAL DESIGN
FOR ELDERLY FAMILIES INTEGRATING THE
KANO MODEL AND PERCEPTUAL
ENGINEERING
Wei Chen*
College of Arts, Chongqing Technology and Business University, Chongqing,
400067, China.
px13677699899@126.com
Hong Tang
College of Arts, Chongqing Technology and Business University, Chongqing,
400067, China.
Tingting Yan
College of Arts, Chongqing Technology and Business University, Chongqing,
400067, China.
Reception: 04/04/2023 Acceptance: 02/06/2023 Publication: 30/06/2023
Suggested citation:
Chen, W., Tang, H. and Yan, T. (2023). Smart home service terminal design
for elderly families integrating the KANO model and perceptual
engineering. 3C Tecnología. Glosas de innovación aplicada a la pyme, 12(2),
220-235. https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
ABSTRACT
In recent years, smart homes have gradually come into our lives and have brought
many positive impacts to our lives. However, in specifically targeted application design
planning, the designer community is not always able to consider and analyze every
factor. This paper proposes an integrated nonlinear design that incorporates the
KANO model as well as a mathematical model of sensible engineering coupled with
design science. The application of different design solutions in different types of
households is evaluated after dividing the different types of living of elderly
households into specific situations. The results show that for elderly households of
type 1 versus 4, scenario 2 generally has more accurate application feasibility
compared to scenario 1. The maximum increase in application accuracy for Scenario
2 compared to Scenario 1 was 6.28%. However, the frequency of use decreased by
3.09%. And for elderly households of type 2 and 3, which tend to live alone, the
feasibility of application of scenario 2 is similar or even worse than that of scenario 1.
The improved Scenario 3 both have higher application feasibility than Scenarios 1 and
2 and has a more user-friendly visual aid to understand the design, which helps the
elderly group to better use the smart home service terminal system.
KEYWORDS
Smart home; terminal design; elderly home; KANO model; perceptual engineering
INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. PRINCIPLE OF SMART HOME SERVICE TERMINAL
2.1. Principles of perceptual engineering
2.2. Principle of the KANO model
3. EVALUATION MODEL BASED ON LEAST SQUARES ALGORITHM (PLS)
4. ANALYSIS AND DISCUSSION
4.1. Division of different types of families
4.2. PLS analysis process
4.3. Evaluation of different conventional program applications
4.4. Evaluation of improvement program applications
5. CONCLUSION
REFERENCES
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Ed.44 | Iss.12 | N.2 April - June 2023
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1. INTRODUCTION
In recent years, smart homes have gradually come into our lives and brought a lot
of positive impacts to our lives [1, 2]. Compared with ordinary homes, smart homes
have been greatly improved, which can not only meet users' needs for living, provide
a suitable, convenient, and reliable living environment, but also give intelligence to
traditional living spaces [3, 4]. First of all, smart home products help us to save
resources. Intelligent home lighting systems can realize automatic adjustment of the
brightness of lamps and lanterns, which can ensure the brightness of the room while
minimizing energy consumption [5]. In addition, the intelligent lighting system can
achieve the light on when people come and go, giving us very much convenience, on
the other hand, it can also prevent forgetting to turn off the lights and cause power
waste [6, 7]. Secondly, in the smart home, the smart cat's eye can be installed on the
home security door, which has a wider visual range, infrared night vision function, and
can be connected to the network, which in turn can realize real-time monitoring of the
situation at the doorstep [8]. The smart home is a technologically intelligent product
closely related to people's daily life. The smart home is intended to serve our life and
bring comfort and convenience to our life [9, 10]. With the development of new
technologies such as the Internet of Things, cloud computing, and wireless
communication, smart home has been developed rapidly more convenient for
people's life [11-18]. Smart home enables users to control the devices in their homes
using smartphones to achieve remote control, scene control, linkage control, and
timing control. In terms of smart homes for the elderly, due to the arrival of aging and
the special physiological, psychological, and social needs of the elderly group for
smart homes, there are also some special needs for home products.
The smart home is mainly realized by applying the Internet of Things (IoT)
technology, where many home objects in the home, etc. are connected to the Internet
through sensors [19]. Secondly, for smart home, the main thing is that he has a variety
of control methods [20]. For the elderly, the smart home has added many new
functions and services, the smart home control is more abstract, and its control
methods have changed a lot [21]. For these changes, it is generally difficult for the
elderly to adapt. Compared with ordinary homes, the operation and control of smart
homes require higher abstract thinking skills, and these increase the information
burden of the elderly [22, 23]. Therefore, it is necessary to optimize the design of
smart homes to meet the needs of the elderly. Dhanusha, C [24] detected Alzheimer's
disease in the elderly by recording the daily activities of residents equipped with
sensor devices in their home appliances as smart homes. To obtain deeper features
of the sensor dataset that differ from the existing traditional supervised learning
paradigm, they constructed an optimized self-learning model. The model produced
better results on a smart home testbed and can be used to investigate the presence of
Alzheimer's disease in the elderly. Huu, P . N [25] proposed a system for recognizing
gestures and actions in smart homes. They used actions such as walking, sitting,
backing, putting on shoes, waving, falling, smoking, infant crawling, standing, reading,
and typing for recognition. In this system, data is captured from the camera of the
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1. INTRODUCTION
In recent years, smart homes have gradually come into our lives and brought a lot
of positive impacts to our lives [1, 2]. Compared with ordinary homes, smart homes
have been greatly improved, which can not only meet users' needs for living, provide
a suitable, convenient, and reliable living environment, but also give intelligence to
traditional living spaces [3, 4]. First of all, smart home products help us to save
resources. Intelligent home lighting systems can realize automatic adjustment of the
brightness of lamps and lanterns, which can ensure the brightness of the room while
minimizing energy consumption [5]. In addition, the intelligent lighting system can
achieve the light on when people come and go, giving us very much convenience, on
the other hand, it can also prevent forgetting to turn off the lights and cause power
waste [6, 7]. Secondly, in the smart home, the smart cat's eye can be installed on the
home security door, which has a wider visual range, infrared night vision function, and
can be connected to the network, which in turn can realize real-time monitoring of the
situation at the doorstep [8]. The smart home is a technologically intelligent product
closely related to people's daily life. The smart home is intended to serve our life and
bring comfort and convenience to our life [9, 10]. With the development of new
technologies such as the Internet of Things, cloud computing, and wireless
communication, smart home has been developed rapidly more convenient for
people's life [11-18]. Smart home enables users to control the devices in their homes
using smartphones to achieve remote control, scene control, linkage control, and
timing control. In terms of smart homes for the elderly, due to the arrival of aging and
the special physiological, psychological, and social needs of the elderly group for
smart homes, there are also some special needs for home products.
The smart home is mainly realized by applying the Internet of Things (IoT)
technology, where many home objects in the home, etc. are connected to the Internet
through sensors [19]. Secondly, for smart home, the main thing is that he has a variety
of control methods [20]. For the elderly, the smart home has added many new
functions and services, the smart home control is more abstract, and its control
methods have changed a lot [21]. For these changes, it is generally difficult for the
elderly to adapt. Compared with ordinary homes, the operation and control of smart
homes require higher abstract thinking skills, and these increase the information
burden of the elderly [22, 23]. Therefore, it is necessary to optimize the design of
smart homes to meet the needs of the elderly. Dhanusha, C [24] detected Alzheimer's
disease in the elderly by recording the daily activities of residents equipped with
sensor devices in their home appliances as smart homes. To obtain deeper features
of the sensor dataset that differ from the existing traditional supervised learning
paradigm, they constructed an optimized self-learning model. The model produced
better results on a smart home testbed and can be used to investigate the presence of
Alzheimer's disease in the elderly. Huu, P . N [25] proposed a system for recognizing
gestures and actions in smart homes. They used actions such as walking, sitting,
backing, putting on shoes, waving, falling, smoking, infant crawling, standing, reading,
and typing for recognition. In this system, data is captured from the camera of the
https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
mobile device used to detect the object. The results are obtained from the objects on
the frame through the bounding box. The results show that the system meets the
requirements with an accuracy of more than 90% and is suitable for practical smart
home applications for the elderly. Alzahrani. T [26] explored the main barriers and
facilitators to the use of smart home technology, remote monitoring, and telemedicine
systems to support healthcare and enable older people to maintain their
independence, and showed that lack of information about the functionality and
usability of the technology was the main barrier to adoption. Human issues such as
cost, platform management and infrastructure, and privacy are also barriers to the
diffusion of smart homes. Heon. R.J. [27] systematically reviewed the adoption and
user perception of health and environmental monitoring devices, highlighting the
difference between wearable and non-wearable. We identify user perceptions based
on usefulness, ease of use, and privacy. In terms of user experience, as wearable
devices compensate for their limitations, making an integrated model can improve
user perception. Kong, H [28] argues that the development of smart homes has driven
the concept of user authentication. This not only protects user privacy but also
provides personalized services to users. They developed a deep learning-based
method to extract behavioral features of finger gestures for highly accurate user
identification. The results of their study show that their optimized smart home
achieves a great user experience. Enhancing the intrinsic human inclination towards
nature for optimal health and well-being and supporting the physical, mental, and
social health of the elderly are goals that smart homes need to strive for. Yang, H [29]
develop a comprehensive research model that can explain the behavioral intentions of
potential customers to adopt and use smart home services. This will enable people to
access smart home services on the move using mobile devices through control and
monitoring functions, enhancing the sense of user experience. Liu, J [30] argues that
current smart home control terminals have many shortcomings and limitations in
terms of interaction methods and level of intelligence. They combined the theories of
context and behavior analysis to build a product design process based on behavior
context analysis, and through the analysis of building and unit behavior context, they
obtained the user's needs for control terminals in each context unit, and transformed
them into design elements for product concept design, and then designed smart home
control terminals to meet the needs of elderly people. Renaud, J [31] proposed that
product design should be human-centered, and they suggest that product use should
be considered through behavioral analysis and its user's behavior combined with
functionality. In the above studies, we can see that systematic research has been
made on the optimal design of smart homes for elderly families in terms of elderly
user experience, elderly operation, and elderly health monitoring. In addition, in the
smart home for the elderly, due to their safety protection, psychological and other
special needs. We need to make more emotional designs and simpler service
terminals for the elderly smart home so that they can have a better experience.
Traditional smart homes focus on improving reliability, functionality, usability,
appearance, and other design features. For older adults, however, their satisfaction is
influenced not only by perception but also by emotion. Therefore, in our research, we
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construct a framework through perception engineering to express the perception of
services through users' own words to understand smart home product attributes and
user perceptions. In addition, to better classify the needs attributes of the elderly on
the client side, and then better design smart home products for the elderly according
to their needs, we try to meet customer needs and improve customer satisfaction. We
combine the Kano model and perception engineering. We hope our work can make
some contributions to the application and service of smart homes for the elderly, and
provide a reference for building a green, low-carbon, environmentally friendly, and
low-cost smart home.
2. PRINCIPLE OF SMART HOME SERVICE TERMINAL
The main purpose of this study is to attach the smart home to the elderly living
space, link the smart home devices involved in the elderly home living environment
based on various smart home technologies, build a smart space suitable for the
elderly, help the elderly manage the smart home devices in the living environment,
and provide a multifunctional, reliable, and satisfactory living environment for the
elderly. In addition, the smart home service terminal for elderly families designed in
this study meets the multifaceted needs of the elderly living space. It has the following
characteristics.
1. Networked elderly living environment and smart home device control
The smart home devices involved in the elderly living environment are networked
online through the Internet of Things so that the elderly family members can not only
share and communicate the internal information of the smart home devices but also
control the working status of the smart home and obtain the required important
information in the places covered by the network in the elderly living environment.
2. Intelligent elderly living environment and smart home device control
Intelligent home equipment control intelligence provides a lot of convenience to the
elderly home life. First of all, the security of the living space for the elderly is greatly
guaranteed, which can make the children of the elderly home a lot of worries; on the
other hand, the addition of smart home devices can improve the quality of life of the
elderly, and facilitate the elderly to remote control the smart home devices involved in
the living environment, etc. Smart home device control intelligence can make the
elderly enjoy the benefits of technological progress. Therefore, the research on smart
home products suitable for elderly users has great practical significance.
2.1. PRINCIPLES OF PERCEPTUAL ENGINEERING
Perceptual engineering is a new product development technology that transforms
the elderly's requirements for the quality of future family life into the design of home-
smart homes and home-smart services. It is seeking the correlation between the
https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
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construct a framework through perception engineering to express the perception of
services through users' own words to understand smart home product attributes and
user perceptions. In addition, to better classify the needs attributes of the elderly on
the client side, and then better design smart home products for the elderly according
to their needs, we try to meet customer needs and improve customer satisfaction. We
combine the Kano model and perception engineering. We hope our work can make
some contributions to the application and service of smart homes for the elderly, and
provide a reference for building a green, low-carbon, environmentally friendly, and
low-cost smart home.
2. PRINCIPLE OF SMART HOME SERVICE TERMINAL
The main purpose of this study is to attach the smart home to the elderly living
space, link the smart home devices involved in the elderly home living environment
based on various smart home technologies, build a smart space suitable for the
elderly, help the elderly manage the smart home devices in the living environment,
and provide a multifunctional, reliable, and satisfactory living environment for the
elderly. In addition, the smart home service terminal for elderly families designed in
this study meets the multifaceted needs of the elderly living space. It has the following
characteristics.
1. Networked elderly living environment and smart home device control
The smart home devices involved in the elderly living environment are networked
online through the Internet of Things so that the elderly family members can not only
share and communicate the internal information of the smart home devices but also
control the working status of the smart home and obtain the required important
information in the places covered by the network in the elderly living environment.
2. Intelligent elderly living environment and smart home device control
Intelligent home equipment control intelligence provides a lot of convenience to the
elderly home life. First of all, the security of the living space for the elderly is greatly
guaranteed, which can make the children of the elderly home a lot of worries; on the
other hand, the addition of smart home devices can improve the quality of life of the
elderly, and facilitate the elderly to remote control the smart home devices involved in
the living environment, etc. Smart home device control intelligence can make the
elderly enjoy the benefits of technological progress. Therefore, the research on smart
home products suitable for elderly users has great practical significance.
2.1. PRINCIPLES OF PERCEPTUAL ENGINEERING
Perceptual engineering is a new product development technology that transforms
the elderly's requirements for the quality of future family life into the design of home-
smart homes and home-smart services. It is seeking the correlation between the
https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
elderly's response to the quality of life in the future home and the attributes of
designing the home smart service to develop a quantitative model for smart home
design and optimization.
Ergonomics establishes a framework through the self-expressed requirements of
the elderly for their future home quality of life, and then uses experts in the relevant
fields to design smart home functions; in addition, the special requirements of the
elderly are measured and their smart home attributes are optimized to a certain extent
to enhance their applicability and increase flexibility. There are two advantages of
perceptual engineering for the design of smart home service terminals in elderly
homes. One is to visually convey the real feelings of elderly users through smart
home design; the second is to establish a regression analysis model to determine the
interaction between customers' emotional responses and design features.
2.2. PRINCIPLE OF THE KANO MODEL
The main feature of the KANO model is to meet the requirements of the elderly for
future home life quality as much as possible to provide a multi-functional, reliable, and
satisfactory living environment for the elderly, but also to minimize the elderly's
discomfort with the smart home. The attributes can be divided into five categories:
charm attributes (A), expectation or one-dimensional attributes (O), essential or basic
attributes (M), reverse attributes (Q), and undifferentiated attributes (I), as shown in
Figure 1. The details are described as follows.
Figure 1. KANO model attribute diagram
Attraction attribute (A). This attribute intuitively expresses the elderly's expectation
of the appearance of the smart home, and the improvement of the charm attribute of
the home will bring great satisfaction to the elderly users.
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Expectation or one-dimensional attribute (O). This attribute shows the linear
relationship between the satisfaction of the elderly with the smart home and whether
the design concept of the smart home matches each other, i.e., the more the design
concept of the smart home matches the actual conditions, the higher the satisfaction
of the elderly with the smart home.
Essential or Basic Attributes (M). This attribute indicates the functional attributes
that seniors take for granted in the smart home and that are not explicitly mentioned
by the seniors' customers. The absence of this attribute causes a decrease in the
satisfaction of the elderly with the smart home, but the addition of this attribute does
not cause interest in the elderly.
Reverse attribute (Q). This attribute indicates that seniors do not want the
functional attributes in the smart home, and providing this functional attribute will
decrease seniors' satisfaction with the smart home, while the lack of this attribute will
increase seniors' satisfaction with the smart home.
No difference attribute (I). This attribute indicates the functional attributes that
seniors feel dispensable in the smart home, and providing this functional attribute or
the lack of this functional attribute will not affect seniors' satisfaction with the smart
home.
3. EVALUATION MODEL BASED ON LEAST SQUARES
ALGORITHM (PLS)
This paper applies the PLS algorithm to evaluate the relationship between
perceptual engineering and the KANO model coupled with each other to analyze the
relationship between annual people's satisfaction with smart homes. the PLS
algorithm needs to be tested by calculating the residual sum of squares after
extracting the principal components, and the residual sum of squares needs to be
smaller than the maximum allowable error r. firstly, n observations are made, i.e. n
sample points are selected to study the relationship between the dependent and
independent variables. The partial least squares-based correlation analysis is similar
to the typical correlation analysis in that it requires the extraction of principal
components in the independent variable X and the dependent variable Y, respectively,
and calculates the specific computational procedure as follows.
(1)
(2)
Where is the residual sum of squares; denotes the sample point
denotes the indicator; denotes the number of extracted principal components; is
r=min(p,q)
PRESS
j(k) =
n
∑
i
(
yij −
yij(k)
)2
PRESSj(k)
i
i
j
j
k
p
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Expectation or one-dimensional attribute (O). This attribute shows the linear
relationship between the satisfaction of the elderly with the smart home and whether
the design concept of the smart home matches each other, i.e., the more the design
concept of the smart home matches the actual conditions, the higher the satisfaction
of the elderly with the smart home.
Essential or Basic Attributes (M). This attribute indicates the functional attributes
that seniors take for granted in the smart home and that are not explicitly mentioned
by the seniors' customers. The absence of this attribute causes a decrease in the
satisfaction of the elderly with the smart home, but the addition of this attribute does
not cause interest in the elderly.
Reverse attribute (Q). This attribute indicates that seniors do not want the
functional attributes in the smart home, and providing this functional attribute will
decrease seniors' satisfaction with the smart home, while the lack of this attribute will
increase seniors' satisfaction with the smart home.
No difference attribute (I). This attribute indicates the functional attributes that
seniors feel dispensable in the smart home, and providing this functional attribute or
the lack of this functional attribute will not affect seniors' satisfaction with the smart
home.
3. EVALUATION MODEL BASED ON LEAST SQUARES
ALGORITHM (PLS)
This paper applies the PLS algorithm to evaluate the relationship between
perceptual engineering and the KANO model coupled with each other to analyze the
relationship between annual people's satisfaction with smart homes. the PLS
algorithm needs to be tested by calculating the residual sum of squares after
extracting the principal components, and the residual sum of squares needs to be
smaller than the maximum allowable error r. firstly, n observations are made, i.e. n
sample points are selected to study the relationship between the dependent and
independent variables. The partial least squares-based correlation analysis is similar
to the typical correlation analysis in that it requires the extraction of principal
components in the independent variable X and the dependent variable Y, respectively,
and calculates the specific computational procedure as follows.
(1)
(2)
Where is the residual sum of squares; denotes the sample point
denotes the indicator; denotes the number of extracted principal components; is
r=min(p,q)
PRESSj(k) =
n
∑
i
(yij −
yij(k))2
PRESSj(k)
i
i
j
j
k
p
https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
the independent variable ; and
is the dependent variable
.
There are q dependent variables
and p independent variables
, and n sample points are observed, thus forming the data tables of
independent variables and dependent variables and .
Partial least squares regression extracts components and from and
respectively (that is, is the linear combination of ;
is the linear
combination of ). and should represent the data table and as well as
possible, and the component
of the independent variable has the strongest
explanatory ability to the component
of the dependent variable. After the first
components and are extracted, the regression of to and to is carried out
respectively. If the regression equation reaches satisfactory accuracy, the algorithm
terminates; Otherwise, the residual information after is interpreted by and
is
interpreted by
for the second round of component extraction. Such reciprocation,
until a more satisfactory accuracy can be achieved. If components are finally
extracted for , partial least squares regression will be implemented by implementing
regression of , and then expressed as regression equation about the
original variable ,
. This completes the modeling of partial least
squares regression.
4. ANALYSIS AND DISCUSSION
In recent years, smart home has gradually come into our lives and brought a lot of
positive impacts to our lives. Among them, smart home applications and services for
elderly families are gradually being emphasized and put on the agenda in China.
However, in the specific design planning of targeted applications, the designer
community is not always able to consider and analyze every factor. At the same time,
the overall process factors of the design are strongly non-linear in nature, and some of
them are even coupled and influenced by each other. In some cases, some of these
performance metrics are difficult to reconcile and may even conflict.
Imagine a real case scenario. When designing a smart home application and
service system for the elderly, it is difficult to improve the accuracy and total usage
frequency of the elderly group at the same time. As the memory of elderly users
decreases, some shortcut operations to improve the accurate usage rate often cause
tedious operation processes, which makes the total usage frequency decline.
Therefore, it is necessary to make a trade-off between the accuracy rate and the total
frequency of use in the design process. The weighting of the trade-off should be
matched with the more sensitive index factor in the overall benefit of the elderly group.
For the elderly, the error rate factor of smart home product usability is more important
than the usage efficiency factor, that is to say, the error rate should be reduced as
much as possible while meeting the basic usage efficiency of the elderly, which is
more compatible with the special physiological and psychological characteristics and
lifestyle of the elderly.
X{x1,x2, …xp}
q
Y{y1,y2, …yp}
{Y1…Yq}
{X1…Xp}
X= {X1…Xp}
Y= {Y1…Yq}
t1
u1
X
Y
t1
X1…XP
u1
Y1…YQ
t1
u1
X
Y
t1
u1
t1
u1
X
t1
Y
t1
X
t1
Y
t1
m
t1…tm
X
Yk's
t1…tm
Yk's
X1…Xm
k= 1,2…q
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4.1. DIVISION OF DIFFERENT TYPES OF FAMILIES
Therefore, in this paper, we envision an integrated nonlinear design that
incorporates the KANO model as well as a mathematical model of sensible
engineering coupled with design. Our design ideas are firstly divided into different
types of life in elderly households in specific situations. The key points in different
types of elderly households are screened out, and the key indicators with higher
sensitivity in the smart home applied to elderly households are matched with the key
features for the design. As we all know, the group of elderly people is generally
divided by age, and people over 65 years old are internationally classified as elderly
people. And in China, people over 60 years old are considered an elderly group.
However, classifying older people only by calendar age makes the group of older
people have great variability. Some groups are in the older age group by calendar
age, but they are still physically functioning and thinking fast. Therefore, their
physiological age does not exactly match the traditional elderly group. Therefore, the
target population cannot be divided by calendar age in general but should be defined
according to specific research needs. In this paper, we focus on the living
environment, interpersonal relationships, and autonomy of the elderly group, and the
division types and important characteristics are shown in Table 1. For the living
environment and interpersonal relationships of the elderly group, we have identified
four types: living with relatives, living alone, living with relatives, and living together.
For the main characteristics of type 1, we collected information about 572 elderly
households in a city in China based on an open questionnaire. The key characteristics
were summarized to focus on family relationships and autonomy. For example, elderly
families with relatives living together have good health status and relatively complex
family relationships. The self-care type of elderly families has better self-care abilities
and do not need more help and care from others.
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4.1. DIVISION OF DIFFERENT TYPES OF FAMILIES
Therefore, in this paper, we envision an integrated nonlinear design that
incorporates the KANO model as well as a mathematical model of sensible
engineering coupled with design. Our design ideas are firstly divided into different
types of life in elderly households in specific situations. The key points in different
types of elderly households are screened out, and the key indicators with higher
sensitivity in the smart home applied to elderly households are matched with the key
features for the design. As we all know, the group of elderly people is generally
divided by age, and people over 65 years old are internationally classified as elderly
people. And in China, people over 60 years old are considered an elderly group.
However, classifying older people only by calendar age makes the group of older
people have great variability. Some groups are in the older age group by calendar
age, but they are still physically functioning and thinking fast. Therefore, their
physiological age does not exactly match the traditional elderly group. Therefore, the
target population cannot be divided by calendar age in general but should be defined
according to specific research needs. In this paper, we focus on the living
environment, interpersonal relationships, and autonomy of the elderly group, and the
division types and important characteristics are shown in Table 1. For the living
environment and interpersonal relationships of the elderly group, we have identified
four types: living with relatives, living alone, living with relatives, and living together.
For the main characteristics of type 1, we collected information about 572 elderly
households in a city in China based on an open questionnaire. The key characteristics
were summarized to focus on family relationships and autonomy. For example, elderly
families with relatives living together have good health status and relatively complex
family relationships. The self-care type of elderly families has better self-care abilities
and do not need more help and care from others.
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Table 1. Basic types of elderly households and smart home service terminal design scheme
4.2. PLS ANALYSIS PROCESS
SmartPLS is an application running on the Java platform. It provides three different
internal weight modes: centroid weight, factor weight, and path weight, as well as the
default maximum number of iterations, iteration accuracy, and initial weight. It can
process the original sample data. Therefore, this paper uses smartPLS2.0 path
analysis software to calculate the model in this paper, obtain the path coefficient, and
investigate the hidden variables and the relationship between the hidden variables
and the measured variables. As for the path coefficient, it can directly reflect the
influence of each implied variable. The higher the value of the path coefficient, the
greater the direct influence of an implied variable on the implied variable pointed by
the arrow. The path coefficient between functionality and perceived effect is 0.0893,
Family Type Description Features
Design
Solutions
Features
Type 1: Relative
cohabitation
Living with their
children or
relatives
1. good for
health; 2.
relatively
complex
interpersonal
relationships；
Solution 1:
Infrastructure
distributed
intelligent
system
Visualization of
basic functions
such as air
conditioners,
refrigerators,
and switches for
TVs and other
devices
Type 2: Living
alone
Living alone,
largely
independent of
children or
relatives
1. need strong
self-care ability;
2. relatively
single family
relationship.
Solution 2:
Infrastructure
Intelligent
Integration
System
Such as air
conditioners,
refrigerators and
televisions are
controlled from a
single mobile
terminal.
Type 3: Relative
Neighbor
Live separately
from relatives,
but take care of
each other
1. maintain
separate and
independent
lives with
relatives; 2. visit
and care for
each other
frequently
Program 3:
Complete
intelligent
integrated
system for home
appliances and
facilities
All appliances
and other
devices are
integrated in the
mobile terminal
and have visual
interaction
function
Type 4:
Centralized
housing
Concentrated
residence in
service
institutions
1. the cost is
divided into
three ways:
government
funding, social
sponsorship and
personal
commitment; 2.
but the number
of institutions is
small and the
fees are high.
Intelligent
Integration
system with
KANO model
and perceptual
engineering
All air
conditioners,
refrigerators and
televisions are
controlled by a
general control
system.
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which is explained by the non-linear relationship between these two implied variables,
or the path coefficient is not obvious, indicating that the high functionality of smart
homes has little direct impact on the perception of the elderly. For Figure 2, through
comparative analysis, it is found that most of the observed variables have high Outer
weights. This shows that the observed variables can better reflect their corresponding
hidden variables. The specific results are shown in Table 2 and Figure 2.
Table2. Path coefficient
Figure 2. Outer weights of each factor
4.3. EVALUATION OF DIFFERENT CONVENTIONAL
PROGRAM APPLICATIONS
We matched the built smart home service terminal system incorporating the KANO
model as well as sensible engineering with the smart homes of 572 elderly
households in a city in China. Different smart home service terminal systems were
Elderly
satisfaction
Perceived
effect
Smart home
services
Reliability Functionality
Elderly
satisfaction
0.2641 0 0 0.4078 0.3192
Perceived
effect
0 0 0 0.1288 0.0893
Smart home
services
0 0 0 0 0.5351
Reliability 0 0 0 0 0.5763
Functionality 0 0 0 0 0.6242
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230
which is explained by the non-linear relationship between these two implied variables,
or the path coefficient is not obvious, indicating that the high functionality of smart
homes has little direct impact on the perception of the elderly. For Figure 2, through
comparative analysis, it is found that most of the observed variables have high Outer
weights. This shows that the observed variables can better reflect their corresponding
hidden variables. The specific results are shown in Table 2 and Figure 2.
Table2. Path coefficient
Figure 2. Outer weights of each factor
4.3. EVALUATION OF DIFFERENT CONVENTIONAL
PROGRAM APPLICATIONS
We matched the built smart home service terminal system incorporating the KANO
model as well as sensible engineering with the smart homes of 572 elderly
households in a city in China. Different smart home service terminal systems were
Elderly
satisfaction
Perceived
effect
Smart home
services
Reliability
Functionality
Elderly
satisfaction
0.2641
0
0
0.4078
0.3192
Perceived
effect
0
0
0
0.1288
0.0893
Smart home
services
0
0
0
0
0.5351
Reliability
0
0
0
0
0.5763
Functionality
0
0
0
0
0.6242
https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
applied to different basic types of elderly households in Table 1, and the application
accuracy and usage frequency percentages of each system design solution in
different types of elderly households were observed. The statistical results of the
application accuracy rate are shown in Figure 2(a), where it is observed that for type
1: elderly households with relatives living together, scheme 2 has more accurate
application feasibility compared to scheme 1. The application accuracy rate of
scenario 2 reached 88.98%, which is an improvement of 6.28% compared to scenario
1. However, in terms of application frequency in Figure 2(b), the usage frequency of
Scenario 2 decreased by 3.09%. This indicates that the combined accuracy of air
conditioners, refrigerators and TVs controlled centrally from mobile terminals has
more room for application in elderly households with young people living with them,
but is not often used by the elderly group when the young people are not at home. For
type 2: elderly households living alone, the application accuracy rate of scenario 2
decreases to 69.58%, which is 4.31% lower than that of scenario 1. At the same time,
the frequency of use of scenario 2 decreased by 26.55%. This indicates that such as
air conditioning, for the traditional elderly group living alone, the overly intelligent
design of Scenario 2 may make the application accuracy and frequency of use
significantly lower. As for Type 3: Relative Neighbors, the family relationship with
relatives living separately but taking care of each other makes the difference in
application accuracy between Scenario 1 and Scenario 2 not significant. It can be
surmised from the illustrations in Table 1 that frequent visits and mutual care among
relatives somewhat alleviate the degree of convenience in application for the elderly
group living alone. However, the frequency of use for scenario 2 still decreased by
about 20.33% compared to scenario 1. As for the type of Type 4: Concentration, the
application accuracy of Scenario 2 and Scenario 1 is not much different for the elderly
group living centrally in service institutions as the communication between the groups
becomes closer. It is 81.76% and 79.49%, respectively. And the gap between the
frequency of use of scenario 1 and scenario 2 also gradually decreases, with 81.76%
and 73.63%, respectively.
Figure 3. Evaluation of smart home service terminal design solutions for different types of
households
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4.4. EVALUATION OF IMPROVEMENT PROGRAM
APPLICATIONS
Finally, we improved and adapted Scheme 1 and Scheme 2, where we integrated
all appliances and other devices into a mobile terminal and made them visual and
interactive. The improved system design was named Scenario 3, which was applied to
different basic types of elderly households, and the results are shown in Figure 3. It is
observed that for type 1 households, scenario 3 has the most accurate application
feasibility of 92.91%. For type 2, 3, and 4 households, the feasibility of the application
of scenario 3 is higher than that of scenarios 1 and 2, with 73.58%, 76.9%, and
86.41%. In terms of frequency of use, Scenario 3 has a more friendly visual aid
understanding design compared to the equally intelligent Scenario 2, which helps the
elderly group to better apply the smart home service terminal system. Therefore, the
application frequency of Scenario 3 is higher than that of Scenario 2 in the elderly
households of Types 1, 2, 3, and 4.
Figure 4. Evaluation of smart home service terminal improvement solutions for different types
of households
5. CONCLUSION
In recent years, smart homes have gradually come into our lives and have brought
many positive impacts to our lives. However, in specifically targeted application design
planning, the designer community is not always able to consider and analyze every
factor. In this paper, we envision an integrated nonlinear design that combines the
KANO model and the mathematical model of sensual engineering with design
coupling. The application of different design solutions in different types of households
is evaluated after classifying the different types of life of elderly households in specific
situations. The conclusions are as follows:
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Ed.44 | Iss.12 | N.2 April - June 2023
232
4.4. EVALUATION OF IMPROVEMENT PROGRAM
APPLICATIONS
Finally, we improved and adapted Scheme 1 and Scheme 2, where we integrated
all appliances and other devices into a mobile terminal and made them visual and
interactive. The improved system design was named Scenario 3, which was applied to
different basic types of elderly households, and the results are shown in Figure 3. It is
observed that for type 1 households, scenario 3 has the most accurate application
feasibility of 92.91%. For type 2, 3, and 4 households, the feasibility of the application
of scenario 3 is higher than that of scenarios 1 and 2, with 73.58%, 76.9%, and
86.41%. In terms of frequency of use, Scenario 3 has a more friendly visual aid
understanding design compared to the equally intelligent Scenario 2, which helps the
elderly group to better apply the smart home service terminal system. Therefore, the
application frequency of Scenario 3 is higher than that of Scenario 2 in the elderly
households of Types 1, 2, 3, and 4.
Figure 4. Evaluation of smart home service terminal improvement solutions for different types
of households
5. CONCLUSION
In recent years, smart homes have gradually come into our lives and have brought
many positive impacts to our lives. However, in specifically targeted application design
planning, the designer community is not always able to consider and analyze every
factor. In this paper, we envision an integrated nonlinear design that combines the
KANO model and the mathematical model of sensual engineering with design
coupling. The application of different design solutions in different types of households
is evaluated after classifying the different types of life of elderly households in specific
situations. The conclusions are as follows:
https://doi.org/10.17993/3ctecno.2023.v12n2e44.220-235
1. The study population was defined according to specific research needs. In this
paper, we focus on the living environment, interpersonal relationships, and
autonomy of the elderly population and classify the types to match the
important characteristics. The types of living environment and interpersonal
relationships of the elderly group are cohabitation with relatives, living alone,
cohabitation with relatives, and centralized living.
2.
For elderly households of type 1 and 4, scenario 2 generally has more
accurate application feasibility than scenario 1. The maximum improvement of
Scenario 2 over Scenario 1 is 6.28%. However, for the application frequency,
the maximum decrease of 3.09% was observed for Scenario 2. For elderly
households living alone in Types 2 and 3, Scenario 2 has similar or worse
application feasibility than Scenario 1. This suggests that, as in the case of air
conditioning, overly intelligent designs may not be popular for the traditional
elderly group living alone.
3.
It is observed that for type 1 households, scenario 3 has the most accurate
application feasibility of 92.91%. For Type 2, 3, and 4 households, Option 3
has a higher application feasibility than Options 1 and 2, at 73.58%, 76.9%,
and 86.41%. In terms of frequency of use, Scenario 3 has a more friendly
visual aid understanding design compared to the equally intelligent Scenario 2,
which helps the elderly group to better use the smart home service terminal
system.
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