Collaborative, Social-networked Posture Training (CSPT) through

Head-and-Neck Posture Monitoring and Biofeedbacks

Da-Yin Liao

Straight & Up Intelligent Innovations Group Co., San Jose, California, U.S.A.

Keywords: Posture Training, Head and Neck Posture, Collaborative Training, Cloud Computing, Social Network, Peer-

Influenced Learning.

Abstract: This research is motivated by the need of a tool to train elementary/middle-school students to maintain good

posture while sitting. We propose a collaborative, social-networked approach to design the posture training

tool so that students can be aware of and timely improve their bad posture. The posture training tool is

composed of a wearable posture training headset, a social-network App, and cloud storage and computing

services. The wearable training headset is equipped with real-time sensors to monitor head and neck

postures. The App provides biofeedback mechanisms of sound, voice, or vibration, to remind the students

when their postures become bad. In the App, students and their guardians can review the posture history and

the statistical analysis of their postures. Students can glance over their friends’ posture performance.

Through this collaborative, social-networked approach, students of peer influences are thus encouraged to

maintain good postures.

1 INTRODUCTION

Chiropractors and spinal specialists worldwide have

seen an increase in the number of young patients

experiencing “text neck” (Fishman 2017). Text

neck and its syndrome are threatening to turn

today’s teenagers into a generation of hunchbacks.

Originated by Dr. D. Fishman in Year 2008, the

phrase “text neck” is to describe the repeated stress

injury to the body caused by poor posture and

brought on largely by overuse of all digital devices.

Poor posture not only causes structural and spinal

problems to people, but it can also lead to cognitive

problems that may incur anxiety and depression

(Pop, 2016), especially to those emotional and

sensitive people like teenagers.

The rapid rise of poor posture in kids is the curse

of modern era. Digitally savvy teens are likely the

most affected because they use smartphones, tablets

and computers the most. Moving the head forward

and bending down in a hunched position for typing

or gaming imposes high pressure in the spine. The

pressure increases drastically with every degree of

head/neck flexing. For head position of bending 45

degrees, the head exerts 22.5 Kg, comparing with

5.5 Kg in its normal position (Hansraj, 2014).

For older people, prolonged poor posture could

result in permanent pains in their necks and

shoulders, but the severity can be minimized for kids

and adolescents through neck and shoulder stretches

and exercise. Frequent breaks with simple neck and

shoulder stretches can improve blood flow and

relieve tension. Keeping good posture, with the

body aligned in a neutral position, is the key to avoid

straining the neck and shoulder. Posture awareness

and timely improvement are important to stretch and

relax the tense muscles.

However, actions always speak louder than

words. It is not easy to deal with children on the

brink of adolescent rebellion. Poor posture is not an

immediate damage or hazard to people. Its harmful

effects are not obvious and can only appear over a

long period of time. Neck and shoulder pains may

be annoying but texting or gaming are more

attractive to teens.

Awareness of poor posture is not easy, not to say

to do timely improvement to correct poor posture.

Parents or teachers always exhort to sit or stand up

straight, stop slouching and to straighten the

shoulders. However, they cannot stay aside with

kids all the time and most teens do not like so. The

critical point is teens’ self-awareness of their poor

posture and the motivation to improve the poor

posture.

158

Liao, D-Y.

Collaborative, Social-networked Posture Training (CSPT) through Head-and-Neck Posture Monitoring and Biofeedbacks.

DOI: 10.5220/0006358301580165

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 3, pages 158-165

ISBN: 978-989-758-249-3

Parents and other significant adults may serve as

important models in the childhood, but all become

less effects when kids grow up. Adolescents are

most influenced by their peers. They adopt or

mimic many behaviors of their peers in some social

settings in order to be accepted by their peers. Teens

need encouragement and recognition from their

peers. The imitation to peers can have positive or

negative influences on teens, depending on several

factors like what characteristics of the individuals is,

how responsible the group members are, and so on.

The influences can be strong or weak, heavily

relying on the trust to each other and the competition

among the peers of the group (Kirke, 2006; Lenhart

et al, 2015).

The 21st-century-born teens are sometimes

called the “i-Generation,” representing the types of

technologies, such as iPhone, iPod, iTune and Wii,

being heralded by children and adolescents. They

are digital natives, grow up with Internet connection,

and are surrounded with technologies all days long.

The Internet, mobile phones, and social network

software all become an integral part of their lives

and are increasingly relevant to their learning and

social networking. While particular software

websites may rise and fade, the i-Generation

continue to engage through social network software

for identity formation, status negation, and peer-to-

peer sociality (Mason & Rennie, 2008).

The term social network refers to the web of

social relationships that surround individuals

(Heaney & Israel, 2008). In this research, it refers to

the linkages between teens whose closeness is

embedded in a informal group in which group

members can provide social functions like

emotional, instrumental, informational, and appraisal

supports and collaboration to individuals. Social

networks and social support can have positive

effects on physical, mental, and social health (Ayubi

et al, 2014). Collaborative social networks open up

new ways to work with peers and improve

engagement and effectiveness to activities (Webb,

1989).

This research aims to develop a tool to train

elementary/middle-school students to maintain good

posture while sitting. We propose a collaborative,

social-networked approach to design the posture

training tool, which is composed of a wearable,

posture training headset, a social-network app

(application program), and cloud storage and

computing services. The wearable training headset

is equipped with real-time sensors to monitor head

and neck postures. An App is developed to provide

biofeedback mechanisms of sound, voice, or

vibration, to remind the students when their postures

become bad. In the App, students and their

guardians can review the history of posture and

conduct statistical analysis of their postures. Some

metrics are defined to indicate the behavior of their

sitting postures. The App provides functions to

glance over their friends’ performance. Through the

collaborative, social-networked sharing and

competition, students of peer influences are thus

encouraged to maintain their good postures.

The remainder of this paper is organized as

follows. Section 2 proposes the design of the

Collaborative, Social-networked, Posture Training

(CSPT) framework. The posture training tool that

adopts biofeedback techniques in helping correct

poor head and neck posture is developed in Section

3. Section 4 presents the design of experiments to

validate the effectiveness of collaborative, social-

networked posture training. Section 5 discusses the

experiment results. Finally, in Section 6 concluding

remarks are made with some future research

directions.

2 DESIGN OF COLLABORATIVE,

SOCIAL-NETWORKED

POSTURE TRAINING (CSPT)

FRAMEWORK

Design of the Collaborative, Social-networked

Posture Training (CSPT) framework is based on

three technologies of (1) real-time, head-and-neck

posture monitoring, (2) biofeedback mechanisms,

and (3) social networks and collaboration.

Monitoring of head and neck postures requires

techniques of sensing the movement and measuring

the displacement of head and neck positions in real

time, with respect to their neural positions.

Transformation among many coordinate systems is

needed to reflect head and neck postures. Many

researches have attempted to define the normal and

correct posture of head, neck and shoulder, from

various different points of view (Liao, 2016). Most

researchers and practitioners adopt the idea along the

neutral spine position — ears aligned with the

shoulders and the shoulder blades retracted. This

research uses head-and-neck angles — the angles

between true vertical (or horizontal) and a line

connecting C7 vertebra and tragus (the cartilaginous

protrusion in front of the ear hole) as the head-and-

neck posture.

The idea behind the biofeedback technology is

that, by harnessing the power of the mind and

becoming aware of what’s going on inside the body,

people can get more control over those normally

involuntary functions. Biofeedback promotes

Collaborative, Social-networked Posture Training (CSPT) through Head-and-Neck Posture Monitoring and Biofeedbacks

159

relaxation and can help relieve a number of

conditions that are related to stress.

Social connection and the quality of the

relationships are important for physical health. The

use of social networks and collaboration technology

is appealing for three reasons. First, the i-Generation

teens spend hours a day with their peers using social

networks. Second, individuals can use social

networks to share their performance in maintaining

good posture to their peers. Third, peers give their

appraisals and encouragement through social

networks where positive social competition and

support are timely and amplified. Collaboration

among the peers is thus achieved.

The CSPT framework consists of posture

monitoring wearables, handheld devices, a social-

network app, and cloud storage and computing

services. Its architecture is shown as Figure 1.

Figure 1: Architecture of the CSPT Framework.

The embedded-system-based device is devised to

monitor neck-and-head posture in real time.

Handheld devices like smartphones, and desktop or

laptop computers are used to provide interface to the

device. They also provide biofeedback and social

networking functions.

3 BIOFEEDBACK POSTURE

TRAINING TOOL

3.1 Biofeedback

Biofeedback is an autonomic feedback mechanism

that gains awareness of physiological functions from

the information measured by instruments (Schwartz

& Andrasik, 2005; McKee, 2008). Biofeedback

monitors and uses physiologic information (e.g.

hearing, vision, feeling) to teach people to change

specific physiologic functions (e.g., posture)

accordingly. Figure 2 depicts the biofeedback

mechanism in a posture control loop. In the control

loop, posture is monitored and biofeedback to the

sensory nervous system with sound, flashing light,

or vibration in order to notify to change and improve

the posture accordingly.

Figure 2: The Biofeedback Mechanism.

As one of the popular clinical therapy

approaches in healthcare, biofeedback aims at

helping people take responsibility for the cognitive,

emotional, and behavioral changes needed to affect

healthy physiologic change. A biofeedback

mechanism includes both a biofeedback process and

the instruments used in the process. A biofeedback

process is a learning process where physiologic

information is monitored and fed back through the

biofeedback instruments. Biofeedback instruments

monitor the physiologic processes, measure and

transform the measurement data into auditory,

visual, or vibrating signals in a simple, direct, and

immediate way. The goal of biofeedback is to

enable and change the physiologic process of the

people, guided by the information provided by the

biofeedback instruments.

Successful design of a biofeedback mechanism

should consider the following factors:

▪ whether the individual have the capacity to

respond;

▪ how the individual is motivated to learn;

▪ how the individual is positively reinforced to

learn; and

▪ whether the individual is given accurate

information about the results of the learning

effort.

We design the biofeedback mechanism by using

sounds, music, flashing light, and vibration functions

of smartphones to timely notify the teens when their

bad posture is detected.

3.2 Real-time Posture Training with

Biofeedback

This research adopts the direct feedback learning

mechanism that the individual gains control of the

head and neck posture after receiving the feedback

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information. The biofeedback instruments monitor

real-time head-and-neck posture and determine head

craning forward or hanging downward. Using the

biofeedback process with sound, light, or vibration,

people receives alert or warning when their head and

neck posture is determined as bad. The biofeedback

mechanism guides the people to identify, change and

correct the head and neck posture to right positions.

There are five steps in the operation scenarios of

the CSPT system, described as follows:

1. Initialization: Teen wears the posture training

headset and invokes the smartphone App.

2. Monitoring: The posture monitoring sensor

continues to monitor the posture status and send

streaming data to the receiving App or gadgets.

3. Biofeedback: The App or gadgets biofeedback

to the teen with predefined sound, music,

vibration, or flashing light, when poor posture is

determined. The teen responds and corrects the

head and neck posture to the good position

timely.

4. Storing and Analysis: The posture data are

compiled and transferred to the cloud for storing

and further analysis. The teen can query and

review their own historical behaviors and

analytic information in their smartphone or

smartwatch.

5. Sharing and Research: Notifications and

analytic data can be shared to teen’s parents,

guardian, or friends. The data stored in the

cloud, without violating privacy and security

policies, can be shared to doctors, researchers,

or public health workers to improve healthcare

and welfare.

3.3 System Description

The CSPT system consists of three subsystems –

Posture Monitoring subsystem, App subsystem, and

Cloud Services subsystem. Each subsystem is

described as below:

Posture Monitoring Subsystem

The Posture Monitoring subsystem is an

embedded system with dedicated hardware of

accelerometer functions to detect and transmit the 3-

axis acceleration values of the device continuously.

We adopt a 32-bit ARM Cortex M0 microprocessor

(“Cortex-M0 Processor,” 2017) as the core of the

embedded system. The microprocessor equips with

AES 128-bit encryption.

A 3-axis accelerometer of ultra-low-power, high-

performance, MEMS (Micro-Electro Mechanical

System) motion sensor is used in the embedded

system to detect the attitude of the posture

monitoring hardware, i.e., its pitch, roll, and yaw.

Its function is to measure 3-axis accelerations with

16-bit data output rates in hundreds Hertz. The

analog measured accelerometer readings are first

converted into digital signals and then sent to the

microprocessor via serial communication interfaces

of I2C (Inter-Integrated Circuit) or SPI (Serial

Peripheral Interface) to calculate the tilt angle of the

posture monitoring hardware.

App Subsystem

The App subsystem plays many roles, from

sensor data gateway and processing, biofeedback

initiating, data feeder to the cloud, and presentation

or rendering the historical and analytic posture

information, to social networking GUI.

As the core in the App subsystem, the App

executes and manages tasks of receiving, processing

and further transmitting head-and-neck angles,

determining to notify when biofeedback is needed,

rendering the analytic data streams from the cloud,

managing identity and access control, and doing

encryption/decryption of the data and user ID, as the

platform for chatting, messaging, and file sharing of

social network functions.

Not every teen’s smartphone is fresh and up-to-

date. Some teens use parents’ used smartphones

whose OS or interfaces are unable to upgrade.

Development of the App subsystem involves various

and many smartphone models from different

manufacturers with different OS and software

versions and is so challenging and tedious as

compared to the development of other subsystems.

Cloud Services Subsystem

The Cloud Services subsystem stores and syncs

user data, exports the App analytic information, and

supports social network services. We adopt the web

services with the on-demand computing platform

offered by a well-known Internet service provider

that operates from 12 geographical regions across

the world.

Several clustered servers are equipped in the

cloud subsystem for web, applications and database

server functions. NoSQL databases are adopted to

manage user data, head-and-neck angles, and

analytic data.

3.4 Wearable Training Headset

The wearable training headset is an earhook device

that measures and transmits head-and-neck angles to

the receiving smartphone. The wearable training

headset is composed of four components, including

accelerometer, microprocessor, power supply, and

Collaborative, Social-networked Posture Training (CSPT) through Head-and-Neck Posture Monitoring and Biofeedbacks

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wearable earhooks and lanyard. Monitoring head-

and-neck posture is accomplished by calculating

head-and-neck posture angles, as described below.

In this research we adopts the C7-tragus angle,

a.k.a. the cranial-vertebral angle, as depicted in

Figure 3, as the metric to measure head-and-neck

postures. A comfortable head-and-neck angle is

about 30° in a normal sitting posture and about 40°

in using computer. A posture below 25° or beyond

50° is considered poor and need-to-be-corrected.

Figure 3: Coordinate Systems with Origins at C7, Sensor

and Center of the Head, respectively.

Let G = [ G

]

be an acceleration vector,

where G

, G

y',

and G

z’

represent the acceleration in

x’-, y’- and z’-axis, respectively, and G

x’

y’

z’

≠ 0.

The tilt angle along the z’-axis, ρ, can be calculated

by the following equation:

ρ = cos

-1

(

’

/sqrt(G

’

)

where cos

-1

(.) is inverse cosine and the sqrt(.) the

square root functions, respectively. By measuring

the amount of accelerations from the accelerometer,

the above equation gives the angle how the sensor is

tilted at with respect to the earth.

The tilt angle is further transformed into the

people’s coordinate system (x, y, z), with its origin

at the center of the head in Figure 3. It is then

converted into the posture angle. The stream of the

posture angles forms a set of time-series data which

are processed by the meta-heuristic based on

Kalman filter and fuzzy logics algorithms

(Dakhlallah, 2007).

The posture angle sensor is put in a find plastic

enclosure that is attached to a lanyard and connects

to an creative designed earhook in both sides. Figure

4 shows the wearable training headset.

Figure4: Wearable Training Headset.

3.5 Social Network App

As the core in the smartphone subsystem, the App is

the only entrance for people to use the biofeedback

system to prevent their neck and shoulder pains.

The App provides registration function for new user

to register in a simple step and start to use the

system. It manages user identity and access control.

The App is capable of doing encryption/decryption

of the data and ID.

The App is a notifier for people to receive alert

or warning so that they can correct the poor posture

immediately. The App executes and manages tasks

of receiving, processing and further transmitting

head-and-neck angles, determining to play sound or

music, or start vibrating when biofeedback is

needed.

The App is also an important interface to people.

It provides friendly GUI (Graphical User Interface)

and renders the analytic data streams from the cloud.

User can watch the status of their current head and

neck posture and realize how good or poor their

head and neck posture is. People can glance over

their friends’ posture performance. People can also

query the analytic statistics of good posture (%),

wearing time, and response time of historical data in

different time scales spreading from day, week,

month, to year. Figure 5 depicts an example of the

analytic report in the day.

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Figure 5: An Example GUI of the App.

4 DESIGN OF EXPERIMENTS

This section reports on the use of the proposed

posture training framework with a group of six

teenagers in a middle school in San Jose, California,

U.S.A. The families in this group are similar in

various respects, including their race of Asian

Americans, socioeconomic status, occupational

status, family size, housing, geographic location,

ethics and morals. The teenagers are all 8th graders

in the school and form peer ties. They study and

play in very close proximity to each other of the

group. They are denoted as “C”, “E”, “GC”, “GW”,

“H” and “L”, by the first letter(s) of their names,

respectively.

4.1 Experiment Method

Our objectives in the design of experiments have

four folds as follows:

1. to validate the effectiveness of the developed

posture training tool;

2. to validate the effectiveness of the proposed

CSPT framework;

3. to study the effects of peer influences on head-

and-neck posture training; and

4. to study the effects of biofeedback on head-and-

neck posture training.

The following three scenarios are designed for the

experiments:

Scenario I

Both the Biofeedback and social network

functions are DISABLED.

Scenario II

Biofeedback is ENABLED but social network

function is DISABLED.

Scenario III

Both the biofeedback and social network

functions are ENABLED.

The experiments were carried out in each teen’s

home during October 17th~21st and 24th~28th,

Year 2016, with guidance of teens’ parents. Each

teenage is provided with a wearable training headset

and the Posture Training App. They are asked to

wear the training headset for at least sixty minutes a

day. The experiments were carried out as follows:

We tested Scenario I first in October 17th and 18th.

The tests of Scenario II were followed in October

19th, 20th and 21st. And the tests of Scenario III in

October 24th ~ 28th. The teens know nothing about

the details of the three scenarios before the tests.

That is, at first, the teens were told to wear the

headset without knowing anything about

biofeedback and social network functions for the

first two days. They were aware of the biofeedback

signals in Day 3 (October 19th). And on October

24th, they were told to download App’s new

function to glance at their friends’ training scores.

Before the experiments, all the teens knew who of

their peers will participate the experiments. And

they can share experiences and observations to each

other during the test period.

4.2 Data Collection

Collection of the time-series posture data is achieved

automatically via the wearable training headset, the

App, and the Cloud services.

5 EXPERIMENT RESULTS

The effectiveness of the posture training tool and the

proposed CSPT framework are deliberately

reviewed and validated throughout the experiments.

Tables 1 and 2 show the experiment results of good

posture (%) and wearing time (min) of the six teens,

respectively. and 2 show the experiment results of

Collaborative, Social-networked Posture Training (CSPT) through Head-and-Neck Posture Monitoring and Biofeedbacks

163

good posture (%) and wearing time (min) of the six

teens, respectively.

Figure 6 depicts the comparison of average good

posture percentages of Scenario I versus Scenario II,

i.e., without versus with biofeedback. Note that the

biofeedback does help increase good posture

percentages of time for all the teens significantly.

Similar results of biofeedback effectiveness on

forward head posture have been observed and

reported in the literature (Kim et al, 2011).

Results of good posture percentages of time (%)

of each teen in Scenarios I, II and III are shown in

Figure 7. Peer influences from teens’ social network

and social support do encourage teens in maintaining

good posture. Same observations can be found in

the results of wearing times. Figure 8 depicts the

results of wearing times of each teen in Scenarios I,

II & III, where peer competition and encouragement

does urge longer wearing times in Scenario III, as

compared to their wearing times in Scenarios I & II,

respectively. In summary, through the collaborative,

Table 1: Experiment Results of Good Posture (%).

Date

GoodPosture(%)

C E GC GW H L Scenario

10/17 65 85 80 78 74 87

10/18 68 88 82 76 77 85

10/19 89 95 96 95 90 98

II 10/20 90 92 95 92 95 99

10/21 85 95 90 93 92 98

10/24 92 100 98 100 98 100

III

10/25 95 100 99 100 99 100

10/26 94 100 100 99 100 100

10/27 99 100 100 100 100 100

10/28 99 100 99 100 100 100

Table 2: Experiment Results of Wearing Time (min).

Date

WearingTime(min)

C E GC GW H L

Scenario

10/17 60 61 60 63 61 74

10/18 61 64 60 62 61 77

10/19 60 72 62 62 62 75

10/20 61 68 68 61 64 82

10/21 61 69 65 60 61 86

10/24 62 113 85 96 89 127

III

10/25 87 151 90 111 120 149

10/26 102 155 121 160 158 156

10/27 115 169 162 176 159 180

10/28 152 192 170 179 167 190

social-networked approach, the teens of peer

influences are supported, encouraged, and

collaborative to achieve the goals of maintaining

good posture.

Figure 6: Results with vs. without Biofeedback (BF).

Figure 7: Results of Good Posture (%).

Figure 8. Results of Waiting Time (min).

6 CONCLUDING REMARKS

This paper develops a head-and-neck posture

training tool to invoke students’ awareness of their

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164

bad posture so that they can timely improve their

poor posture and maintain good posture while

sitting. A collaborative, social-networked approach

is used and three technologies of real-time posture

monitoring, biofeedback, and collaborative social

networks are adopted, which consists of an

embedded-system-based posture monitoring headset,

a handheld device, a social-network App, and cloud

services. The proposed framework is tested with a

group of six middle-school best-friend teens for ten

days. Three scenarios are designed to validate the

effectiveness of the proposed approach and tools.

Experiment results show that the proposed

framework and the developed posture training tools

are very effective in increasing teens’ good posture

percentage of time. Social support and peer

influences are important and effective to encourage

the peers in maintaining good posture and being

willing to spend longer time in wearing the tool.

There are some mHealth apps, like iOS Health

and Google Fit, and mobile wearable fitness devices

available on the market. Only few of them have

social networks or social media functions. There

still needs an integrated social network platform to

accommodate the bio-sensing functions for heart

beats, EKG (electrocardiogram), blood glucose, and

so on, as an integrated health service. Future

research may consider the posture training of lower

backs where disorders of the lumbar spinal and its

surrounding muscle, nerves, bones, discs or tendons

usually cause severe lower back pains due to poor

posture.

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