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Review Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues
, 2014
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Article Analysis of Android Device-Based Solutions for Fall Detection
, 2015
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FAST: A Fog Computing, Distributed Analytics-based Fall Monitoring System for Stroke
"... Abstract—Fog computing is a recently proposed computing paradigm that extends Cloud computing and services to the edge of the network. The new features offered by fog computing (e.g., distributed analytics and edge intelligence), if successfully applied for pervasive health monitoring applications, ..."
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Abstract—Fog computing is a recently proposed computing paradigm that extends Cloud computing and services to the edge of the network. The new features offered by fog computing (e.g., distributed analytics and edge intelligence), if successfully applied for pervasive health monitoring applications, has great potential to accelerate the discovery of early predictors and novel biomarkers to support smart care decision making in a connected health scenarios. While promising, how to design and develop real-word fog computing-based pervasive health monitoring system is still an open question. As a first step to answer this question, in this paper, we employ pervasive fall detection for stroke mitigation as a case in study. There are four major contributions in this paper: (1) to investigate and develop a set of new fall detection algorithms, including new fall detection algorithms based on acceleration magnitude values and non-linear time series analysis techniques, as well as new filtering techniques to facilitate fall detection process; (2) to design and employ a real-time fall detection system employing fog computing paradigm, which distribute the analytics throughout the network by splitting the detection task between the edge devices (e.g., smartphones attached to the user) and the server (e.g., servers in the cloud); (3) we carefully exam the special needs and constraints of stroke patients and propose patient-centered design that is minimal intrusive to patients. This type of patient-centered design is currently lacking in most of the existing work; and (4) our experiments with real-word data show that our proposed system achieves the high sensitivity (low missing rate) while it also achieves the high specificity (low false alarm rate). At the same time, the response time and energy consumption of our system are close to the minimum of the existing approaches. I.
Article User-Independent Motion State Recognition Using Smartphone Sensors
, 2015
"... Abstract: The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using acceleromete ..."
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Abstract: The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users ’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.
Can Context Awareness and Affective Computing inform Mobile Games User Research?
"... Mobile games can be used anytime and anywhere, therefore user context and behavior are important variables for evaluating the gameplay experience. In addition, the emotions that arise during gameplay constitute an important aspect in mobile game user research. This paper presents the position that i ..."
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Mobile games can be used anytime and anywhere, therefore user context and behavior are important variables for evaluating the gameplay experience. In addition, the emotions that arise during gameplay constitute an important aspect in mobile game user research. This paper presents the position that it is possible to collect information on both, by leveraging on the data available through mobile phone sensing, thus providing a non intrusive and versatile solution for automatic evaluation of contextual game user experiences. In the following, a conceptual framework to automatically collect and analyze information about context and user emotions is presented, with the purpose to study techniques for automatically inferring gameplay experience metrics such as flow, immersion and presence. This represents a work-in-progress study to demonstrate the feasibility of this research direction. Author Keywords user experience; ubiquitous computing; affective computing; ACM Classification Keywords H.5.2. [Information interfaces and presentation]:
