Omni Vision Technologies推出医疗影像传感器产品组合中的最新成员OV6930。OV6930是一种SquareGA方形图形阵列（400×400像素）CMOS影像传感器，光学格式为1／10英寸，封装尺寸为1．8mm×1．8mm，这使其成为要求外径小于2．8m...Omni Vision Technologies推出医疗影像传感器产品组合中的最新成员OV6930。OV6930是一种SquareGA方形图形阵列（400×400像素）CMOS影像传感器，光学格式为1／10英寸，封装尺寸为1．8mm×1．8mm，这使其成为要求外径小于2．8mm的摄像头应用（如用于微创医疗程序的医用内窥镜）的理想选择。展开更多
This paper reviews recent advances in radar sensor design for low-power healthcare,indoor real-time positioning and other applications of IoT.Various radar front-end architectures and digital processing methods are pr...This paper reviews recent advances in radar sensor design for low-power healthcare,indoor real-time positioning and other applications of IoT.Various radar front-end architectures and digital processing methods are proposed to improve the detection performance including detection accuracy,detection range and power consumption.While many of the reported designs were prototypes for concept verification,several integrated radar systems have been demonstrated with reliable measured results with demo systems.A performance comparison of latest radar chip designs has been provided to show their features of different architectures.With great development of IoT,short-range low-power radar sensors for healthcare and indoor positioning applications will attract more and more research interests in the near future.展开更多
Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which pro...Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which provide a convenient and inexpensive way to collect motion data of users. Such rich, continuous motion data provide great potential for remote healthcare and decease diagnosis. Information processing algorithms play the critical role in these approaches, which is to extract the motion signatures and to access different kinds of judgements. This paper reviews key algorithms in these areas. In particular, we focus on three kinds of applications: 1) gait analysis; 2) fall detection and 3) sleep monitoring. They are the most popular healthcare applications based on the inertial data. By categorizing and introducing the key algorithms, this paper tries to build a clear map of how the inertial data are processed; how the inertial signatures are defined, extracted, and utilized in different kinds of applications. This will provide a valuable guidance for users to understand the methodologies and to select proper algorithm for specifi c application purpose.展开更多
文摘This paper reviews recent advances in radar sensor design for low-power healthcare,indoor real-time positioning and other applications of IoT.Various radar front-end architectures and digital processing methods are proposed to improve the detection performance including detection accuracy,detection range and power consumption.While many of the reported designs were prototypes for concept verification,several integrated radar systems have been demonstrated with reliable measured results with demo systems.A performance comparison of latest radar chip designs has been provided to show their features of different architectures.With great development of IoT,short-range low-power radar sensors for healthcare and indoor positioning applications will attract more and more research interests in the near future.
基金supported in part by National Natural Science Foundation of China Grant 61202360, 61033001, 61361136003the National Basic Research Program of China Grant 2011CBA00300, 2011CBA00302
文摘Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which provide a convenient and inexpensive way to collect motion data of users. Such rich, continuous motion data provide great potential for remote healthcare and decease diagnosis. Information processing algorithms play the critical role in these approaches, which is to extract the motion signatures and to access different kinds of judgements. This paper reviews key algorithms in these areas. In particular, we focus on three kinds of applications: 1) gait analysis; 2) fall detection and 3) sleep monitoring. They are the most popular healthcare applications based on the inertial data. By categorizing and introducing the key algorithms, this paper tries to build a clear map of how the inertial data are processed; how the inertial signatures are defined, extracted, and utilized in different kinds of applications. This will provide a valuable guidance for users to understand the methodologies and to select proper algorithm for specifi c application purpose.
