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Accidental falls are the main factors that endanger the health of elderly people in modern society. Timely and effective fall detection and alarm can reduce the risk of falls. With the increasing of the aging society, the design and development of the fall detection system which is portable, accurate and real-time, has gradually become one of the most urgent needs of the community. With the rapid...
Human action recognition plays an important role in E-health, such as risk assessment, disease treatment, rehabilitation and so on. We proposes a mobile phone-based internet of things method for human action recognition. In the work, data are collected from a smart phone worn on the waist and transmitted to the application server on the internet. The application server program cuts these data into...
Road accidents are one of the universal problems faced by people all around the world. A major reason is reckless driving on speed breakers and ditches causing high causalities due to no warning signs, lack of street lights, substandard construction etc. There has been some research related to finding a Computer Science oriented solution but no substance in terms of a product. We present an Android...
With the spread of high-performance mobile terminals that include sensors, participatory sensing is attracting attention for collecting sensor information using smartphones. Although there are two key advantages to this approach, its low cost and wide range of sensing, they come at the tradeoff of both the battery and storage. Therefore, efficient collection is required. There is also a challenge...
In-vehicle smartphones contain rich information of driving and therefore can be utilized to establish driver behavior profile. The GPS data indicates the occurrence of driving events like gas-hit, brake-hit, turn-right, turn-left, and forward-driving. The inertial measurement unit (IMU) data, provides good estimations of vehicle lateral, longitudinal, and vertical accelerations and rotations. One...
This paper presents a classification method for smartphone users mobility data in urban environments according to the used transportation mode. This classification is possible among several different transportation modes and using only the location data from user's mobility. Among the methods applied, includes data mining with machine learning techniques for the inference. This paper also presents:...
The availability of a rich variety of sensors in smart mobile devices has enabled today's software to be situational-aware and to learn about surrounding environment. We explore a novel generation of mobile malware, which utilizes this situational awareness and can attack a mobile device carried by a specific person, or people matching a specific set of criteria. The behavior and threat posed by StuxMob...
One in five Americans is lonely and loneliness disproportionately affects senior citizens and international students. In this paper, we propose Socialoscope, a smartphone app that passively senses user loneliness from their communication and interaction patterns (e.g. calls, SMS, browsing patterns and social media usage), while factoring in different personality types. Data was gathered from 9 international...
A low-cost but high-accuracy mechanism for detecting falls is critical for many health and safety applications, including caring for the elderly. Existing approaches are unduly expensive and sensitive to user physique and biometrics. Additionally, most approaches were developed using limited, simulated fall data and often perform poorly in field tests. To resolve these issues, in this paper we propose...
Lung cancer is the number one cause of cancer deaths. Many early stage lung cancer patients have a resectable tumor, however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Pulmonary function is assessed via spirometry and diffusion capacity. If these are below a certain threshold, cardiopulmonary exercise testing (CPET) is recommended. CPET...
In this paper, massive multiple-input multiple-output (massive MIMO) and full-duplex communications (FDC) are considered together for high efficiency next generation wireless LAN (WLAN) systems such as IEEE 802.11ax or beyond. The proposed scheme allocates different carrier sensing thresholds by applying the joint spatial division and reuse (JSDR) scheme and is able to enhance the efficiency of multi-user...
With the Internet of Things, sensors are becoming ever more ubiquitous, but interacting with them continues to present numerous challenges, particularly for applications running on resource-constrained devices like smartphones. The SOUL abstractions in this paper address two issues faced by such applications: (1) access to sensors with the levels of convenience needed for their ubiquitous, dynamic...
Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these,...
In the last decade, consumer electronic devices such as smartphones, are packaged with small cameras, gyroscopes, and accelerometers, all sensors allowing autonomous deployment of aerial robots in GPS-denied environments. Our previous work [1], demonstrated the feasibility of using smartphones for autonomous flight. In many applications, there is a large interest to the use multiple autonomous aerial...
Depression is a serious health disorder. In this study, we investigate the feasibility of depression screening using sensor data collected from smartphones. We extract various behavioral features from smartphone sensing data and investigate the efficacy of various machine learning tools to predict clinical diagnoses and PHQ-9 scores (a quantitative tool for aiding depression screening in practice)...
Depression is the most common mental disorder and is negatively impactful to individuals and their social networks. Passive sensing of behavior via smartphones may help detect changes in depressive symptoms, which could be useful for tracking and understanding disorders. Here we look at a passive way to detect changes in depressive symptoms from data collected by users’ smartphones. In particular,...
Traditional STEM education models in electrical engineering and computer science rely on structured classes, laboratories, and textbooks to transfer key concepts. Even though this process meets most of the ABET objectives, it does not respond well to current workforce needs that require widely accessible programs that will provide a large pool of graduates with STEM backgrounds, analytical and programming...
Phone-based surveys are increasingly being used in healthcare settings to collect data from potentially large numbers of subjects, e.g., to evaluate their levels of satisfaction with medical providers, to study behaviors and trends of specific populations, and to track their health and wellness. Often subjects respond to such surveys once, but it has become increasingly important to capture their...
With the spread of high-performance mobile terminals that include sensors, participatory sensing is attracting attention for collecting sensor information using smartphones. Although there are two key advantages to this approach, its low cost and wide range of sensing, they come at the tradeoff of both the battery and storage. Therefore, efficient collection is required. There is also a challenge...
As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution...
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