IoT
This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for respiration rate measurements in a standard 3m x 3m room. The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool. The Wi-Fi CSI data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.
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In this paper, we develop an internet of medical things (IoMT)-based electrocardiogram(ECG) recorder for monitoring heart conditions in practical cases. To remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that utilizes deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for the noise cancellation.
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Data of cricket bowlers was recorded using wearable IMU sensors. Data were collected from the designed sensor units placed on the thigh and tibia of the front leg of each bowler using a strap. Recorded data include 3-axes accelerometer data and 4-dimensional quaternion data.
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Supplementary material for the article "A Sensor Network Utilizing Consumer Wearables for Telerehabilitation of Post-acute COVID-19 Patients": (1) TERESA (TEleREhabilitation Self-training Assistant) back-end application API documentation and (2) anonymous details of the Wristband protocols used in the study.
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<p>This article created to address the </p><p>current state of affairs, which has </p><p>resulted in an insufficient progress and </p><p>innovation system. The purpose of this </p><p>overview article is to increase educate </p><p>society's knowledge of how to use </p><p>modern and innovative technologies </p><p>based on need, cultural aspects, social </p><p>context, and state context.
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This data set contains data collected from an overhead crane (https://doi.org/10.1109/WF-IoT.2018.8355217) OPC UA server when driving an L-shaped path with different loads (0kg, 120kg, 500kg, and 1000kg). Each driving cycle was driven with an anti-sway system activated and deactivated. Each driving cycle consisted of repeating five times the process of lifting the weight, driving from point A to point B along with the path, lowering the weight, lifting the weight, driving back to point A, and lowering the weight.
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Supplementary dataset for "Smart homes for early diagnosis of mobility decline: A scoping review"
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Our data set has 5136 records collected in 214 days. The sampling rate of the sensors is 1 hour. Each record includes the number of vehicles entering and leaving the parking lot in an hour, the CO2 concentration of every building floor at the recording time, and the power consumption of each floor in an hour.
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This data set contains measurements on reading and writing data to OPC UA servers directly and via REST and GraphQL interfaces. Each measurement is conducted 1000 times. Measurements include reading a single value and reading 50 values. Measurements using cache server were also performed. Measurement data is collected with Wireshark and the .csv files are exported from it. in addition, .txt files contain request execution times recorded by the client.
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