Synthesising motion sensor data from biomechanical simulations to investigate motion sensor placement and orientation variations

2019 ◽  
Author(s):  
Adrian Derungs ◽  
Oliver Amft
Keyword(s):  
Sensor Placement ◽  
Sensor Data ◽  
Motion Sensor

scholarly journals A Hierarchical Deep Fusion Framework for Egocentric Activity Recognition Using a Wearable Hybrid Sensor System

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 546 ◽  
Author(s):  
Haibin Yu ◽  
Guoxiong Pan ◽  
Mian Pan ◽  
Chong Li ◽  
Wenyan Jia ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Short Term Memory ◽  
Specific Activity ◽  
Negative Influence ◽  
Sensor System ◽  
Sensor Data ◽  
Motion Sensor ◽  
Motion Sensors ◽  
Motion State ◽  
Fusion Framework

Recently, egocentric activity recognition has attracted considerable attention in the pattern recognition and artificial intelligence communities because of its wide applicability in medical care, smart homes, and security monitoring. In this study, we developed and implemented a deep-learning-based hierarchical fusion framework for the recognition of egocentric activities of daily living (ADLs) in a wearable hybrid sensor system comprising motion sensors and cameras. Long short-term memory (LSTM) and a convolutional neural network are used to perform egocentric ADL recognition based on motion sensor data and photo streaming in different layers, respectively. The motion sensor data are used solely for activity classification according to motion state, while the photo stream is used for further specific activity recognition in the motion state groups. Thus, both motion sensor data and photo stream work in their most suitable classification mode to significantly reduce the negative influence of sensor differences on the fusion results. Experimental results show that the proposed method not only is more accurate than the existing direct fusion method (by up to 6%) but also avoids the time-consuming computation of optical flow in the existing method, which makes the proposed algorithm less complex and more suitable for practical application.

scholarly journals Real-Time Reconstruction of Contaminant Dispersion from Sparse Sensor Observations with Gappy POD Method

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1956 ◽  
Author(s):  
Zheming Tong ◽  
Yue Li
Keyword(s):  
Real Time ◽  
Field Data ◽  
Complex Geometry ◽  
Sensor Placement ◽  
Three Dimensional ◽  
Time Estimation ◽  
Sensor Data ◽  
Real Time Control ◽  
Time Control ◽  
Gappy Pod

Real-time estimation of three-dimensional field data for enclosed spaces is critical to HVAC control. This task is challenging, especially for large enclosed spaces with complex geometry, due to the nonuniform distribution and nonlinear variations of many environmental variables. Moreover, constructing and maintaining a network of sensors to fully cover the entire space is very costly, and insufficient sensor data might deteriorate system performance. Facing such a dilemma, gappy proper orthogonal decomposition (POD) offers a solution to provide three-dimensional field data with a limited number of sensor measurements. In this study, a gappy POD method for real-time reconstruction of contaminant distribution in an enclosed space is proposed by combining the POD method with a limited number of sensor measurements. To evaluate the gappy POD method, a computational fluid dynamics (CFD) model is utilized to perform a numerical simulation to validate the effectiveness of the gappy POD method in reconstructing contaminant distributions. In addition, the optimal sensor placement is given based on a quantitative metric to maximize the reconstruction accuracy, and the sensor placement constraints are also considered during the sensor design process. The gappy POD method is found to yield accurate reconstruction results. Further works will include the implementation of real-time control based on the POD method.

scholarly journals Perancangan Sistem Efisiensi Penggunaan Energi Listrik Menggunakan Sensor Gerak dan Sensor Arus

2020 ◽  
Vol 4 (3) ◽  
pp. 626
Author(s):  
Sarmayanta Sembiring ◽  
Hadir Kaban ◽  
Rido Zulfahmi
Keyword(s):  
Human Movement ◽  
Electrical Energy ◽  
Electronic Equipment ◽  
Sensor Data ◽  
Current Sensor ◽  
Motion Sensor ◽  
Air Conditioners ◽  
Average Current ◽  
Sensor Current ◽  
Predetermined Time

Efficiency system in using electrical energy has been designed using a PIR motion sensor, current sensor SCT-013-030, infrared LED and relay with a controller using Arduino Uno. The system is designed to turn off electronic equipment such as air conditioners, projectors and lights automatically as a solution from users forgetting to turn off electronic equipment when it is no longer in use. The experimental results show that the system has been running well, where the system can detect no movement for a predetermined time by using a PIR motion sensor. Detection of electronic equipment using sensors SCT-013-030 has been able to distinguish the state of the equipment whether it is ON or OFF based on differences in sensor output data that is read by the Arduino analog port. Sensor data when detecting the lamp when OFF is average = 1.333 while the mini projector and TV when off the average sensor data value = 1.667. The average current sensor data when detecting lights when ON = 5,333, mini projector = 8,333 and TV = 11,333. Overall the system designed has been able to turn off the equipment that is still active when the sensor does not detect any human movement during a predetermined time

scholarly journals Scalable Polyhedral Verification of Recurrent Neural Networks

2021 ◽  
pp. 225-248
Author(s):  
Wonryong Ryou ◽  
Jiayu Chen ◽  
Mislav Balunovic ◽  
Gagandeep Singh ◽  
Andrei Dan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Recurrent Neural Networks ◽  
Sensor Data ◽  
Motion Sensor ◽  
Use Case ◽  
Prior Work ◽  
Non Linear ◽  
Speech Classification ◽  
Sampling Optimization

AbstractWe present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.

Predicting Human Actions Using a Hybrid of ReliefF Feature Selection and Kernel-Based Extreme Learning Machine

2018 ◽  
pp. 379-397 ◽  
Author(s):  
Musa Peker ◽  
Serkan Ballı ◽  
Ensar Arif Sağbaş
Keyword(s):  
Feature Selection ◽  
Extreme Learning Machine ◽  
Human Action ◽  
High Accuracy ◽  
Attribute Selection ◽  
Sensor Data ◽  
Motion Sensor ◽  
Sensor Signals ◽  
Learning Machine

Human activity recognition (HAR) is a growing field that provides valuable information about a person. Sensor-equipped smartwatches stand out in these studies in terms of their portability and cost. HAR systems usually preprocess raw signals, decompose signals, and then extract attributes to be used in the classifier. Attribute selection is an important step to reduce data size and provide appropriate parameters. In this chapter, classification of eight different actions (brushing teeth, walking, running, vacuuming, writing on the board, writing on paper, using the keyboard, and stationary) has been performed with smartwatch motion sensor data. Forty-two different features have been extracted from the motion sensor signals and the feature selection has been performed with the ReliefF algorithm. After that, performance evaluation has been performed with four different machine learning methods. With this study in which the best results have been obtained with the kernel-based extreme learning machine (KELM) algorithm, estimation of human action has been performed with high accuracy.

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