Real sampled data of a moving vehicle is provided.

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Multi-modal Exercises Dataset is a multi- sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD).The MEx Dataset contains data from 25 people recorded with four sensors, 2 accelerometers, a pressure mat and a depth camera.

Instructions: 

The MEx Multi-modal Exercise dataset contains data of 7 different physiotherapy exercises, performed by 30 subjects recorded with 2 accelerometers, a pressure mat and a depth camera.

Application

The dataset can be used for exercise recognition, exercise quality assessment and exercise counting, by developing algorithms for pre-processing, feature extraction, multi-modal sensor fusion, segmentation and classification.

 

Data collection method

Each subject was given a sheet of 7 exercises with instructions to perform the exercise at the beginning of the session. At the beginning of each exercise the researcher demonstrated the exercise to the subject, then the subject performed the exercise for maximum 60 seconds while being recorded with four sensors. During the recording, the researcher did not give any advice or kept count or time to enforce a rhythm.

 

Sensors

Obbrec Astra Depth Camera 

-       sampling frequency – 15Hz 

-       frame size – 240x320

 

Sensing Tex Pressure Mat

-       sampling frequency – 15Hz

-       frame size – 32*16

Axivity AX3 3-Axis Logging Accelerometer

-       sampling frequency – 100Hz

-       range – 8g

 

Sensor Placement

All the exercises were performed lying down on the mat while the subject wearing two accelerometers on the wrist and the thigh. The depth camera was placed above the subject facing down-words recording an aerial view. Top of the depth camera frame was aligned with the top of the pressure mat frame and the subject’s shoulders such that the face will not be included in the depth camera video.

 

Data folder

MEx folder has four folders, one for each sensor. Inside each sensor folder,

30 folders can be found, one for each subject. In each subject folder, 8 files can be found for each exercise with 2 files for exercise 4 as it is performed on two sides. (The user 22 will only have 7 files as they performed the exercise 4 on only one side.)  One line in the data files correspond to one timestamped and sensory data.

 

Attribute Information

 

The 4 columns in the act and acw files is organized as follows:

1 – timestamp

2 – x value

3 – y value

4 – z value

Min value = -8

Max value = +8

 

The 513 columns in the pm file is organized as follows:

1 - timestamp

2-513 – pressure mat data frame (32x16)

Min value – 0

Max value – 1

 

The 193 columns in the dc file is organized as follows:

1 - timestamp

2-193 – depth camera data frame (12x16)

 

dc data frame is scaled down from 240x320 to 12x16 using the OpenCV resize algorithm

Min value – 0

Max value – 1

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WiFi measurements dataset for WiFi fingerprint indoor localization compiled on the first and ground floors of the Escuela Técnica Superior de Ingeniería Informática, in Seville, Spain. The facility has 24.000 m² approximately, although only accessible areas were compiled.

Instructions: 

The training dataset consists of 7175 fingerprints collected from 489 different locations. Each fingerprint is stored as a JSON object corresponding to an unique scan with the following values:

  • _id: contains an unique identifier for the fingerprint, uses to differentiate one fingerprint from another.

  • avgMagneticMagnitude: average magnetic magnitude during scanning with the mobile phone sensor, although this value is not used is provided in case it was useful.

  • location: object with the coordinates of the real world in which the sample was captured.

    • floor: number indicating the floor in which the sample was captured.

    • lat: latitude as part of the coordinate at which the sample was captured.

    • lon: longitude as part of the coordinate at which the sample was captured.

  • timestamp: UNIX timestamp in which the sample was captured.

  • userId: identifier of the user who captured the sample, this value will be anonymized so that it is not directly identifiable but remains unique.

  • wifiDevices: list of APs appearing in the sample.

    • bssid: unique AP identifier, this value will be anonymized so that it is not directly identifiable but remains unique.

    • frequency: AP WiFi frequency.

    • level: AP WiFi signal strength (RSSI).

    • ssid: AP name, this value will be anonymized so that it is not directly identifiable but can be used to compare APs with the same name.

The training dataset was compiled by taking samples at every 3 meters on average with 15 samples per location. The time at each location was approximately 40 seconds performing consecutive scans with a bq Aquaris E5 4G device using Android stock 6.0.1 without making any movements during the process. The following is an example of a fingerprint, the list of WiFi devices has been shortened to two APs, as it was too long.

{
"_id":"5cc81e8ac28d6d2533709425",
"avgMagneticMagnitude":40.615368,
"location":{
"floor":1,
"lat": 37.357746,
"lon": -5.9878354
},
"timestamp":1556618890,
"userId":"USER-0",
"wifiDevices":[
{
"bssid":"AP-BSSID-0",
"frequency":2457,
"level":-75,
"ssid":"AP-SSID-0"
},
...
{
"bssid":"AP-BSSID-23",
"frequency":2437,
"level":-64,
"ssid":"AP-SSID-6"
}
]
}

The testing dataset consists of two tests with a total of 390 samples in random locations yet in areas captured by the training dataset and with different devices. This dataset is grouped by tests and within it are the captured samples, so both the individual error and the average error can be obtained, besides recalculating this error to test different algorithms. Each test is stored as a JSON object corresponding to an unique scan with the following values:

  • _id: contains an unique identifier for the test, uses to differentiate one test from another.

  • userId: identifier of the user who performed the test, this value will be anonymized so that it is not directly identifiable but remains unique.

  • startTimestamp: UNIX timestamp that indicates when the test was started.

  • endTimestamp: UNIX timestamp that indicates when the test was ended.

  • samples: list of samples taken during testing.

    • timestamp: UNIX timestamp that indicates when the sample was collected.

    • real: object with the coordinates of the real world in which the sample was captured.

      • floor: number indicating the floor in which the sample was captured.

      • lat: latitude as part of the coordinate at which the sample was captured.

      • lon: longitude as part of the coordinate at which the sample was captured.

    • predicted: object with the predicted coordinates of the real world.

      • floor: number indicating the floor predicted.

      • lat: latitude as part of the predicted coordinate.

      • lon: longitude as part of the predicted coordinate.

    • wifiDevices: list of APs appearing in the sample.

      • bssid: unique AP identifier, this value will be anonymized so that it is not directly identifiable but remains unique.

      • frequency: AP WiFi frequency.

      • level: AP WiFi signal strength (RSSI).

      • ssid: AP name, this value will be anonymized so that it is not directly identifiable but can be used to compare APs with the same name.

    • error: approximate distance between the actual location and the predicted location.

  • error: average distance between the actual locations and the predicted locations.

The testing dataset was compiled two days after the training phase by taking samples at random locations with an average of 3 meters, performing a single scan per location. The samples were taken with two devices, which represent each of the tests individually, a bq Aquaris E5 4G device using Android stock 6.0.1 and a Xiaomi Redmi 4X using Android 7.1.2 with MIUI 10 Global 9.5.16. Before taking the sample, 5 seconds were waited without making any movements. The following is an example of a test entry, the list of samples has been shortened to one sample and wifi devices has been shortened to two APs, as it was too long.

{
"_id":"5d13245e279a550b548e3bfe",
"userId":"USER-0",
"startTimestamp": 1557212799.6555429,
"endTimestamp": 1557222705.0710876,
"samples":[
{
"timestamp":1557212799.6552203,
"real":{
"floor":0,
"lat":37.358547,
"lon":-5.9867215
},
"predicted":{
"floor":0,
"lat":37.358547,
"lon":-5.9868493
},
"wifiDevices":[
{
"bssid":"AP-BSSID-156",
"frequency":2412,
"level":-80,
"ssid":"AP-SSID-5"
},
...
{
"bssid":"AP-BSSID-146",
"frequency":2462,
"level":-36,
"ssid":"AP-SSID-6"
}
],
"error":5.233510868645419
},
...
],
"error":3.975672826048607
}

In order to provide more information about the device used in each fingerprint of the dataset, the following relationship between users and devices is given:

USER-0: Xiaomi Redmi 4X (Android 7.1.2 with MIUI 10 Global 9.5.16)

USER-1: BQ Aquaris E5 4G (Android stock 6.0.1)

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Herein we present a multi-threshold-based constant micro-increment control strategy to detect and suppress the slip for the prosthetic hand, and to minimize the loading force increment after the stabilization. The proposed strategy primarily encompasses slipping process model, multi-threshold detection method, constant micro-increment controller and a preset filter. First and foremost, a slipping process model is proposed that involves the nonlinear and noise characteristics of the system.

Instructions: 

There are two sets of experiments, a total of four videos, which need to be decompressed and watched.

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The videos demonstrate 2D thermal gradient mappings based on two pairs of 50 µm x- an y- thin film thermocouple (TFTC) sensors. We investigate thin film thermocouples (TFTC) asthermal gradient sensors at the micro-scale and demonstrate two-dimensional dynamic thermal gradient mapping for features as small as 20 μm. Pairs of x-direction and y-direction thermocouples sense the thermal gradient while another calibrates the Seebeck coefficient as S = 20.33±0.01μV/K. The smallest detectable temperature difference is 10 mK, and the sensitivity is 0.5 mK/μm.

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Abstractــــ Wireless Sensor Networks (WSNs) typically have been consumed high energy in monitoring and radio communication trends. Furthermore, data diffusion modes in WSN typically generate errors such as noisy values, incorrect measurements or missing information, which minimize the standard of performance in such dynamic systems.

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Indoor positioning systems based on radio frequency systems such as UWB inherently present multipath related phenomena. This causes ranging systems such as UWB}to lose accuracy by detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will make important errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques for a previous classification and mitigation of the propagation effects.

Instructions: 

Please, if you use this dataset in your research activities, please add a reference to our related paper:

 

Barral, V.; Escudero, C.J.; García-Naya, J.A.; Suárez-Casal, P. Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems. Sensors 2019, 19, 5438. https://doi.org/10.3390/s19245438

 

 

 

These are Matlab files.

 

The measurements were recorded in the scenario shown in the figure.

Three configurations where used:

- "h0_front" contains the measurements with the tag facing North at the same height than the anchors.Height = 1.28m.

- "h0_back.mat " includes the measurements  with the tag facing South at the same height than the anchors. Height = 1.28m.

- "h1_front" includes the measurements with the tag facing North at a higher altitude  than the anchors. Tag Height = 2.05m. Anchors Height = 1.28m.

 

How to use:

 

In Matlab:

 

 

 

h0_front = load('h0_front.mat');

h0_back = load('h0_back.mat');

 

h1_front = load('h1_front.mat');

 

 

where

"h0_front" contains the measurements with the tag facing North at the same height than the anchors.

"h0_back.mat " includes the measurements  with the tag facing South at the same height than the anchors.

 "h1_front" includes the measurements with the tag facing North at a higher altitude  than the anchors.

 

 

The file contains 3 arrays:

- beacons (1x5 struct) Contains the coordinates of each of the 5 anchors.

- pos (1x9 struct) Contains the coordinates of the 9 measurement points.

 

- ranging (1x9 struct) Each row contains the measurements from one of the 9 positions of the array. The struct  includes:

 

 

- range (Nx1) int64. The value outputted by the device. In cm.

 

- rxPower (Nx1) double. The received power strength. In dBm.

 

- timestamp (Nx1) double. Measurement timestamp. In unix time.

 

- angle (Nx1) double. Not used in this set.

- destinationId (Nx1) int64. The index of the anchor

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ebooks

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Database for FMCW THz radars (HR workspace) and sample code for federated learning 

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A set of datasets (Excel files since it also contains dynamically generated images) to facilitate the reproducibity of the test performed for the evaluation of the FDS proposal

Instructions: 

Description: The set of datasets used for the paper in order to get the graphics that summarize the results from these datasets.Size: 293 KBPlatform: Microsoft Excel 365Environment: Any running MS Excel (Linux, Windows, MacOS)Major Component Description: 3 files are provided, each corresponding to a different dataset(s) with several sheet.1. "Comm latency": contains datasets and figures related with the data obtained when evaluating the communication latency of the proposal and the Amazon Cloud service one.    · "Measures FDS": sheet that contain the data obtained when evaluating the communication latency of the proposal (FDS)    · "Measures Cloud": sheet that contain the data obtained when evaluating the communication latency of Amazon Cloud service    · "Comparison": a sheet containing a dynamic figure for comparing the data of the other two sheets2. "Power consumption": the largest dataset. It contains the samples for different situations regarding power consumption in the sensor nodes when using FDS or when running as they normally do (without storing any kind of data)    ·Idle (DTIM1) naked 3,3V: sheet contains the samples for the "base" power consumption of a "bare-mounted" ESP8266 avoiding a programmer IC and a voltage regulator. Constant current was provided using an external power generator    ·TX no SD card: sheet contains the power consumption data while transmitting when FDS is not used (no SD data storage is performed)    ·RX no SD card: sheet contains the power consumption data while receiving when FDS is not used (no SD data storage is performed)    ·RXTX no SD card: sheet contains the power consumption data while transmitting and receiving when FDS is not used (no SD data storage is performed)    ·TX with SD card: sheet contains the power consumption data while transmitting when FDS is used (SD data storage is performed)    ·RX with SD card: sheet contains the power consumption data while receiving when FDS is used (SD data storage is performed)    ·RXTX with SD card: sheet contains the power consumption data while transmitting and receiving when FDS is used (SD data storage is performed)        ·Comparison: sheet including the dynamic figure generated from the other sheets3. "Network overload": contains datasets and figures related with the data obtained when evaluating the network overload of the proposal and the Amazon Cloud service one (best and worst scenario).    · Received: sheet contains number of messages for different replicates configuration in FDS as well as counts for the number of messages for the best and worst case for Amazon regarding messages received.    · Sent: sheet contains number of messages for different replicates configuration in FDS regarding messages sent.Detailed Set-up Instructions: Unzip and open the files with MS ExcelContact Information: for additional information about these datasets, contact Marino Linaje, the corresponding author (mlinaje@unex.es)

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