Is Pattern of Life" Size-Invariant? Recovering the Underlying Intent of a Walker from Human Walking Trajectory Data"

Investigating the association between streetscapes and human walking activities using Google Street View and human trajectory data

Transactions in GIS ◽

10.1111/tgis.12472 ◽

2018 ◽

Vol 22 (4) ◽

pp. 1029-1044 ◽

Cited By ~ 11

Author(s):

Xiaojiang Li ◽

Paolo Santi ◽

Theodore K. Courtney ◽

Santosh K. Verma ◽

Carlo Ratti

Keyword(s):

Human Walking ◽

Trajectory Data ◽

Google Street View ◽

Street View

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Feasibility Check of an Assist System Through the Simulation of Bipedal Walking Using a CPG

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2012.p0657 ◽

2012 ◽

Vol 24 (4) ◽

pp. 657-665

Author(s):

Tomohito Takubo ◽

◽

Yohei Fukano ◽

Kenichi Ohara ◽

Yasushi Mae ◽

...

Keyword(s):

Pressure Sensor ◽

Pattern Generator ◽

Pattern Generation ◽

Bipedal Walking ◽

Human Walking ◽

Trajectory Data ◽

Body Dimension ◽

Walking Pattern ◽

Walking Pattern Generation ◽

Mobile Base

A wearable mobile-base Walking Assist System (WAS) is simulated in this paper with the bipedal simulator we developed. The simulator employs a Central Pattern Generator (CPG) for bipedal walking pattern generation. The CPG-based walking pattern is one of the candidates for simulating human walking. Average Japanese body dimension data is applied to the bipedal model so that walking efficiency can be evaluated using the simulator. The effectiveness of the proposed simulator is confirmed by comparing real human walking and simulated walking in terms of the shape of the swing leg trajectory, data from the pressure sensor, and the feasibility of the prototype WAS. A prototype is developed and experimental results show the effectiveness of the bipedal simulator.

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Path Choice Behavior Analysis Based on Vehicle Trajectory Data

CICTP 2020 ◽

10.1061/9780784483053.070 ◽

2020 ◽

Author(s):

Zuyao Zhang ◽

Li Tang ◽

Yifeng Wang ◽

Xuejun Zhang

Keyword(s):

Behavior Analysis ◽

Choice Behavior ◽

Trajectory Data ◽

Vehicle Trajectory

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A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Informatica ◽

10.15388/informatica.2018.196 ◽

2019 ◽

Vol 30 (1) ◽

pp. 33-52 ◽

Cited By ~ 1

Author(s):

Pengfei HAO ◽

Chunlong YAO ◽

Qingbin MENG ◽

Xiaoqiang YU ◽

Xu LI

Keyword(s):

Compression Algorithm ◽

Trajectory Data ◽

Gps Trajectory Data ◽

Gps Trajectory

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Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

10.26434/chemrxiv.13332530 ◽

2020 ◽

Author(s):

Jiawei Peng ◽

Yu Xie ◽

Deping Hu ◽

Zhenggang Lan

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Collective Motion ◽

Principal Component ◽

Component Analysis ◽

Nonadiabatic Dynamics ◽

Trajectory Data ◽

Unsupervised Machine Learning ◽

Physical Knowledge ◽

Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

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