441 research outputs found
ShimFall&ADL: Triaxial accelerometer fall and activities of daily living detection dataset
No full textShimFall&ADL dataset
Version 1.0 (2020-06-19)
Please cite as: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"
Disclaimer
While every care has been taken to ensure the accuracy of the data included in the ShimFall&ADL dataset, the authors and the University of the West of Scotland do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.
Contact
For inquiries regarding the ShimFall&ADL dataset, please contact:
Dr Stamos Katsigiannis, [email protected], University of the West of Scotland
Prof. Naeem Ramzan, [email protected], University of the West of Scotland
Acknowledgment
The authors would like to thank Md. Hasan Shahriar for the data collection under his MSc project.
Dataset summary
The ShimFall&ADL dataset contains recordings from 35 individuals, acquired using a chest-strapped Shimmer v2 tri-axial accelerometer, recording at a 50Hz sampling rate. Experiments were conducted in a controlled environment at a research lab in the University of the West of Scotland. Thirty five (35) healthy individuals were recruited among young or mid-aged volunteers, aged between 19 and 34 years old, having a body weight between 52 and 113 kg, and a body height between 1.45 and 1.82 m.
Participants performed the following activities of daily living (ADL):
Jumping
Lying down
Bending/picking up
Sitting to a chair
Standing up from a chair
Walking
Participants performed the following falls:
Steep (hard)
Front (soft)
Front (hard)
Left (soft)
Left (hard)
Right (soft)
Right (hard)
Back (soft)
Back (hard)
Data
Each ".dat" file in the dataset corresponds to one event for one individual and contains 101 accelerometer samples corresponding to the event. Each row of the file corresponds to one 3-channel sample, dividing the x, y, z axes values using the "\t" character, as follows:
Row 1: x1\ty1\tz1
Row 2: x2\ty2\tz2
...
Row N: xN\tyN\tzN
The files within the dataset are named as follows:
adl__.dat
fall__.dat
For example, the file "adl_standingfromchair_18.dat" corresponds to the accelerometer recording of the 18th participant, performing the "standing up from chair" ADL. The file, "leftfall_soft_11.dat" corresponds to the accelerometer recording of the 11th participant, performing a soft left fall.
Additional information
For additional information regarding the creation of the ShimFall&ADL dataset, please refer to the associated publication: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777
DREAMER: A Database for Emotion Recognition through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices
Full text link<p>We present DREAMER, a multi-modal database consisting of electroencephalogram (EEG) and electrocardiogram (ECG) signals recorded during affect elicitation by means of audio-visual stimuli. Signals from 23 participants were recorded along with the participants' self-assessment of their affective state after each stimuli, in terms of valence, arousal, and dominance. All the signals were captured using portable, wearable, wireless, low-cost and off-the-shelf equipment that has the potential to allow the use of affective computing methods in everyday applications. The Emotiv EPOC wireless EEG headset was used for EEG and the Shimmer2 ECG sensor for ECG.</p><p>Classification results for valence, arousal and dominance of the proposed database are comparable to the ones achieved for other databases that use non-portable, expensive, medical grade devices.</p><p>The proposed database is made publicly available in order to allow researchers to achieve a more thorough evaluation of the suitability of these capturing devices for affect recognition applications.</p><p> </p><p>Please cite as:</p><p>S. Katsigiannis, N. Ramzan, "DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 1, pp. 98-107, Jan. 2018. doi: 10.1109/JBHI.2017.2688239</p>
A machine learning driven solution to the problem of perceptual video quality metrics
Full text linkThe advent of high-speed internet connections, advanced video coding algorithms, and consumer-grade computers with high computational capabilities has led videostreaming-over-the-internet to make up the majority of network traffic. This effect has led to a continuously expanding video streaming industry that seeks to offer enhanced quality-of-experience (QoE) to its users at the lowest cost possible. Video streaming services are now able to adapt to the hardware and network restrictions that each user faces and thus provide the best experience possible under those restrictions. The most common way to adapt to network bandwidth restrictions is to offer a video stream at the highest possible visual quality, for the maximum achievable bitrate under the network connection in use. This is achieved by storing various pre-encoded versions of the video content with different bitrate and visual quality settings. Visual quality is measured by means of objective quality metrics, such as the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF), and others, which can be easily computed analytically. Nevertheless, it is widely accepted that although these metrics provide an accurate estimate of the statistical quality degradation, they do not reflect the viewer’s perception of visual quality accurately. As a result, the acquisition of user ratings in the form of Mean Opinion Scores (MOS) remains the most accurate depiction of human-perceived video quality, albeit very costly and time consuming, and thus cannot be practically employed by video streaming providers that have hundreds or thousands of videos in their catalogues. A recent very promising approach for addressing this limitation is the use of machine learning techniques in order to train models that represent human video quality perception more accurately. To this end, regression techniques are used in order to map objective quality metrics to human video quality ratings, acquired for a large number of diverse video sequences. Results have been very promising, with approaches like the Video Multimethod Assessment Fusion (VMAF) metric achieving higher correlations to useracquired MOS ratings compared to traditional widely used objective quality metrics
EEG-based biometrics: Effects of template ageing
Full text linkThis chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations of EEG signals and examine the difference of performance in subject identification between single session and cross-session identification experiments. In order to do this, EEG signals are characterised with common state-of-the-art features, i.e. Mel Frequency Cepstral Coefficients (MFCC), Autoregression Coefficients, and Power Spectral Density-derived features. The samples were later classified using various classifiers, including Support Vector Machines and k-Nearest Neighbours with different parametrisations. Results show that performance tends to be worse for crosssession identification compared to single session identification. This finding suggests that temporal permanence of EEG signals is limited and thus more sophisticated methods are needed in order to characterise EEG signals for the task of subject identificatio
Machine learning-based affect detection within the context of human-horse interaction
Full text linkThis chapter focuses on the use of machine learning techniques within the field of affective computing, and more specifically for the task of emotion recognition within the context of human-horse interaction. Affective computing focuses on the detection and interpretation of human emotion, an application that could significantly benefit quantitative studies in the field of animal assisted therapy. The chapter offers a thorough description, an experimental design, and experimental results on the use of physiological signals, such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals, for the creation and evaluation of machine learning models for the prediction of the emotional state of an individual during interaction with horses
Analysis of multimodal learning styles in the contemporary medical school
No full textAzhar-Ramzan Bilal, Zishan Naeem, Zain NisarFaculty of Medicine, St George’s Hospital Medical School, London, UK Undergraduate teaching represents a significant change in education style for individuals, presenting a challenge to preclinical medical students. Based on this, we were greatly interested in the study conducted by Parashar et al regarding the assessment of learning styles of medical students in India in the era of digitization.1 Pertinently, the authors identified most students possessed multimodal learning styles, with auditory and kinesthetic being most common. As medical students within the UK, we would like to offer our perspective through discussing the applicability of authors recommendations to the contemporary medical school in the UK. View the original paper by Parashar and colleagues
BED: Biometric EEG dataset
No full textThe BED dataset
Version 1.0.0
Please cite as: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.
Disclaimer
While every care has been taken to ensure the accuracy of the data included in the BED dataset, the authors and the University of the West of Scotland, Durham University, and Universitat de València do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.
Contact
For inquiries regarding the BED dataset, please contact:
Dr Pablo Arnau-González, arnau.pablo [*AT*] gmail.com
Dr Stamos Katsigiannis, stamos.katsigiannis [*AT*] durham.ac.uk
Prof. Miguel Arevalillo-Herráez, miguel.arevalillo [*AT*] uv.es
Prof. Naeem Ramzan, Naeem.Ramzan [*AT*] uws.ac.uk
Dataset summary
BED (Biometric EEG Dataset) is a dataset specifically designed to test EEG-based biometric approaches that use relatively inexpensive consumer-grade devices, more specifically the Emotiv EPOC+ in this case. This dataset includes EEG responses from 21 subjects to 12 different stimuli, across 3 different chronologically disjointed sessions. We have also considered stimuli aimed to elicit different affective states, so as to facilitate future research on the influence of emotions on EEG-based biometric tasks. In addition, we provide a baseline performance analysis to outline the potential of consumer-grade EEG devices for subject identification and verification. It must be noted that, in this work, EEG data were acquired in a controlled environment in order to reduce the variability in the acquired data stemming from external conditions.
The stimuli include:
Images selected to elicit specific emotions
Mathematical computations (2-digit additions)
Resting-state with eyes closed
Resting-state with eyes open
Visual Evoked Potentials at 2, 5, 7, 10 Hz - Standard checker-board pattern with pattern reversal
Visual Evoked Potentials at 2, 5, 7, 10 Hz - Flashing with a plain colour, set as black
For more details regarding the experimental protocol and the design of the dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, 2021. (Under review)
Dataset structure and contents
The BED dataset contains EEG recordings from 21 subjects, acquired during 3 similar sessions for each subject. The sessions were spaced one week apart from each other.
The BED dataset includes:
The raw EEG recordings with no pre-processing and the log files of the experimental procedure, in text format
The EEG recordings with no pre-processing, segmented, structured and annotated according to the presented stimuli, in Matlab format
The features extracted from each EEG segment, as described in the associated publication
The dataset is organised in 3 folders:
RAW
RAW_PARSED
Features
RAW/ Contains the RAW files
RAW/sN/ Contains the RAW files associated with subject N
Each folder sN is composed by the following files:
- sN_s1.csv, sN_s2.csv, sN_s3.csv -- Files containing the EEG recordings for subject N and session 1, 2, and 3, respectively. These files contain 39 columns:
COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 ...UNUSED DATA... UNIX_TIMESTAMP
- subject_N_session_1_time_X.log, subject_N_session_2_time_X.log, subject_N_session_3_time_X.log -- Log files containing the sequence of events for the subject N and the session 1,2, and 3 respectively.
RAW_PARSED/
Contains Matlab files named sN_sM.mat. The files contain the recordings for the subject N in the session M. These files are composed by two variables:
- recording: size (time@256Hz x 17), Columns: COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 UNIX_TIMESTAMP
- events: cell array with size (events x 3) START_UNIX END_UNIX ADDITIONAL_INFO
START_UNIX is the UNIX timestamp in which the event starts
END_UNIX is the UNIX timestamp in which the event ends
ADDITIONAL INFO contains a struct with additional information regarding the specific event, in the case of the images, the expected score, the voted score, in the case of the cognitive task the input, in the case of the VEP the pattern and the frequency, etc..
Features/
Features/Identification
Features/Identification/[ARRC|MFCC|SPEC]/: Each of these folders contain the extracted features ready for classification for each of the stimuli, each file is composed by two variables, "feat" the feature matrix and "Y" the label matrix.
- feat: N x number of features
- Y: N x 2 (the #subject and the #session)
- INFO: Contains details about the event same as the ADDITIONAL INFO
Features/Verification: This folder is composed by 3 different files each of them with one different set of features extracted. Each file is composed by one cstruct array composed by:
- data: the time-series features, as described in the paper
- y: the #subject
- stimuli: the stimuli by name
- session: the #session
- INFO: Contains details about the event
The features provided are in sequential order, so index 1 and index 2, etc. are sequential in time if they belong to the same stimulus.
Additional information
For additional information regarding the creation of the BED dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021
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