论文信息 - Analyzing bilinear neural networks with new curve fitting for application to human motion analysis

Analyzing bilinear neural networks with new curve fitting for application to human motion analysis

This paper presents a method for the analysis of bilinear neural networks (BNNs) with time delay. A BNN system was proposed to analyze a weak nonlinear model. To analyze this system, we need to solve the equations for the system. In this paper, we propose a method that determines whether it is possible to solve the equations analytically. If this is possible, we propose a concrete method for solving them. This paper also presents new optimum curve fitting for data sampled at discrete times, such as data outputted from a sensor in a mobile phone, and a new BNN system for curve fitting. Moreover, this paper presents one example where the BNN system for curve fitting and other BNN systems are applied to the analysis of human motion.

Toshio Ito | Fumio Nagashima | Yosuke Senta

[1] Sung-Bae Cho,et al. Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer , 2011, HAIS.

[2] Fumio Nagashima. Bilinear Time Delay Neural Network System for Humanoid Robot Software , 2007 .

[3] Gary M. Weiss,et al. Activity recognition using cell phone accelerometers , 2011, SKDD.

[4] Jun Yang,et al. Toward physical activity diary: motion recognition using simple acceleration features with mobile phones , 2009, IMCE '09.

[5] Norbert Gyorbíró,et al. An Activity Recognition System For Mobile Phones , 2009, Mob. Networks Appl..

[6] Lin Sun,et al. Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations , 2010, UIC.

[7] David W. Mizell,et al. Using gravity to estimate accelerometer orientation , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..