Analysis and Modeling of Temporal Features in Data Streams from Multiple Wearable Devices

Time is a vitally important issue in the coordination of multiple wearable devices. Theoretically, wearable applications should require data streams to be synchronized with the necessary degree of precision. However, in the available applications, this critical issue has not been well considered. Actually, time discrepancies exist among data streams, resulting in certain decrease of data analysis and fusion accuracy. The study of time discrepancy is rarely found in the literature, and there is no specific model to describe temporal features. In this dissertation, we first analyze several temporal issues in multi-wearable system and the source of time discrepancy. Then, by taking into account temporal features, we propose two typical models, which provide statistical methods for describing time discrepancy and its distribution. Furthermore, the accuracy of the models is verified by a set of experiments. Finally, we demonstrate the application of the proposed models through a case study, in which the adaptive frequency strategy is adopted. Experimental results show that the strategy can not only guarantee the completeness of the data, but also reduce redundancy compared with the static frequency method. Our models and experiments of time discrepancy can be a basis for further research on the time synchronization of data from multiple wearable devices

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use

Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called "MobiAct", which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with mbientlabs wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.Comment: 7 page