*.csv (zip); *.mat; *.h5; *.pkl

These data is state estimation accuracy of the proposed algorithm.

These data includes the position estimation accuracy and velocity estimation accuracy of the algorithm.

The data are explained below:

rmse_ckf_1 and rmse_ckf_2 are the position accuracy and speed accuracy of CKF,respectively.

rmse_ukf_1 and rmse_ukf_2 are the position accuracy and speed accuracy of UKF,respectively.

rmse_ssmckf1_2 and rmse_ssmckf1_2 are the position accuracy and speed accuracy of SSM-RCKF when the similarity function is selected as exponentiac function, respectively.

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76 Views

Nowadays, more and more machine learning models have emerged in the field of sleep staging. However, they have not been widely used in practical situations, which may be due to the non-comprehensiveness of these models' clinical and subject background and the lack of persuasiveness and guarantee of generalization performance outside the given datasets. Meanwhile, polysomnogram (PSG), as the gold standard of sleep staging, is rather intrusive and expensive. In this paper, we propose a novel automatic sleep staging architecture calle

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156 Views

This dataset of 7200 channels is generated at different locations in the room area of 30x15x4 m3, where the locations are separated by 0.25m in both horizontal and vertical directions. Each AP uses 10 dBm TX power and 2D BF. In the concurrent mmWave BT scenario, all APs are operating, while in the single mmWave BT scenario, we consider a single AP fixed on the center of the room’s ceiling

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738 Views

This dataset is simulation output for our work on model swapping. It represents runs of a simulation where we swapped out the L1 cache model with different simple statistical models. 

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83 Views

Dataset description

This dataset contains EEG signals from 73 subjects (42 healthy; 31 disabled) using an ERP-based speller to control different brain-computer interface (BCI) applications. The demographics of the dataset can be found in info.txt. Additionally, you will find the results of the original study broken down by subject, the code to build the deep-learning models used in [1] (i.e., EEG-Inception, EEGNet, DeepConvNet, CNN-BLSTM) and a script to load the dataset.

Original article:

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2489 Views

Nurse Care Activity Recognition

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2045 Views

Drive test measurements of deployed LTE base stations located at and around the campus of The Technical Unversity of Denmark. Metrics of signal quality are obtained using TSMW equipment with a vehicle driving around the area. In addition to the radio measurements, a GNSS receiver is utilized for additional localization metrics. Approximate altitude information is provided by the GNSS. 

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554 Views

FallAllD is a large open dataset of human falls and activities of daily living simulated by 15 participants. FallAllD consists of 26420 files collected using three data-loggers worn on the waist, wrist and neck of the subjects. Motion signals are captured using an accelerometer, gyroscope, magnetometer and barometer with efficient configurations that suit the potential applications e.g. fall detection, fall prevention and human activity recognition.

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7993 Views