Pedestrian motion state classification using pressure sensors

2016 ◽  
Author(s):  
Birendra Ghimire ◽  
Christian Nickel ◽  
Jochen Seitz
Keyword(s):  
Pressure Sensors ◽  
Motion State ◽  
State Classification

scholarly journals Human Motion State Recognition Based on Flexible, Wearable Capacitive Pressure Sensors

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1219
Author(s):  
Qingyang Yu ◽  
Peng Zhang ◽  
Yucheng Chen
Keyword(s):  
Flexible Substrate ◽  
Recognition Rate ◽  
Pressure Sensors ◽  
Recognition System ◽  
Human Motion ◽  
Body Motion ◽  
Capacitive Pressure Sensor ◽  
Wearable Sensor ◽  
State Recognition ◽  
Motion State

Human motion state recognition technology based on flexible, wearable sensor devices has been widely applied in the fields of human–computer interaction and health monitoring. In this study, a new type of flexible capacitive pressure sensor is designed and applied to the recognition of human motion state. The electrode layers use multi-walled carbon nanotubes (MWCNTs) as conductive materials, and polydimethylsiloxane (PDMS) with microstructures is embedded in the surface as a flexible substrate. A composite film of barium titanate (BaTiO3) with a high dielectric constant and low dielectric loss and PDMS is used as the intermediate dielectric layer. The sensor has the advantages of high sensitivity (2.39 kPa−1), wide pressure range (0–120 kPa), low pressure resolution (6.8 Pa), fast response time (16 ms), fast recovery time (8 ms), lower hysteresis, and stability. The human body motion state recognition system is designed based on a multi-layer back propagation neural network, which can collect, process, and recognize the sensor signals of different motion states (sitting, standing, walking, and running). The results indicate that the overall recognition rate of the system for the human motion state reaches 94%. This proves the feasibility of the human motion state recognition system based on the flexible wearable sensor. Furthermore, the system has high application potential in the field of wearable motion detection.

scholarly journals Stereo camera visual SLAM with hierarchical masking and motion-state classification at outdoor construction sites containing large dynamic objects

2021 ◽  
Vol 35 (3-4) ◽  
pp. 228-241
Author(s):  
Runqiu Bao ◽  
Ren Komatsu ◽  
Renato Miyagusuku ◽  
Masaki Chino ◽  
Atsushi Yamashita ◽  
...  
Keyword(s):  
Visual Slam ◽  
Construction Sites ◽  
Stereo Camera ◽  
Motion State ◽  
State Classification ◽  
Dynamic Objects

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

PROPAGATION OF THE BURNING WAVE IN A PULSED DETONATION COMBUSTOR OPERATING ON MIXTURES OF HEPTANE AND JET A-1 WITH AIR AND OXYGEN AT [O2/AIR] < 1

2020 ◽  
Author(s):  
M. S. ASSAD ◽  
◽  
O. G. PENYAZKOV ◽  
I. I. CHERNUHO ◽  
K. ALHUSSAN ◽  
...  
Keyword(s):  
Wave Propagation ◽  
Combustion Wave ◽  
Equivalence Ratio ◽  
Pressure Sensors ◽  
Jet Fuel ◽  
Propagation Time ◽  
Burning Wave ◽  
Pulsed Detonation ◽  
Combustion Wave Propagation ◽  
Fuel Jet

This work is devoted to the study of the dynamics of combustion wave propagation in oxygen-enriched mixtures of n-heptane with air and jet fuel "Jet A-1" in a small-size pulsed detonation combustor (PDC) with a diameter of 20 mm and a length less than 1 m. Experiments are carried out after the PDC reaches a stationary thermal regime when changing the equivalence ratio (ϕ = 0.73-1.89) and the oxygen-to-air ratio ([O2/air] = 0.15-0.60). The velocity of the combustion wave is determined by measuring the propagation time of the flame front between adjacent pressure sensors that form measurement segements along the PDC.

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