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opencc-by-4.0Feb 2023View details →

mmWave-based Fitness Activity Recognition Dataset

Description:This mmWave Datasets are used for fitness activity identification. This dataset (FA Dataset) contains 14 common fitness daily activities. The data are captured by the mmWave radar TI-AWR1642. The dataset can be used by fellow researchers to reproduce the original work or to further explore other machine-learning problems in the domain of mmWave signals.Format: .png formatSection 1: Device Configuration<ul><li>A commodity mmWave radar TI AWR1642, which integrates a 2 × 4 antenna array. The detailed information of it can be found at <a href="https://www.ti.com/product/AWR1642#:~:text=The%20AWR1642%20is%20an%20ideal,of%2076%20to%2081%20GHz">https://www.ti.com/product/AWR1642#:~:text=The%20AWR1642%20is%20an%20ideal,of%2076%20to%2081%20GHz.</a></li><li>A TI DCA1000EVM data capture card is used to collect data from the mmWave device and send data to a laptop. The detailed information can be found at <a href="https://www.ti.com/tool/DCA1000EVM?keyMatch=DCA1000EVM">https://www.ti.com/tool/DCA1000EVM?keyMatch=DCA1000EVM</a>.</li><li>mmWave radar work at the frequency in the range of 77~81GHz. The sampling rate is fixed at 100 frames per second and each frame has 17 chirps.</li></ul>Section 2: Data FormatWe provide our mmWave data in heatmaps for this dataset. The data file is in the png format. The details are shown in the following:<ul><li>14 activities are included in the FA Dataset.</li><li>2 participants are included in the FA Dataset.</li><li>FA_d_p_i_u_j.png:<ul><li>d represents the date to collect the fitness data.</li><li>p represents the environment to collect the fitness data.</li><li>i represents fitness activity type index</li><li>u represents user id</li><li>j represents sample index</li></ul></li><li>Example:<ul><li>FA_20220101_lab_1_2_3 represents the 3rd data sample of user 2 of activity 1 collected in the lab</li></ul></li></ul>Section 3: Experimental Setup<ul><li>We place the mmWave device on a table with a height of 60cm.</li><li>The participants are asked to perform fitness activity in front of a mmWave device with a distance of 2m.</li><li>The data are collected at an lab with a size of (5.0m×3.0m).</li></ul>Section 4: Data Description<ul><li>We develop a spatial-temporal heatmap to integrates multiple activity features, including the range of movement, velocity, and time duration of each activity repetition.</li></ul> <ul><li>We first derive the Doppler-range map of the users' activity by calculating Range-FFT and Doppler-FFT. Then, we generate the spatial-temporal heatmap by accumulating the velocity of every distance in every Doppler-range map together. Next, we normalize the derived velocity information and present the velocity-distance relationship in time dimension. In this way, we transfer the original instantaneous velocity-distance relationship to a more comprehensive spatial-temporal heatmap which describes the process of a whole activity.</li></ul> <ul><li>As shown in Figure attached, in each spatial-temporal heatmap, the horizontal axis represents the time duration of an activity repetition while the vertical axis represents the range of movement. The velocity is represented by color.</li></ul> <ul><li>We create 14 zip files to store the the dataset. There are 14 zip files starting with "FA", each contains repetitions from the same fitness activity.</li></ul>14 common daily activities and their corresponding files File Name               Activity Type                             File Name              Activity TypeFA1                          Crunches                                    FA8                        SquatsFA2                          Elbow plank and reach               FA9                       BurpeesFA3                          Leg raise                                     FA10                     Chest squeezesFA4                          Lunges                                        FA11                     High kneesFA5                          Mountain climber                        FA12                     Side leg raiseFA6                          Punches                                      FA13                     Side to side chopsFA7                          Push ups                                     FA14                     Turning kicks Section 5: Raw Data and Data Processing Algorithms<ul><li>We also provide the mmWave raw data (.mat format) stored in the same zip file corresponding to the heatmap datasets. Each .mat file can store one set of activity repetitions (e.g., 4 repetations) from a same user.<ul><li>For example: FA_d_p_i_u_j.mat:<ul><li>d represents the data to collect the data.</li><li>p represents the environment to collect the data.</li><li>i represents the activity type index</li><li>u represents the user id</li><li>j represents the set index</li></ul></li></ul></li><li>We plan to provide the data processing algorithms (heatmap_generation.py) to load the mmWave raw data and generate the corresponding heatmap data.</li></ul>Section 6: CitationsIf your paper is related to our works, please cite our papers as follows.<a href="https://ieeexplore.ieee.org/document/9868878/">https://ieeexplore.ieee.org/document/9868878/</a>Xie, Yucheng, Ruizhe Jiang, Xiaonan Guo, Yan Wang, Jerry Cheng, and Yingying Chen. "mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave." In 2022 International Conference on Computer Communications and Networks (ICCCN), pp. 1-10. IEEE, 2022.Bibtex:@inproceedings{xie2022mmfit,  title={mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave},  author={Xie, Yucheng and Jiang, Ruizhe and Guo, Xiaonan and Wang, Yan and Cheng, Jerry and Chen, Yingying},  booktitle={2022 International Conference on Computer Communications and Networks (ICCCN)},  pages={1--10},  year={2022},  organization={IEEE}}

opencc-by-4.0Apr 2023View details →

Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned~Dataset

This refers to dataset of the paper Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned to appear in IEEE WoWMoM 2023. Those files labeled with "beamsnr" correspond to 60 GHz while others with "data_processed" correspond to CSI at 5GHz. "ENV1" and "ENV2" represent two environments. "orient90" means user performs gestures 90 degree with respect to LoS.   Abstract: In recent years, channel state information (CSI) at sub-6 GHz has been widely exploited for Wi-Fi sensing, particularly for activity and gesture recognition. In this work, we instead explore mmWave (60 GHz) Wi-Fi signals for gesture recognition/pose estimation. Our focus is on the mmWave WiFi signals so that they can be used not only for high data rate communication but also for improved sensing e.g., for extended reality (XR) applications. For this reason, we extract spatial beam signal-to-noise ratios (SNRs) from the periodic beam training employed by IEEE 802.11ad devices. We consider a set of 10 gestures/poses motivated by XR applications. We conduct experiments in two environments and with three people. As a comparison, we also collect CSI from IEEE 802.11ac devices. To extract features from the CSI and the beam SNR, we leverage a deep neural network (DNN). The DNN classifier achieves promising results on the beam SNR task with stateof-the-art 96.7% accuracy in a single environment, even with a limited dataset. We also investigate the robustness of the beam SNR against CSI across different environments. Our experiments reveal that features from the CSI generalize without additional re-training, while those from beam SNRs do not. Therefore, retraining is required in the latter case.

opencc-zeroApr 2023View details →

Finger gesture recognition with smart skin technology and deep learning

Finger gesture recognition was extensively studied in recent years for a wide range of human-machine interface applications. Surface electromyography (sEMG), in particular, is an attractive, enabling technique in the realm of finger gesture recognition, and both low and high-density sEMG were previously studied. Despite the clear potential, cumbersome electrode wiring and electronic instrumentation render contemporary sEMG-based finger gestures recognition to be performed under unnatural conditions. Recent developments in smart skin technology provide an opportunity to collect sEMG data in more natural conditions. Here we report on a novel approach based on a soft 16-electrode array, a miniature and wireless data acquisition unit and neural network analysis, in order to achieve gesture recognition under natural conditions. Finger gesture recognition accuracy values, as high as 93.1%, were achieved for 8 gestures when the training and test data were from the same session. For the first time, high accuracy values are also reported for training and test data from different sessions for three different hand positions. These results demonstrate an important step towards sEMG-based gesture recognition in non-laboratory settings, such as in gaming or Metaverse.

opencc-by-4.0Apr 2023View details →

ESTER-Pt: An Evaluation Suite for TExt Recognition in Portuguese

disclaimer:  Version accepted as full paper in ICDAR 2023. Optical Character Recognition (OCR) is a technology that enables machines to read and interpret printed or handwritten texts from scanned images or photographs. However, the accuracy of OCR systems can vary depending on several factors, such as the quality of the input image, the font used, and the language of the document. As a general tendency, OCR algorithms perform better in resource-rich languages as they have more annotated data to train the recognition process. We propose ESTER-Pt, an Evaluation Suite for TExt Recognition in Portuguese in this work. Despite being one of the largest languages in terms of speakers, OCR in Portuguese remains largely unexplored. Our evaluation suite comprises four types of resources: synthetic text-based documents, synthetic image-based documents, real scanned documents, and a hybrid set with real image-based documents that were synthetically degraded.

Fig. 68 in A revision of Afrotropical Chyromyidae (excluding Gymnochiromyia Hendel) (Diptera: Schizophora), with the recognition of two subfamilies and the description of new genera

Fig. 68. Distribution of Somatiosoma Frey () and Gymnochiromyia Hendel (•) in Africa.

Fig. 69 in A revision of Afrotropical Chyromyidae (excluding Gymnochiromyia Hendel) (Diptera: Schizophora), with the recognition of two subfamilies and the description of new genera

Fig. 69. Distribution of Aphaniosoma Becker in Africa.

Fig. 66 in A revision of Afrotropical Chyromyidae (excluding Gymnochiromyia Hendel) (Diptera: Schizophora), with the recognition of two subfamilies and the description of new genera

Fig. 66. Distribution of Notiochyromya gen. n.

Fig. 67 in A revision of Afrotropical Chyromyidae (excluding Gymnochiromyia Hendel) (Diptera: Schizophora), with the recognition of two subfamilies and the description of new genera

Fig. 67. Distribution of Oroschyromya gen. n.

Fig. 11 in A revision of Afrotropical Chyromyidae (excluding Gymnochiromyia Hendel) (Diptera: Schizophora), with the recognition of two subfamilies and the description of new genera

Fig. 11. Notiochyromya tripunctata sp. n., ơ hypopygium, lateral. Scale bar = 0.2 mm.

Fig. 17 in A revision of Afrotropical Chyromyidae (excluding Gymnochiromyia Hendel) (Diptera: Schizophora), with the recognition of two subfamilies and the description of new genera

Fig. 17. Oroschyromya dubia (Lamb), ^postabdomen, lateral (a) and ventral (b). Scale bar = 0.15 mm.

opencc-by-4.0Dec 2022View details →

Virtual Reality Gesture Recognition Dataset

This dataset provides valuable insights into hand gestures and their associated measurements. Hand gestures play a significant role in human communication, and understanding their patterns and characteristics can be enabled various applications, such as gesture recognition systems, sign language interpretation, and human-computer interaction. This dataset was carefully collected by a specialist who captured snapshots of individuals making different hand gestures and measured specific distances between the fingers and the palm. The dataset offers a comprehensive view of these measurements, allowing for further analysis and exploration of the relationships between different gestures and their corresponding hand measurements. The dataset's potential applications are wide-ranging. For instance, it can be used to develop gesture recognition systems that can identify and interpret hand movements accurately. By training machine learning models on this dataset, it is possible to create algorithms capable of recognizing specific hand gestures based on the measured distances. This can enable intuitive human-machine interaction and interfacing, particularly in domains such as virtual reality, augmented reality, and smart devices. Moreover, researchers interested in the biomechanics of hand movements or exploring the cultural significance of specific gestures can leverage this dataset to gain insights into the physical aspects of hand gestures and their variations across different individuals.

opencc-zeroMay 2023View details →

Patterns of genetic variation and morphology support the recognition of five species in the Gaultheria leucocarpa Blume (Ericaceae) group from mainland China

Gaultheria leucocarpa and its varieties form a clade of aromatic shrubs that is widely distributed in subtropical and East Asian tropical regions. The group is taxonomically difficult and is in need of thorough taxonomic investigation. This study focused on taxonomic delimitation within the G. leucocarpa group from mainland China. Field surveys covering the distributional range of G. leucocarpa in mainland China were conducted, wherein four populations from Yunnan and one from Hunan were found bearing visibly morphological and habitat differences. A 63-species phylogenetic tree of Gaultheria based on one nuclear and three chloroplast markers that included samples from the G. leucocarpa group was reconstructed with maximum likelihood to clarify the monophyly of the G. leucocarpa group. Taxonomic relationships among populations of the G. leucocarpa group were investigated with morphology and population genetics, the latter by using two chloroplast genes and two low-copy nuclear genes. Based on the sum of morphological and genetic analyses, we described three species of Gaultheria as new to science, clarified the taxonomic status of G. leucocarpa var. pingbienensis, elevating it to the species level, and resurrected G. crenulata and treated the varieties G. leucocarpa var. crenulata, and G. leucocarpa var. yunnanensis as synonyms of this species. We provide a key to the five species now recognized, along with descriptions and photographs.

opencc-by-nc-4.0Jun 2023View details →

The EmoHI Test stimuli: Measuring vocal emotion recognition in hearing-impaired populations

Before reading this file, make sure you have read the README.1.pdf file. That file also contains information about the license these materials are distributed under. Versions <ul> <li>Version 2: This is the current version. To cite this version specifically, use DOI 10.5281/zenodo.7997063. In this version we fixed some naming mistakes in the files (in 8 of the files the sentence was identified as <code>t2</code> instead of <code>s2</code>), and added two missing stimuli (<code>t5_neutral_t2_u04.wav</code> and <code>t5_sad_t1_u05.wav</code>).</li> <li>Version 1: This was the initial version. To cite that version specifically, use DOI 10.5281/zenodo.3689710.</li> </ul> The latest version of the EmoHI material can be downloaded from <a href="https://doi.org/10.5281/zenodo.3689709">https://doi.org/10.5281/zenodo.3689709</a>. Please always check that you have the latest version, and that you comply with the current license requirements. The EmoHI Test The EmoHI Test was developed to measure the accuracy at which participants can recognize vocal emotions based on pseudospeech sentences that were produced in a happy, angry sad, or neutral manner. The EmoHI Test recordings are particularly suitable for testing hearing-impaired populations due to their high sound quality. All recordings, including the ones that were used in Nagels et al. (2020, PeerJ, <a href="https://doi.org/10.7717/peerj.8773">doi: 10.7717/peerj.8773</a>), are made available here. The stimuli were recorded in an anechoic room at a sampling rate of 44.1 kHz. The microphone was placed at a distance of approximately 30 cm (12 in) from the speaker. The recordings were made by connecting a standing Røde NT1 microphone to a Presonus TubePre V2 preamplifier and a TASCAM DR-100 portable digital recorder. The gain of the recordings was adjusted for each emotion production using the preamplifier to record the stimuli at an intensity level that was approximately the same across emotions to reduce large intensity differences between the recordings of different emotions. The files are not RMS equalized. Citation When using this repository in your research, please cite the repository itself. For this version: <blockquote> Nagels L., Gaudrain E., Hendriks P., & Başkent D. (2023, June 2). The EmoHI Test stimuli: Measuring vocal emotion recognition in hearing-impaired populations. Version 2. Zenodo. <a href="https://doi.org/10.5281/zenodo.7997063">https://doi.org/10.5281/zenodo.7997063</a> </blockquote> Also cite the PeerJ article that describes the material: <blockquote> Nagels L., Gaudrain E., Vickers D., Matos Lopes M., Hendriks P., Başkent D. (2020). Development of vocal emotion recognition in school-age children: The EmoHI test for hearing-impaired populations. PeerJ 8:e8773 <a href="https://doi.org/10.7717/peerj.8773">https://doi.org/10.7717/peerj.8773</a> </blockquote> Sound file name structure The sound files are named using the following convention: <code>t[1-6]_{emotion}_s{1,2}_u[01-18].wav</code> <ul> <li><code>t[1-6]</code> represents the talker who produced the stimulus: <code>t1</code>, <code>t2</code>, <code>t3</code>, <code>t4</code>, <code>t5</code>, or <code>t6</code></li> <li><code>{emotion}</code> is the label of the emotion that was produced: <code>neutral</code>, <code>happy</code>, <code>angry</code>, or <code>sad</code></li> <li><code>s{1,2}</code> is the pseudospeech sentence that was used: <code>s1</code> for "Koun se mina lod belam." <code>s2</code> for "Nekal ibam soud molen."</li> <li><code>u[01-18]</code> is the utterance number: Number ranging from <code>u01</code> to <code>u18</code></li> </ul> For instance, <code>t1_happy_s2_u01.wav</code> is utterance 1 of talker <code>t1</code> producing emotion "happy" using sentence 2. Talker demographic information The table below gives an overview of the voice characteristics from the talkers who produced the EmoHI test stimuli. <table> <thead> <tr> <th scope="col">Talker</th> <th scope="col">Age (years)</th> <th scope="col">Gender</th> <th scope="col">Height (m)</th> <th scope="col">Mean F0 (Hz)</th> <th scope="col">F0 range (Hz)</th> </tr> </thead> <tbody> <tr> <td>t1</td> <td>48</td> <td>f</td> <td>1.72</td> <td>253.14</td> <td>179.97 – 421.81</td> </tr> <tr> <td>t2</td> <td>36</td> <td>f</td> <td>1.68</td> <td>302.23</td> <td>200.71 – 437.38</td> </tr> <tr> <td>t3</td> <td>27</td> <td>m</td> <td>1.85</td> <td>166.92</td> <td>100.99 – 296.47</td> </tr> <tr> <td>t4</td> <td>45</td> <td>m</td> <td>1.90</td> <td>149.41</td> <td>96.97 – 274.72</td> </tr> <tr> <td>t5</td> <td>25</td> <td>f</td> <td>1.63</td> <td>282.89</td> <td>199.49 – 429.38</td> </tr> <tr> <td>t6</td> <td>24</td> <td>m</td> <td>1.75</td> <td>167.76</td> <td>87.46 – 285.79</td> </tr> </tbody> </table> Supporting data The behavioural data from the PeerJ article is accessible at <a href="https://doi.org/10.34894/BDMX6D">https://doi.org/10.34894/BDMX6D</a>.

opencc-by-4.0Jun 2023View details →

Dataset for Paper: Text Line Detection and Recognition of Greek Polytonic Documents

Dataset for Paper: Text Line Detection and Recognition of Greek Polytonic Documents, P. Kaddas, B. Gatos, K. Palaiologos, K. Christopoulou and K. Kritsis, 4th Workshop on Machine Learning (WML), San Jose, California, USA We introduce a new dataset, named GTLD-small dataset, with annotated text line quadrilateral polygons of 1.642 documents, including annotations on 3 datasets (Tobacco-3482, PIOP and ShakeIT dataset) <table> <caption>Overview of the datasets included in this work and the number of images used for training, validation and testing.</caption> <thead> <tr> <th scope="col">Collection</th> <th scope="col">#Total</th> <th scope="col">#train</th> <th scope="col">#val</th> <th scope="col">#test</th> </tr> </thead> <tbody> <tr> <td>PIOP-small</td> <td>950</td> <td>672</td> <td>90</td> <td>188</td> </tr> <tr> <td>ShakeIT-small</td> <td>357</td> <td>264</td> <td>27</td> <td>66</td> </tr> <tr> <td>Tobacco-3482-small</td> <td>335</td> <td>240</td> <td>30</td> <td>65</td> </tr> </tbody> </table>

opencc-by-4.0Jun 2023View details →

The Belfort dataset: Handwritten Text Recognition from Crowdsourced Annotations

This dataset includes minutes of Belfort municipal council drawn up between 1790 and 1946. Documents include deliberations, lists of councillors, convocations, and agendas. The dataset includes 24,105 text-line images that were automatically detected from pages. Up to 4 transcriptions are available for each line image: two from humans, and two from automatic models. We would like to thank the Archives municipales de la ville de Belfort, France for giving us access to these documents.

opencc-by-4.0Jun 2023View details →

Data and codes: Speech-recognition in landlide predictive modelling

This is the data and codes for the manuscript "Speech-recognition in landlide predictive modelling"

opencc-by-4.0Apr 2023View details →

Supplemental Datasets for 'Multiplexed effector screening for recognition by endogenous resistance genes using positive defense reporters in wheat protoplasts'

Supplemental Datasets:Number File name Description S1 S1_Figure1_Data.csv Luminescence data for Figure 1, protoplast time course experiment S2 S2_RNAseqSampleTable.csv List of samples and treatment groups for RNAseq experiment S3 S3_LFC_1_genes.xlsx Lists of Gabo/GaboSr50, Fielder and all cultivar genes upregulated by log fold threshold of 1 S4 S4_GOTerms_Table.xlsx GO terms analysis Query tables and g:profiler results tables S5 S5_LFC2_genes.xlsx Lists of Gabo/GaboSr50, Fielder and all cultivar genes upregulated by log fold threshold of 2  S6 S6_Primers.xlsx List of primers used S7 S7_PlasmidList.xlsx List of plasmids used S8 S8_Figures3-5Data.xlsx Luminescence data used for generating Figures 3 - 7S9 S9_Figures3-7Stat.xlsx Stats output for Figures 3 - 7 S10 S10_SuppFiguresData.xlsx Data for Supplementary Figures S1 and S2

opencc-zeroJul 2023View details →

Recognition of sounds by ensembles of proteinoids

Proteinoids are artificial polymers that imitate certain characteristics of natural proteins, including self-organization, catalytic activity, and responsiveness to external stimuli. This paper investigates the potential of proteinoids as organic audio signal processors. We convert sounds of the English alphabet into waveforms of electrical potential, feed the waveforms into proteinoid solutions and record electrical responses of the proteinoids. We also undertake a detailed comparison of proteinoids' electrical responses (frequencies, periods, and amplitudes) with original input signals. We found that responses of proteinoids are less regular and have lower dominant frequency, wider distribution of proteinoids and less skewed distribution of amplitudes compared with input signals. We found that letters of the English alphabet uniquely map onto a pattern of electrical activity of a proteinoid ensemble, that is the proteinoid ensembles recognise spoken letters of the English alphabet. The finding will be used in further designs of organic electronic devices, based on ensembles of proteinoids, for sound processing and speech recognition.

opencc-by-4.0Jul 2023View details →

Appendix - Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition

Appendix tables for the paper "Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition". Feature informativeness information was gathered using SHAP values. TABLE VIII: Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral TABLE IX: (continue)Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral TABLE X: Table of Statistics and Individual t-Test Results Between Genders TABLE XI: (continue) Table of Statistics and Individual t-Test Results Between Genders TABLE XII: Table of 5 the most informative feature for each model, according to SHAP values TABLE XIII: (continue)Table of 5 the most informative feature for each model, according to SHAP values