Relationship analysis between body flexion angles and smartphone tilt during smartphone use

https://doi.org/10.1016/j.ergon.2020.103034Get rights and content

Highlights

  • The cervical and thoracic flexion angles were greater when standing.

  • The lumbar and overall flexion angles were greater when sitting.

  • The cervical flexion had significantly higher value when playing a game than when reading news.

  • Classification accuracy was 100% in tasks and 79.2%-83.3% in posture × task by smartphone tilt.

  • The estimation algorithm showed a major correlation between smartphone tilt and the flexion angle.

Abstract

Although smartphones are used as essential devices in everyday life, many users are exposed to joint diseases owing to prolonged use. The objectives of this study were to analyze how posture and smartphone tasks affect various body flexion angles and develop an algorithm to classify posture/task and estimate body flexion angles using smartphone tilt data. Eighteen participants performed two tasks (playing a game and reading news) in two postures (sitting and standing) in a laboratory environment. The three-axis orientation data (azimuth, pitch, and roll) of the smartphone and the participants’ body flexion angles were measured simultaneously. This study found that the cervical, thoracic, lumbar, and overall flexion angles were all statistically significantly different depending on the posture of the smartphone user, and the cervical flexion angle was significantly different depending on the task. Furthermore, task and task × posture can be classified with high accuracy based on smartphone tilt data, and tilt data had a high correlation with body flexion angles. Relevance to industry: The results of this study can be used as a reference for designing various products and interfaces for neck health. The results can be applied as a smartphone alarm or a built-in application, which can inform the user of the need to stretch his or her neck.

Introduction

With the development of digital devices, smartphones have become more popular and are used as essential devices in everyday life. For example, according to eMarketer (2018), average smartphone usage per day is 3 h and 35 min for US users, increasing by more than 11 min per year. However, in proportion to the increasing usage time of digital devices, many users are likely to develop joint diseases due to long-term digital device use (Shim, 2012; Ko et al., 2013; Namwongsa et al., 2018). Traditionally, research has been conducted on joint diseases related to the use of desktop personal computers (PCs) and laptop PCs (Asundi et al., 2010; Young et al., 2012; Albin and McLoone, 2014; Ning et al., 2015; Chiang and Liu, 2016; Yoon and Lee, 2018; Ailneni et al., 2019). Recently, joint diseases related to handheld devices, such as smartphones, have been actively studied (Kietrys et al., 2015). To prevent joint diseases caused by the use of digital devices, it is important to maintain proper head and neck posture during their use (Mousavi-Khatir et al., 2018).

In general, increased time spent using a mobile device is associated with increased muscle fatigue (Shin and Kim, 2014; Kim, 2015). There are numerous reports that measure muscle activity to identify muscle fatigue of mobile device users. Straker et al. (2008) measured muscle activity of the neck and upper limbs during reading with computers, and Douglas and Gallagher (2017) measured muscle activity of the head and neck during a reading session on a tablet computer. Studies have also been conducted on the muscle activity of the neck and trunk during smartphone gaming (Park et al., 2017), muscle activity of the neck during texting (Areeudomwong et al., 2018), and muscle activity of the neck, middle trapezius, deltoid, and latissimus dorsi during smartwatch and smartphone use (Jin et al., 2019).

When using mobile devices, users experience body flexion, and flexion angle and muscle fatigue are highly correlated (Straker et al., 2009; Shin and Kim, 2014). Several studies have confirmed neck or head flexion angles based on various tasks and postures of mobile device users. Indeed, users have different flexion angles based on the type of work performed on the mobile device (Table 1).

However, previous studies have focused on the flexion angle of specific parts of the body, especially neck and head flexion. In recent years, various studies were performed on the flexion angles of various body parts, including head and trunk-arm (Jin et al., 2019), lateral trunk flexion (Asante et al., 2018), and cervical, thoracic, and lumbar vertebrae (Xie et al., 2018). The study by Xie et al. (2018) was based on the flexion angle of various body parts but focused only on typing in the sitting position. In this study, we examined the cervical, thoracic, lumbar, and overall flexion angles in two postures (sitting and standing) and for two tasks (playing a game and reading news).

Table 1 shows the main studies measuring body flexion angle based on various tasks and postures when using a smartphone or tablet PC. In the case of task and posture, there were many similar reports; however, the measured body flexion differed based on the research purpose. Even if the same term was used, flexion had different definitions in different reports. For example, the angle between the line joining the C7 spinous process to the tragus and the vertical line was defined as head flexion in Jin et al. (2019) and Lee et al. (2015), and neck flexion in Guan et al. (2016). In future studies on flexion, a comparative analysis should be conducted by examining the definitions used in previous reports. In this study, cervical, thoracic, and lumbar flexion were measured, and the overall flexion was calculated, with the definition of each flexion presented in Section 2.5.

To measure the body flexion angle, sensors, such as an inertial measurement unit (IMU) motion sensor attached directly to the body, are required. If the body flexion angle can be deduced from the tilt of the smart device, then it could be useful in terms of the potential application to prevent future joint disease. Asundi et al. (2012) confirmed that head and neck flexion decreased as the tilt of the notebook computer increased. Meanwhile, Albin and McLoone (2014) confirmed that neck flexion decreased as the tilt of the tablet device increased; However, although the study confirmed the association between the tilt of the device and body flexion, modeling studies were not conducted to deduce body flexion angles. In the current study, we developed a model that can deduce the flexion angle for each posture and task using three-axis orientation data (azimuth, pitch, and roll) collected from smart devices.

The objectives of this study were to analyze the ways in which posture and task during smartphone use affect various body flexion angles (cervical, thoracic, lumbar, and overall flexion) and to verify the possibility of classifying, or estimating, the body flexion angle from three-axis orientation sensor data related to smartphone tilt. Additionally, a t-test was performed to analyze whether there were gender differences in body flexion angles. The results of this study are expected to be useful as reference material for neck joint disease and the prevention of diseases caused by smartphone use.

Section snippets

Participants

There were eighteen participants (9 women and 9 men). Two subjects were left-handed, and the remaining 16 subjects were right-handed. All participants were accustomed to and had no difficulty using smartphones. The age of the participants ranged from 20 to 30 years, and the mean age was 23.6 ± 2.3 years. All participants were college students or graduate students, and none of them reported having any musculoskeletal disorders. Before data collection, each participant provided a written informed

Flexion angle

Cervical flexion based on the smartphone task ranged from 7.14° (10th percentile, sitting) to 43.61° (90th percentile, standing) (average, 25.15 ± 10.72°) when playing a game and from 5.69° (10th percentile, sitting) to 51.47° (90th percentile, standing) (average, 21.41 ± 12.04°) when reading news. Cervical flexion based on posture ranged from 5.69° (10th percentile, reading news) to 37.41° (90th percentile, playing a game) (average, 20.60 ± 9.66°) when sitting and from 8.04° (10th percentile,

Flexion angles according to different postures and tasks

This study measured cervical, thoracic, lumbar, and overall flexion during two tasks (playing a game and reading news) in two postures (sitting and standing). Our results indicate that the cervical and thoracic flexion angles based on posture were greater in the standing position than in the sitting position, and the lumbar, and overall flexion angles based on posture were greater in the sitting than in the standing position; both results were statistically significant. These results are

Conclusion

In this study, we confirmed the flexion angle of the body when using a smartphone and confirmed the possibility of developing an algorithm to classify and estimate the posture and task of the user from the smartphone tilt data. According to the posture of the smartphone use, the flexion angles of the cervical and thoracic muscles were greater in the standing posture, while those of the lumbar were greater in the sitting posture. All results were statistically significant. The flexion angle,

CRediT authorship contribution statement

Hyun K. Kim: Conceptualization, Writing - review & editing, Validation. Nahyeong Kim: Data curation, Writing - original draft. Jaehyun Park: Conceptualization, Supervision, Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The present Research has been conducted by the Research Grant of Kwangwoon University in 2018. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2018R1C1B6008848).

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