**6. Experiment results**

The effectiveness of the posture training tool and the proposed CSPT framework is deliberately reviewed and validated throughout the experiments. **Table 1** shows the experiment Collaborative, Social-Networked Posture Training with Posturing Monitoring and Biofeedback http://dx.doi.org/10.5772/intechopen.74791 51


**Table 1.** Experimental results.

**5.1. Experiment method**

50 Biofeedback

Our design of experiments has four objectives as follows:

• to study the effects of biofeedback on posture training.

October 24th to 28th, 2016, detailed as follows:

**5.2. Experiment data collection**

**6. Experiment results**

knowledge to each other during the entire experiment period.

• to study the effects of peer influences on posture training; and

• to validate the effectiveness of the developed posture monitoring wearables; • to validate the effectiveness of the proposed CSPT posture training framework;

The following three scenarios are designed for the experiments as follows:

**Scenario S1:** Both the biofeedback and social network functions are DISABLED. **Scenario S2:** Biofeedback is ENABLED but social network function is DISABLED. **Scenario S3:** Both the biofeedback and social network functions are ENABLED.

We conducted the experiments in each student's home with the guidance of students' parents. Each student was equipped with a wearable training headset, a training lumbar belt and the posture training CSPT App. They were asked to wear the training headset and lumbar belt for at least 60 min a day. The experiments were carried out from October 17th to 21st and from

Scenario S1 was conducted first on October 17th and 18th. Scenario S2 was followed and conducted on October 19th, 20th and 21st. Scenarios S3 was conducted from October 24th to 28th. Before each experiment, all the teens do not know about any details of the three scenarios. For the first 2 days (October 17th and 18th), each teen was asked to wear the headset and belt without knowing anything about biofeedback and social network functions. They were told and became aware of the biofeedback music, voice and vibration in Day 3 (October 19th). On October 24th, the teens were asked to download App's social network function where they can glance at their friends' training scores. During the experiments, all the teens knew who of their peers are participating in the experiments. Teens can share their observations and

The experiment data of time-series posture data are generated by the wearable devices, collected by the CSPT App and sent to the CSPT Cloud. The experiment data are transferred through a RESTful [49] protocol-based application programming interface (API) to the Cloud. We utilize Node.js [50], featured by its fast, scalable and easy implementation for API, mobile, web and Internet of Things (IoT), in implementing the mobile API to access the data and services.

The effectiveness of the posture training tool and the proposed CSPT framework is deliberately reviewed and validated throughout the experiments. **Table 1** shows the experiment

**Figure 6.** Comparison of results without and with biofeedback.

results of percentages of good posture (%) and the total wearing times (min) of the six students. Comparison of average good posture percentages between Scenarios S1 (without biofeedback) and S2 (with biofeedback) is depicted in **Figure 6**. Note that biofeedback does facilitate the increase of percentages of good posture for all the teens significantly. Similar results on the effectiveness of biofeedback on forward head posture improvements have been

Collaborative, Social-Networked Posture Training with Posturing Monitoring and Biofeedback

http://dx.doi.org/10.5772/intechopen.74791

53

**Figure 7** demonstrates the results of percentage (%) of time in good posture of each teen in Scenarios S1, S2 and S3. Obviously, teens are encouraged by peer influences from their social network and social support in maintaining good posture. Results of wearing times of each teen in Scenarios S1, S2 and S3, as depicted in **Figure 8**, comply with the same observations. As compared to teens' wearing times in Scenarios S1 and S2, peer competition and encouragement do promote longer wearing times in Scenario S3, respectively. The proposed collaborative, social-networked approach is effective for teens of peer influences to be supported, encouraged and collaborative to achieve the goals of maintaining good

This chapter develops a posture training tool to invoke people' awareness of their bad posture so that they can timely improve their poor posture and maintain good posture while sitting. A collaborative, social-networked posture training (CSPT) approach is used in the design of the posture training tool. Three technologies of real-time posture monitoring, biofeedback and collaborative social networks are adopted in the CSPT framework, which is composed of a sophisticated posture monitoring headset, a training lumbar belt, a smartphone, a socialnetwork CSPT App and cloud services. Experiments are conducted to validate the effectiveness of the proposed CSPT framework with a group of six middle-school teenagers. We design three testing scenarios to explore the effects of biofeedback and social networking. Our experiment results indicate that the proposed CSPT framework with posture monitoring and biofeedbacks are effective in increasing the percentage of time in good posture for each teen in the group. Experiment results also show that peer influences and social support are crucial and effective to encourage the teens in maintaining good posture and being willing to wear-

Some mHealth apps like iOS Health [51] and Google Fit [52] and other mobile wearable fitness devices [53] are available in the market. To our best knowledge, none of them implement social networks or social media functions. Future research may develop an integrated social networks platform to provide health services or bio-sensing functions of heartbeats, blood glucose and electrocardiogram (EKG). Although the motivation of this research is preliminarily for posture training while sitting, the applications of the proposed CSPT framework and the devices are not limited to sitting posture only. They can be applied to other workplace environments, sports and performance psychology. Further research directions may also extend the developed biofeedback techniques to applications like driver sleepiness detection,

observed and reported in the literature [11, 45].

ing the posture training tool for longer time.

core stability training, therapeutic, fitness and so on.

posture.

**7. Conclusion**

**Figure 7.** Results of good posture percentages of time (%).

**Figure 8.** Results of wearing time (minutes).

results of percentages of good posture (%) and the total wearing times (min) of the six students. Comparison of average good posture percentages between Scenarios S1 (without biofeedback) and S2 (with biofeedback) is depicted in **Figure 6**. Note that biofeedback does facilitate the increase of percentages of good posture for all the teens significantly. Similar results on the effectiveness of biofeedback on forward head posture improvements have been observed and reported in the literature [11, 45].

**Figure 7** demonstrates the results of percentage (%) of time in good posture of each teen in Scenarios S1, S2 and S3. Obviously, teens are encouraged by peer influences from their social network and social support in maintaining good posture. Results of wearing times of each teen in Scenarios S1, S2 and S3, as depicted in **Figure 8**, comply with the same observations. As compared to teens' wearing times in Scenarios S1 and S2, peer competition and encouragement do promote longer wearing times in Scenario S3, respectively. The proposed collaborative, social-networked approach is effective for teens of peer influences to be supported, encouraged and collaborative to achieve the goals of maintaining good posture.
