Zilu Liang

SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors

Getting enough quality sleep is a key part of a healthy lifestyle. Many people are tracking their sleep through mobile and wearable technology, together with contextual information that may influence sleep quality, like exercise, diet, and stress. However, there is limited support to help people make sense of this wealth of data, i.e., to explore the relationship between sleep data and contextual data. We strive to bridge this gap between sleep-tracking and sense-making through the design of SleepExplorer, a web-based tool that helps individuals understand sleep quality through multi-dimensional sleep structure and explore correlations between sleep data and contextual information. Based on a two-week field study with 12 participants, this paper offers a rich understanding on how technology can support sense-making on personal sleep data: SleepExplorer organizes a flux of sleep data into sleep structure, guides sleep-tracking activities, highlights connections between sleep and contributing factors, and supports individuals in taking actions. We discuss challenges and opportunities to inform the work of researchers and designers creating data-driven health and well-being applications.

Sleep tracking in the real world: a qualitative study into barriers for improving sleep

Wearable devices like Fitbit and Apple Watch provide convenient access to personal information about sleep habits. However, it is unclear if awareness of ones sleep habits also translates into improved sleep. Hence, we conducted an interview study with 12 people who track their sleep with Fitbit devices to investigate if they have managed to improve their sleep and to examine potential barriers for improving sleep. The participants reported increased awareness of sleep habits, but none of the participants managed to improve their sleep. They faced three barriers in improving their sleep: (1) not knowing what is normal sleep, (2) not being able to diagnose the reasons for a lack of sleep, and (3) not knowing how to act. This paper discusses how to address these barriers, both conceptually as well through design considerations - reference points, connections to lifestyle data, and personalised recommendations - to help users gain improvements in wellbeing from their personal data.

Is fitbit fit for sleep-tracking?: sources of measurement errors and proposed countermeasures

It is now easy to track ones sleep through consumer wearable devices like Fitbit from the comfort of ones home. However, compared to clinical measures, the data generated by such consumer devices is limited in its accuracy. The aim of this paper is to explore how users perceive accuracy issues, possible measurement errors and what can be done to address these issues. Through an interview study with 14 Fitbit users we identified three main sources of errors: (1) lack of definition of sleep metrics, (2) limitations in underlying data collection and processing mechanisms, and (3) lack of rigor in tracking approach. This paper proposes countermeasures to address these issues, both from the aspect of technological advancement and through engaging end-users more closely with their data.

A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining

With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.

Designing Intelligent Sleep Analysis Systems for Automated Contextual Exploration on Personal Sleep-Tracking Data

There are many sleep tracking technologies in the consumer market nowadays. These technologies offer rich functions ranging from sleep pattern tracking to smart alarm clock. However, previous study indicates that users find these technologies of little use in facilitating sleep quality improvement, as simply making a user aware of how poor his/her sleep is provides no actionable information on how to improve it. Armed with such understanding, we proposed an architecture for designing intelligent sleep analysis systems and developed a prototype called SleepExplorer to help users automatically analyse and visualize the interrelationship of his/her sleep quality and the context (i.e., psychological states, physiological states, lifestyle, and environment). Such contextual information is crucial in helping users understand what the potential reasons for their sleep problems might be. We conducted a 2-week field study with 10 diverse participants, learning that SleepExplorer help users make sense of their sleep-tracking data and reflect on their lifestyle, and that the system has potentially positive impact on sleep behaviour change.

Investigating Classroom Activities in English Conversation Lessons Based on Activity Coding and Data Visualization

Reflective teaching has become dominant paradigm in second language teacher education, as critical reflection helps teachers achieve a better understanding of teaching and learning processes. Critical reflection begins from classroom investigation. Several methods such as questionnaire, lesson report, teaching journal and audio/video recordings are widely used for classroom investigation. However, these methods are either susceptible to memory bias or are hard to be continued on a day-to-day basis. In this study, we proposed an approach for effective classroom investigation in second language education using activity coding in combination with data visualization technology. The proposed method consists of three stages. In the first stage, a smartphone application was used to record the activities that happen in a class following a slightly modified experience sampling method. In the second stage, the activities were quantified using our proposed 2-level activity coding scheme, and each activity was assigned a colour code. In the third stage, a data visualization tool D3.js was used to create heat maps of the classroom activities. We applied the proposed method to investigating the classroom activities in five English conversation lessons given by native-speaking teachers. The visual feedback led to the answering of some key questions that critical reflection aims to address, including teachers’ time management, lesson structure, and the characteristics of teacher-student interaction. Based on the results obtained, we highlighted the potential of the proposed approach for involving different sectors in second language education and pointed out the directions for future research.

Explicit exercise coaching for health promotion based on bio-mechanics and ontology engineering

Effective exercise coaching is critical for helping people master the correct forms of movements in order to gain the benefit of exercise. However, the potential ambiguity of verbal instructions in exercise coaching may become a hindrance to effective coaching. This study proposes a framework to support explicit and objective exercise coaching. We first present the two components of the proposed framework: (1) quantifying the differences between the correct and the wrong forms of a movement using bio-mechanics, and (2) modelling the sequence of muscle and joint activation in the correct form using ontology engineering. We then provide two examples of applying the proposed framework to exercise coaching on two basic movements. The ultimate aim of the study is to reduce unnecessary injury and to improve the quality of coaching services in the context of health promotion.

Health Promotion Community Support for Vitality and Empathy: Visualize Quality of Motion (QoM)

Nowadays approximately 30 % of the population is suffering from lifestyle-related diseases in Japan. Both individuals and the government are becoming more and more health-conscious and are taking various measures to improve personal health and to prevent lifestyle-related diseases. Among all the measures, improving trunk stability has been given special attention as it is vital for improving physical strength, preventing injury, and extending healthy life span. Many traditional trunk strength evaluation methods were designed to assess core muscle mass. Less emphasis, if any, was given to the stability of the trunk, which could be represented by the smoothness of trunk movement. In this paper, we proposed a new trunk torsion model for the purpose of evaluating two trunk torsion standard movements. We also developed a mobile application named “Axis Visualizer” based on the proposed trunk torsion model, which gives higher score to users who rotate the shoulders or hips smoothly with axis fixed and high frequencies. This application can support trainers and coaches to visualize the smoothness of trunk movement and to increase training outcome, as well as support health promotion community to easily evaluate the effectiveness of group exercise.

A Personalized Approach for Detecting Unusual Sleep from Time Series Sleep-Tracking Data

Nowadays emerging sleep-tracking technologies such as Fibit make it possible for individuals to collect personal sleep data. However, people find it difficult to gain insights from these data without proper analysis. The objective of this study was to investigate the possibility of establishing a sleep analysis approach that helps people detect their unusual sleep pattern by considering their own sleep baselines instead of the population average. The proposed approach was consisted of two steps. In the first step, the dimension of time series sleep data was reduced using permutation entropy. Following that, univariate outlier detection techniques were applied to detect unusual sleep patterns. We tested our approach on a real sleep tracking data set consisting of 35 days of time series data tracked using a Fitbit Charge HR. Depending on the univariate outlier detection technique used, the identified unusual sleep differed. We found that permutation entropy of a sleep time series was strongly correlated to the time that the user went to bed and weekly correlated to minutes asleep, but was not correlated to minutes awake, awakening count and sleep efficiency. Based on the analysis results, we pointed out the directions for future study on personal sleep data analysis.

Axis Visualizer: Enjoy Core Torsion and Be Healthy for Health Promotion Community Support

In Japan, the ratio of people with lifestyle-related diseases has increased to approximately 30%. Individuals as well as the Nation are getting more and more health-conscious, and special attention has been made to body trunk because it is vital for injury prevention, physical strength, and beauty. Various training methods have been proposed to increase the muscle mass of body trunk. However, for sports that emphasize somatoform such as dance, the strength of the trunk is mainly decided by smooth use of the trunk rather than its muscle mass. In this paper, in order to evaluate the use of the trunk torsion movement, we proposed a new trunk torsion model for the purpose of evaluating two trunk torsion standard movements. We also developed a mobile application named “Axis Visualizer” based on the proposed trunk torsion model analyzing sensor data in the device. Axis Visualizer generates higher score when a user rotates the shoulders or hips smoothly with axis fixed and high frequencies. This application can support trainers and coaches to visualize the use of customers’ trunk and to increase the training effect.