Supplementary Data for Publication "A Safety Benchmark for Capacitive Proximity Sensing in Robotics - Safe Transient Contact in Compliance to ISO TS 15066: Power and Force Limiting
The Supplementary Data includes
- STL files of the test objects (already available)
- Measurement Data of each institute for the shown test objects (are partially uploaded, list of files will be completed soon)
Disclaimer: This publication was submitted and is currently under review to be published for IEEE RA-L with ICRA 2021 option.
*** Short hand notation ****
AAU.. Klagenfurt Univeristy
KIT... Karlsruhe Institute of Technology
TUC... Chemnitz University of Technology
**** Test objects *****
This *.zip directory contains *.stl files with the test objects used in the publication
These files are ready to pre-processed with slicing software to be 3d printed.
Bigger objects such as the face and the sphere are splitted into multiple parts, which can be then glued together. When covering your test object with copper foil, make sure that the adhesive layer is also conductive.
In order to mount the test objects to your robot you need a plastic tube (or cylindrical rod) with 12 mm outer diameter, which can be bought at your local hardware store or be 3d printed as well.
An additional adapter is needed to mount the plastic tube to your robot's TCP.
**** Sensing directivity measurements****
The measurement results for the sensing directvitiy can be accessed by
KIT conducted measurements of the fist with three different setups of active electrode (21 cm^2, 42 cm^2 and 84 cm^2 ) sizes.
(further measurement results will be uploaded soon)
Each file is structured as follows:
Columns 1 to 3 represent the Cartesian Coordinates of the lowest point of the test object with respect to the center of the electrode
Column 4 shows the average of measurements conducted in 10 ms calibrated by the baseline value
Column 5 shows the standard deviation of 500 measurements at each point
Column 6 and 7 represent basline value and baseline standard deviation (by the same means as for Column 5-6) respectively.
NOTES: AAU recorded the baseline value at 40 cm (this is why that value is constant for the whole column)
Column 8 indicates if that point was detected by means explained in Section IV - A of the paper
**** Spatial resoltuion measurements and analysis of different grounding options****
For three different grounding options
1.5 kOhm, 1.5 kOhm and 100 pF, 0 Ohm (hard grounding)
the output signal for the sensor of KIT (as shown in the paper submission) and the spatial resolution was analysed for increasing normal distance (z-Direction) to the electrode surface center.
The measurements were conducted using the fist as test object and an active electrode size of 42 cm^2
Column 1 gives the z coordinate,
Column 2 to 4 show the values for signal/spatial resolution 1.5 kOhm, 1.5 kOhm and 100 pF and 0 Ohm (hard grounding) respectively.
If you need further information, do not hesitate to contact us at:
Institute for Smart System Technologies
Sensors and Actuators Group
A - 9020 Klagenfurt
**Dataset will be uploaded soon - dataset is complete but uploader is currently freezing midway through status bar**
This dataset contains inertial data consisting of 1) physiotherapy exercise recordings, and 2) unlabeled other activity data recordings, each collected by smart watches worn by healthy subjects.
This dataset may be used to perform supervised classification analysis of physiotherapy exercises, or to perform out-of-distribution detection analysis with the unlabeled other activity data.
This inertial dataset consists of 86 records for 20 healthy subjects.
Each record consists of an Nx10 array, where numbered columns correspond to 0-2: Accelerometer (X/Y/Z) in G's, 3-5: Magnetometer (X/Y/Z) in μT's, 6-8: Gyroscope (X/Y/Z) in rad/s, and 9: Heart Rate in bpm. Inertial data was captured at 50 Hz.
To use this dataset, please begin by extracting the compressed folder. The data file may be imported into python using the numpy package with the following line of code:
data = np.load(DATA_PATH + 'spars9x.npy', allow_pickle=True).item()
This returns a python dictionary with 6 keys:
'X' : The inertial data consisting of
'y': Associated labels of the inertial data. See 'y_labels' for label numbers and descriptions.
'subject': Labeling indicating which subject each record belongs to for each of the 86 records included in the dataset.
'side': Indicates shoulder watch was worn on for each record. 0: side not recorded (OOD data only); 1: Left Side; 2: Right Side
'X_labels': May be used to label the columns of the inertial data according to the IMU used, as noted in 'X' above.
'y_labels': Numbered labels by activity names.
Temperature profiles for thermal detection.
The dataset contains medical signs of the sign language including different modalities of color frames, depth frames, infrared frames, body index frames, mapped color body on depth scale, and 2D/3D skeleton information in color and depth scales and camera space. The language level of the signs is mostly Word and 55 signs are performed by 16 persons two times (55x16x2=1760 performance in total).
The signs are collected at Shahid Beheshti University, Tehran, and show local gestures. The SignCol software (code: https://github.com/mohaEs/SignCol , paper: https://doi.org/10.1109/ICSESS.2018.8663952 ) is used for defining the signs and also connecting to Microsoft Kinect v2 for collecting the multimodal data, including frames and skeletons. Two demonstration videos of the signs are available at youtube: vomit: https://youtu.be/yl6Tq7J9CH4 , asthma spray: https://youtu.be/PQf8p_YNYfo . Demonstration videos of the SignCol are also available at https://youtu.be/_dgcK-HPAak and https://youtu.be/yMjQ1VYWbII .
The dataset contains 13 zip files totally: One zipfile contains readme, sample codes and data (Sample_AND_Codes.zip), the next zip file contains sample videos (Sample_Videos.zip) and other 11 zip files contain 5 signs in each (e.g. Signs(11-15).zip). For quick start, consider the Sample_AND_Codes.zip.
Each performed gesture is located in a directory named in Sign_X_Performer_Y_Z format which shows the Xth sign performed by the Yth person at the Znd iteration (X=[1,...,55], Y=[1,...,16], Z=[1,2]). The actual names of the signs are listed in the file: table_signs.csv.
Each directory includes 7 subdirectories:
1. Times: time information of frames saved in CSV file.
2. Color Frames: RGB frames saved in 8 bits *.jpg format with the size of 1920x1080.
3. Infrared Frames: Infrared frames saved in 8 bits *.jpg format with the size of 512x424.
4. Depth Frames: Depth frames saved in 8 bits *.jpg format with the size of 512x424.
5. Body Index Frames: Body Index frames scaled in depth saved in 8 bits *.jpg format with the size of 512x424.
6. Body Skels data: For each frame, there is a CSV file containing 25 rows according to 25 joints of body and columns for specifying the joint type, locations and space environments. Each joint location is saved in three spaces, 3D camera space, 2D depth space (image) and 2D color space (image). The 21 joints are visible in this dataset.
7. Color Body Frames: frames of RGB Body scaled in depth frame saved in 8 bits *.jpg format with the size of 512x424.
Frames are saved as a set of numbered images and the MATLAB script PrReadFrames_AND_CreateVideo.m shows how to read frames and also how to create videos, if is required.
The 21 visible joints are Spine Base, Spine Mid, Neck, Head, Shoulder Left, Elbow Left, Wrist Left, Hand Left, Shoulder Right, Elbow Right, Wrist Right, Hand Right, Hip Left, Knee Left, Hip Right, Knee Right, Spine Shoulder, Hand TipLeft, Thumb Left, Hand Tip Right, Thumb Right. The MATLAB script PrReadSkels_AND_CreateVideo.m shows an example of reading joint’s informtaion, fliping them and drawing the skeleton on depth and color scale.
The updated information about the dataset and corresponding paper are available at GitHub repository MedSLset.
Terms and conditions for the use of dataset:
1- This dataset is released for academic research purposes only.
2- Please cite both the paper and dataset if you found this data useful for your research. You can find the references and bibtex at MedSLset.
3- You must not distribute the dataset or any parts of it to others.
4- The dataset just inclues image, text and video files and is scanned via malware protection softwares. You accept full responsibility for your use of the dataset. This data comes with no warranty or guarantee of any kind, and you accept full liability.
5- You will treat people appearing in this data with respect and dignity.
6- You will not try to identify and recognize the persons in the dataset.
Coventry-2018 is a human activity recognition dataset captured by three Panasonic® Grid-EYE (AMG8833) infrared sensors in March 2018. The Grid-EYE sensors represent a 60 field of view scene by an 8 × 8 array named frame. The data streams are synchronized to 10 frames per second and saved as *.csv recordings using the LabVIEW® software. Two layouts are considered in this dataset with different geometry sizes: 1) small layout; and 2) large layout.
Database set information
This article describes the possible design of the electron-ion trap combined density sensor and the composition of the upper atmosphere and simulation of the processes occurring in it. The simulation of the electric field between the electrodes of the trap and the motion of charged particles in it is carried out. The calculation of the maximum speed and energy of the particles below which the trap holds all charged particles, even in the case of the most unfavorable direction of their speed – along the gap between the electrodes.
A Indústria enfrenta desafios graves e fracassa sem competitividade. Atacando esta problemática, conferiu-se o oferecimento de maior eficiência a processos industriais para promover a produtividade, elevar a qualidade e impulsionar mudanças. A solução desenvolvida incluiu dispositivos com sensores não invasivos, simples de instalar, que contabilizam os itens sendo transportados em linhas de produção.
Os dados foram coletados utilizando o dispositivo IoT da EnergyNow Tecnologias denominado Prodbox™, o qual opera como um equipamento empregado para intensificar a produtividade e apontar maneiras estratégicas de modificar variáveis que interferem na visão de gestão sobre a produção.
O dispositivo utiliza sensores não obstrutivos para contabilizar o número de itens que atravessam a linha de detecção gerada entre o transmissor e o receptor instalados.
Notadamente, os dados coletados são enviados para a nuvem, onde podem, quando integrados a uma plataforma de análise, ser processados para apresentar indicadores de acompanhamento de produtividade. Um sistema inteligente pode processar os dados coletados e apresentar métricas que permitem ao gestor identificar formas de aumentar a produção, bem como etapas que estão prejudicando a produtividade. Além disso, alertas customizados podem ser configurados para prover informação sobre a parada ou inatividade detectada pelo dispositivo.
Os dados gerados através do dispositivo podem ser utilizados para entender melhor variáveis sobre o ritmo de produção e, a partir delas, fomentar projeções de produção, calculando-se a relação entre itens produzidos e período de tempo necessário (segundos, minutos, horas, dias, semanas, etc).
Algumas sugestões sobre abordagens a serem consideradas:
Verifique se políticas de aumento de produtividade estão sendo efetivas.
Distribuia melhor os funcionários em etapas diferentes de uma linha de produção.
Correlacione etapas de produção com variáveis que estejam interferindo na produtividade para resolver problemáticas internas.
We document a feedback controller design for a nonlinear electrostatic transducer that exhibits
a~strong unloaded resonance. Challenging features of this type of transducer include the presence
of multiple fixed points (some of which are unstable), nonlinear force-to-deflection transfer,
effective spring-constant softening due to electrostatic loading and associated resonance
frequency shift. Furthermore, due to the utilization of low-pass filters in the electronic readout
fscodt12_modified_ADRC_2FIR_um_scaling.zcosIvan Ryger, 5/6/2020Tested on Scilab 6.0.2/ XcosThe program is solely for non-commercial scientific/educational purposes. The author does not hold any responsibility for the misuse of the code.Short description of the programfscodt12_modified_ADRC_2FIR_um_scaling.zcos is a simulation model of an electrostatic transducer connected in a loop of state feedback controller. The conversion between measured capacitance and voltage is achieved by analytic expressions for a capacitive bridge, sinusoidally excited and connected to a synchronous demodulator. The output is fed through a polynomial approximation section, where the deflection information is inferred from known conversion between deflection -> capacitance -> bridge voltage. The signal is fed through a FIR filter simulating the lowpass filter inherent to the lock-in demodulator. Both feedback force and measured deflection signals are fed into ADRC block, to estimate the internal states of the controlled system (transducer). Based on this output estimation, the state feedback controller calculates the compensating force. This is fed to a force-> voltage conversion block, whose output is in units of voltage(electrostatic bias). Instructions: 1. open the file simulation file (xcos)2. go to "Simulation"- > "set context" and change the pointer to the directory with files ("cd" command)3. run the command " exec('filter_fs_80k_fp_1k.sce') " from the Scilab console. This will load the matrix B into memory, containing the FIR filter coefficients4. run the simulation5. run the command from the console "exec('nice_plots2.sce')"
The Here East Digital Twin was a six month trial of a real-time 3D data visualisation platform, designed for the purpose of supporting operational management in the built environment.
1. View the project video for context: https://vimeo.com/311089492
2. Review details of the Envirosensor implementation: https://github.com/virtualarchitectures/ICRI_Envirosensor
3. One option for analysing the data is using Spark and Python in a Jupyter Notebook as outlines in the following example GitHub repository: https://github.com/virtualarchitectures/ICRI_Envirosensor_Data_Analysis