No abstract

Mobile computing is changing the landscape of clinical monitoring and self-monitoring. One of the major impacts will be in healthcare, where increase in number of sensing modalities is providing more and more information on the state of overall wellbeing, behaviour and health. There are numerous applications of mobile computing that range from wellbeing applications, such as physical fitness, stress or burnout up to applications that target mental disorders including bipolar disorder. Use of information provided by mobile computing devices can track the state of the subjects and also allow for experience sampling in order to gather subjective information. This paper reports on the results obtained from a medical trial with monitoring of bipolar disorder patients and how the episodes of the diseases correlate to the analysis of the data sampled from mobile phone acting as a monitoring device.

In contrast to high precision but highly obtrusive sensors, using smartphone sensors for measuring daily behaviours allowed us to quantify behaviour changes, relevant to occupational stress. Furthermore, we have shown that use of transfer learning to select data from close models is a useful approach to improve accuracy in presence of scarce data.

The level of participation in social interactions has been shown to have an impact on various health outcomes, while it also reflects the overall wellbeing status. In health sciences the standard practice for measuring the amount of social activity relies on periodical self-reports that suffer from memory dependence, recall bias and the current mood. In this regard, the use of sensor-based detection of social interactions has the potential to overcome the limitations of self-reporting methods that have been used for decades in health related sciences. However, the current systems have mainly relied on external infrastructures, which are confined within specific location or on specialized devices typically not-available off the shelf. On the other hand, mobile phone based solutions are often limited in accuracy or in capturing social interactions that occur on small time and spatial scales. The work presented in this paper relies on widely available mobile sensing technologies, namely smart phones utilized for recognizing spatial settings between subjects and the accelerometer used for speech activity identification. We evaluate the two sensing modalities both separately and in fusion, demonstrating high accuracy in detecting social interactions on small spatio-temporal scale.

Stress assessment is a complex issue and numerous studies have examined factors that influence stress in working environments. Research studies have shown that monitoring individuals' behaviour parameters during daily life can also help assess stress levels. In this study, we examine assessment of work-related stress using features derived from sensors in smartphones. In particular, we use information from physical activity levels, location, social-interactions, social-activity and application usage during working days. Our study included 30 employees chosen from two different private companies, monitored over a period of 8 weeks in real work environments. The findings suggest that information from phone sensors shows important correlation with employees perceived stress level. Secondly, we used machine learning methods to classify perceived stress levels based on the analysis of information provided by smartphones. We used decision trees obtaining 67.57% accuracy and 71.73% after applying a semi-supervised method. Our results show that stress levels can be monitored in unobtrusive manner, through analysis of smartphone data.