다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

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A review of wearable sensors and systems with application in rehabilitation.

Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm.

WiFall: Device-Free Fall Detection by Wireless Networks

Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.

Driving style recognition using a smartphone as a sensor platform

This paper introduces WiTrack, a system that tracks the 3D motion of a user from the radio signals reflected off her body. It works even if the person is occluded from the WiTrack device or in a different room. WiTrack does not require the user to carry any wireless device, yet its accuracy exceeds current RF localization systems, which require the user to hold a transceiver. Empirical measurements with a WiTrack prototype show that, on average, it localizes the center of a human body to within a median of 10 to 13 cm in the x and y dimensions, and 21 cm in the z dimension. It also provides coarse tracking of body parts, identifying the direction of a pointing hand with a median of 11.2°. WiTrack bridges a gap between RF-based localization systems which locate a user through walls and occlusions, and human-computer interaction systems like Kinect, which can track a user without instrumenting her body, but require the user to stay within the direct line of sight of the device.

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3D tracking via body radio reflections

Drunk driving, or officially Driving Under the Influence (DUI) of alcohol, is a major cause of traffic accidents throughout the world. In this paper, we propose a highly efficient system aimed at early detection and alert of dangerous vehicle maneuvers typically related to drunk driving. The entire solution requires only a mobile phone placed in vehicle and with accelerometer and orientation sensor. A program installed on the mobile phone computes accelerations based on sensor readings, and compares them with typical drunk driving patterns extracted from real driving tests. Once any evidence of drunk driving is present, the mobile phone will automatically alert the driver or call the police for help well before accident actually happens. We implement the detection system on Android G1 phone and have it tested with different kinds of driving behaviors. The results show that the system achieves high accuracy and energy efficiency.

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Mobile phone based drunk driving detection

Falls are a major health risk that diminish the quality of life among elderly people With the elderly population surging, especially with aging “baby boomers”, fall detection becomes increasingly important However, existing commercial products and academic solutions struggle to achieve pervasive fall detection In this paper, we propose utilizing mobile phones as a platform for pervasive fall detection system development To our knowledge, we are the first to do so We design a detection algorithm based on mobile phone platforms We propose PerFallD, a pervasive fall detection system implemented on mobile phones We implement a prototype system on the Android G1 phone and conduct experiments to evaluate our system In particular, we compare PerFallD's performance with that of existing work and a commercial product Experimental results show that PerFallD achieves strong detection performance and power efficiency

/pdf/perfalld-a-pervasive-fall-detection-system-using-mobile-5aagbr8gmg.pdf

PerFallD: A pervasive fall detection system using mobile phones

Falls are a major health risk that diminishes the quality of life among the elderly people. The importance of fall detection increases as the elderly population surges, especially with aging "baby boomers". However, existing commercial products and academic solutions all fall short of pervasive fall detection. In this paper, we propose utilizing mobile phones as a platform for developing pervasive fall detection system. To our knowledge, we are the first to do so. We propose PerFallD, a pervasive fall detection system tailored for mobile phones. We design two different detection algorithms based on the mobile phone platforms for scenarios with and without simple accessories. We implement a prototype system on the Android G1 phone and conduct extensive experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. The experimental results show that PerFallD achieves superior detection performance and power efficiency.

Mobile phone-based pervasive fall detection

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish nonservice-related information without establishing a connection. We propose a novel iterative Bloom filter (IBF) protocol that encodes topics to fit in SDP attributes and achieves a low false positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.

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E-SmallTalker: A Distributed Mobile System for Social Networking in Physical Proximity

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish non-service-related information without establishing a connection. We propose a novel iterative Bloom filter protocol that encodes topics to fit in SDP attributes and achieves a low false-positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.

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Jiangpeng Dai

Papers

Mobile phone based drunk driving detection

PerFallD: A pervasive fall detection system using mobile phones

Mobile phone-based pervasive fall detection

E-SmallTalker: A Distributed Mobile System for Social Networking in Physical Proximity

E-SmallTalker: A Distributed Mobile System for Social Networking in Physical Proximity