Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data

Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the duration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes

Hybrid system of emotion evaluation in physiotherapeutic procedures

Nowadays, the dynamic development of technology allows for the design of systems based on various information sources and their integration into hybrid expert systems. One of the areas of research where such systems are especially helpful is emotion analysis. The sympathetic nervous system controls emotions, while its function is directly reflected by the electrodermal activity (EDA) signal. The presented study aimed to develop a tool and propose a physiological data set to complement the psychological data. The study group consisted of 41 students aged from 19 to 26 years. The presented research protocol was based on the acquisition of the electrodermal activity signal using the Empatica E4 device during three exercises performed in a prototype Disc4Spine system and using the psychological research methods. Different methods (hierarchical and non-hierarchical) of subsequent data clustering and optimisation in the context of emotions experienced were analysed. The best results were obtained for the k-means classifier during Exercise 3 (80.49%) and for the combination of the EDA signal with negative emotions (80.48%). A comparison of accuracy of the k-means classification with the independent division made by a psychologist revealed again the best results for negative emotions (78.05%)

Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: from Exercise to Functional Activity

Clinicians tailor intervention in chronic pain rehabilitation to movement related self-efficacy (MRSE). This motivates us to investigate automatic MRSE estimation in this context towards the development of technology that is able to provide appropriate support in the absence of a clinician. We first explored clinical observer estimation, which showed that body movement behaviours, rather than facial expressions or engagement behaviours, were more pertinent to MRSE estimation during physical activity instances. Based on our findings, we built a system that estimates MRSE from bodily expressions and bodily muscle activity captured using wearable sensors. Our results (F1 scores of 0.95 and 0.78 in two physical exercise types) provide evidence of the feasibility of automatic MRSE estimation to support chronic pain physical rehabilitation. We further explored automatic estimation of MRSE with a reduced set of low-cost sensors to investigate the possibility of embedding such capabilities in ubiquitous wearable devices to support functional activity. Our evaluation for both exercise and functional activity resulted in F1 score of 0.79. This result suggests the possibility of (and calls for more studies on) MRSE estimation during everyday functioning in ubiquitous settings. We provide a discussion of the implication of our findings for relevant areas

Evaluating the Reproducibility of Physiological Stress Detection Models

Recent advances in wearable sensor technologies have led to a variety of approaches for detecting physiological stress. Even with over a decade of research in the domain, there still exist many significant challenges, including a near-total lack of reproducibility across studies. Researchers often use some physiological sensors (custom-made or off-the-shelf), conduct a study to collect data, and build machine-learning models to detect stress. There is little effort to test the applicability of the model with similar physiological data collected from different devices, or the efficacy of the model on data collected from different studies, populations, or demographics. This paper takes the first step towards testing reproducibility and validity of methods and machine-learning models for stress detection. To this end, we analyzed data from 90 participants, from four independent controlled studies, using two different types of sensors, with different study protocols and research goals. We started by evaluating the performance of models built using data from one study and tested on data from other studies. Next, we evaluated new methods to improve the performance of stress-detection models and found that our methods led to a consistent increase in performance across all studies, irrespective of the device type, sensor type, or the type of stressor. Finally, we developed and evaluated a clustering approach to determine the stressed/not-stressed classification when applying models on data from different studies, and found that our approach performed better than selecting a threshold based on training data. This paper\u27s thorough exploration of reproducibility in a controlled environment provides a critical foundation for deeper study of such methods, and is a prerequisite for tackling reproducibility in free-living conditions

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \& Participation}, \emph{(2) Health Surveillance \& Data Collection}, and \emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.Comment: Submitted to a journal for revie

Personal informatics and negative emotions during commuter driving:Effects of data visualization on cardiovascular reactivity & mood

Mobile technology and wearable sensors can provide objective measures of psychological stress in everyday life. Data from sensors can be visualized and viewed by the user to increase self-awareness and promote adaptive coping strategies. A capacity to effectively self-regulate negative emotion can mitigate the biological process of inflammation, which has implications for long-term health. Two studies were undertaken utilizing a mobile lifelogging platform to collect cardiovascular data over a week of real-life commuter driving. The first was designed to establish a link between cardiovascular markers of inflammation and the experience of anger during commuter driving in the real world. Results indicated that an ensemble classification model provided an accuracy rate of 73.12% for the binary classification of episodes of high vs. low anger based upon a combination of features derived from driving (e.g. vehicle speed) and cardiovascular psychophysiology (heart rate, heart rate variability, pulse transit time). During the second study, participants interacted with an interactive, geolocated visualisation of vehicle parameters, photographs and cardiovascular psychophysiology collected over two days of commuter driving (pre-test). Data were subsequently collected over two days of driving following their interaction with the dynamic, data visualization (post-test). A comparison of pre- and post-test data revealed that heart rate significantly reduced during episodes of journey impedance after interaction with the data visualization. There was also evidence that heart rate variability increased during the post-test phase, suggesting greater vagal activation and adaptive coping. Subjective mood data were collected before and after each journey, but no statistically significant differences were observed between pre- and post-test periods. The implications of both studies for ambulatory monitoring, user interaction and the capacity of personal informatics to enhance long-term health are discussed