In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Ga...In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.展开更多
This paper presents a brief overview of several promising design technologies for high efficiency silicon-based radio frequency （RF） power amplifiers （PAs） as well as the use of these technologies in mobile broadb...This paper presents a brief overview of several promising design technologies for high efficiency silicon-based radio frequency （RF） power amplifiers （PAs） as well as the use of these technologies in mobile broadband wireless communications. Four important aspects of PA design are addressed in this paper. First, we look at class-E PA design equations and provide an example of a class-E PA that achieves efficiency of 65-70% at 2.4 GHz. Then, we discuss state-of-the-art envelope tracking （ET） design for monolithic wideband RF mobile transmitter applications. A brief overview of Doherty PA design for the next-generation wireless handset applications is then given. Towards the end of the paper, we discuss an inherently broadband and highly efficient class-J PA design targeting future multi-band multi-standard wireless communication protocols.展开更多
文摘In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.
文摘This paper presents a brief overview of several promising design technologies for high efficiency silicon-based radio frequency （RF） power amplifiers （PAs） as well as the use of these technologies in mobile broadband wireless communications. Four important aspects of PA design are addressed in this paper. First, we look at class-E PA design equations and provide an example of a class-E PA that achieves efficiency of 65-70% at 2.4 GHz. Then, we discuss state-of-the-art envelope tracking （ET） design for monolithic wideband RF mobile transmitter applications. A brief overview of Doherty PA design for the next-generation wireless handset applications is then given. Towards the end of the paper, we discuss an inherently broadband and highly efficient class-J PA design targeting future multi-band multi-standard wireless communication protocols.
