Inter-Node Distance Estimation from Multipath Delay Differences of Channels to Observer Nodes

We study the estimation of distance d between two wireless nodes by means of their wideband channels to a third node, called observer. The motivating principle is that the channel impulse responses are similar for small d and drift apart when d increases. Following this idea we propose specific distance estimators based on the differences of path delays of the extractable multipath components. In particular, we derive such estimators for rich multipath environments and various important cases: with and without clock synchronization as well as errors on the extracted path delays (e.g. due to limited bandwidth). The estimators readily support (and benefit from) the presence of multiple observers. We present an error analysis and, using ray tracing in an exemplary indoor environment, show that the estimators perform well in realistic conditions. We describe possible localization applications of the proposed scheme and highlight its major advantages: it requires neither precise synchronization nor line-of-sight connection. This could make wireless user tracking feasible in dynamic indoor settings.Comment: To appear at IEEE ICC 2019. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Light Weight Smartphone Based Human Activity Recognition System with High Accuracy

With the pervasive use of smartphones, which contain numerous sensors, data for modeling human activity is readily available. Human activity recognition is an important area of research because it can be used in context-aware applications. It has significant influence in many other research areas and applications including healthcare, assisted living, personal fitness, and entertainment. There has been a widespread use of machine learning techniques in wearable and smartphone based human activity recognition. Despite being an active area of research for more than a decade, most of the existing approaches require extensive computation to extract feature, train model, and recognize activities. This study presents a computationally efficient smartphone based human activity recognizer, based on dynamical systems and chaos theory. A reconstructed phase space is formed from the accelerometer sensor data using time-delay embedding. A single accelerometer axis is used to reduce memory and computational complexity. A Gaussian mixture model is learned on the reconstructed phase space. A maximum likelihood classifier uses the Gaussian mixture model to classify ten different human activities and a baseline. One public and one collected dataset were used to validate the proposed approach. Data was collected from ten subjects. The public dataset contains data from 30 subjects. Out-of-sample experimental results show that the proposed approach is able to recognize human activities from smartphones’ one-axis raw accelerometer sensor data. The proposed approach achieved 100% accuracy for individual models across all activities and datasets. The proposed research requires 3 to 7 times less amount of data than the existing approaches to classify activities. It also requires 3 to 4 times less amount of time to build reconstructed phase space compare to time and frequency domain features. A comparative evaluation is also presented to compare proposed approach with the state-of-the-art works

A Novel Approach to Complex Human Activity Recognition

Human activity recognition is a technology that offers automatic recognition of what a person is doing with respect to body motion and function. The main goal is to recognize a person\u27s activity using different technologies such as cameras, motion sensors, location sensors, and time. Human activity recognition is important in many areas such as pervasive computing, artificial intelligence, human-computer interaction, health care, health outcomes, rehabilitation engineering, occupational science, and social sciences. There are numerous ubiquitous and pervasive computing systems where users\u27 activities play an important role. The human activity carries a lot of information about the context and helps systems to achieve context-awareness. In the rehabilitation area, it helps with functional diagnosis and assessing health outcomes. Human activity recognition is an important indicator of participation, quality of life and lifestyle. There are two classes of human activities based on body motion and function. The first class, simple human activity, involves human body motion and posture, such as walking, running, and sitting. The second class, complex human activity, includes function along with simple human activity, such as cooking, reading, and watching TV. Human activity recognition is an interdisciplinary research area that has been active for more than a decade. Substantial research has been conducted to recognize human activities, but, there are many major issues still need to be addressed. Addressing these issues would provide a significant improvement in different aspects of the applications of the human activity recognition in different areas. There has been considerable research conducted on simple human activity recognition, whereas, a little research has been carried out on complex human activity recognition. However, there are many key aspects (recognition accuracy, computational cost, energy consumption, mobility) that need to be addressed in both areas to improve their viability. This dissertation aims to address the key aspects in both areas of human activity recognition and eventually focuses on recognition of complex activity. It also addresses indoor and outdoor localization, an important parameter along with time in complex activity recognition. This work studies accelerometer sensor data to recognize simple human activity and time, location and simple activity to recognize complex activity

Developing Accessible Collection and Presentation Methods for Observational Data

The processes of collecting, cleaning, and presenting data are critical in ensuring the proper analysis of data at a later date. An opportunity exists to enhance the data collection and presentation process for those who are not data scientists – such as healthcare professionals and businesspeople interested in using data to help them make decisions. In this work, creating an observational data collection and presentation tool is investigated, with a focus on developing a tool prioritizing user-friendliness and context preservation of the data collected. This aim is achieved via the integration of three approaches to data collection and presentation.In the first approach, the collection of observational data is structured and carried out via a trichotomous, tailored, sub-branching scoring (TTSS) system. The system allows for deep levels of data collection while enabling data to be summarized quickly by a user via collapsing details. The system is evaluated against the stated requirements of usability and extensibility, proving the latter by providing examples of various evaluations created using the TTSS framework.Next, this approach is integrated with automated data collection via mobile device sensors, to facilitate the efficient completion of the assessment. Results are presented from a system used to combine the capture of complex data from the built environment and compare the results of the data collection, including how the system uses quantitative measures specifically. This approach is evaluated against other solutions for obtaining data about the accessibility of a built environment, and several assessments taken in the field are compared to illustrate the system’s flexibility. The extension of the system for automated data capture is also discussed.Finally, the use of accessibility information for data context preservation is integrated. This approach is evaluated via investigation of how accessible media entries improve the quality of search for an archival website. Human-generated accessibility information is compared to computer-generated accessibility information, as well as simple reliance on titles/metadata. This is followed by a discussion of how improved accessibility can benefit the understanding of gathered observational data’s context