ALL-IDB (Acute Lymphoblastic Leukemia) Image Database for Image Processing
ALL-IDB dataset comprises of two subsets among them one subset has 260 segmented lymphocytes of them 130 belongs to the leukaemia and the remaining 130 belongs to the non leukaemuia class it requires only classification. second subset has around 108 non segmented blood images that belongs to the leukaemia and non leukaemia groups thus requires segmentation and classification.
Shoulder Physiotherapy Activity Recognition 9-Axis Dataset (SPARS9x)
Suggested uses of this dataset include performing supervised classification analysis of physiotherapy exercises, or to perform out-of-distribution detection analysis with unlabeled activities of daily living data.
(Please note that this dataset is currently only available in individual csv files for each subject as we are having difficulties uploading zipped folders. We have reached out to IEEEDataport to help us resolve this issue)
This dataset consists of 20 csv files, one for each of the subjects in the dataset (e.g. "spars9x_s0.csv" corresponds to the first subject of the dataset). These are contained in the zip file spars9xcsv.zip.
We have also provided the dataset in a numpy array format (spars9xnpy.zip), consisting of a single array and 20 subarrays, one for each dataset subject. These may be loaded into memory using the load() function of the python library numpy.
Either file needs to be decompressed (unzipped) before use.
0: Unlabeled Activities of Daily Living (Non-Exercise)
1: External Rotation (Isometric)
2: External Rotation in 90 Degrees Abduction in Scapula Plane
3: Internal Rotation (Isometric)
4: Extension (Isometric)
5: Abduction (Isometric)
6: Biceps Muscle Strengthening
7: Cross Chest Adduction
8: Active Flexion
9: Shoulder Girdle Stabilization with Elevation
10: Triceps Pull Downs
Inertial data and heart rate have been interpolated to 50 Hz (inertial sensor acquisition was approximately 50 Hz, but sensors recorded asynchronously and so interpolation was needed).
Each record consists of an Nx12 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
9: Heart Rate in bpm
10: Shoulder (0 = Unlabeled, 1 = Left Shoulder, 2 = Right Shoulder)
11: Activity Label (0-10 as described above)
Note: All exercise data has left or right shoulder indicated, whereas other activity data does not. Other activity data may be assigned to left or right shoulder for analysis, or split between each as required.
If using this data in academic publication, please cite the following manuscript:
Boyer, P.; Burns, D.; Whyne, C. Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors. Sensors 2021, 21, x. https://doi.org/10.3390/xxxxx
The dataset provides Abilify Oral user reviews and ratings for drug’s satisfaction, effectiveness, and ease of use on different age groups.
Gait analysis of people with transfemoral amputation is essential to support the rehabilitation process. In particular, the kinematics of the body center of mass (bCoM), derived from the motion of segments’ centers of mass (sCoM), provide crucial information about patients’ locomotion. Magneto-Inertial Measurement Units (MIMUs) may be adopted to obtain this information in-the-field. However, MIMUs provide the 3D acceleration of the origin of the sensor’s frame.
Gait data of a subject with transfemoral amputation. The subject was equipped with a set of 5 magneto-inertial measurement units (MIMUs) (Xsens Technologies B.V., Enschede, The Netherlands, 100 sample·s-1) located on the trunk (over the sternum), both prosthetic and sound thighs (ThighP, ThighS) and shanks (ShankP, ShankS) and a set of 59 reflective markers positioned on the patient’s anatomical landmarks. The participant was asked to walk in a straight line at his natural speed along an 8 m pathway with three force plates (AMTI, Advanced Mechanical Technology, Inc, Massachussets, USA, 1000 Hz) in its middle. Data acquisition was performed over a total of 6 trials. For each trial, only the prosthetic stride performed at steady state walking speed and occurring on the force plates was considered.
All relevant data are within the structure “Data.mat”. This structure array contains reference acceleration data from the force platforms, position data of the segments center of mass (SCoM) and of the MIMUs obtained using the positions of optical motion capture (OMC) markers and data fromMIMUs. All data are already synchronized.
We present here one of the first studies that attempt to differentiate between genuine and acted emotional expressions, using EEG data. We present the first EEG dataset with recordings of subjects with genuine and fake emotional expressions. We build our experimental paradigm for classification of smiles; genuine smiles, fake/acted smiles and neutral expression. For the full details please refere to our paper entitled:
Discrimination of Genuine and Acted Emotional Expressions using EEG Signal and Machine Learning
Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning.
Summary of Drug Events 2020
This data is related to Novel window for cancer nanotheranostics: non-invasive ocular assessments of tumor growth and nanotherapeutic treatment efficacy in vivo published at https://doi.org/10.1364/BOE.10.000151
The file also contains Deep Learning Codes for segmentation of Tumor using U-Net model. Training weights are also uploaded.
The dataset links to the survey performed on students and professors of Biological Engineering introductory course, as the Department of Biological Engineering, University of the Republic, Uruguay.
The dataset is meant for pure academic and non-commerical use.
For queries, please consult the corresponding author (Parag Chatterjee, email@example.com).
Microscopic image based analysis plays an important role in histopathological computer based diagnostics. Identification of childhood medulloblastoma and its proper subtype from biopsy tissue specimen of childhood tumor is an integral part for prognosis.The dataset is of Childhood medulloblastoma (CMB) biopsy samples. The images are of 10x and 100x microscopic magnifications, uploaded in separate folders. The images consist of normal brain tissue cell samples and CMB cell samples of different WHO defined subtypes. An excel sheet is also uploaded for ease of data description.
The dataset contains two folder of diffrent magnification images, i.e; 10x and 100x. The type of each image is described in the provided excel file. Each slide has a unique number and the number in bracket denotes that the corresponding image is of the single slide.