The video demonstrates an accurate, low-latency body tracking approach for VR-based applications using Vive Trackers. Using a HTC Vive headset and Vive Trackers, an immersive VR experience, by animating the motions of the avatar as smoothly, rapidly and as accurately as possible, has been created. The user can see her from the first-person view.

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Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies.  Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.

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This dataset consists of measurements from a foot-mounted inertial measurement unit (IMU). In total, we provide data from five different test subjects travelling over more than 7.6 km. The data are combined with various forms of ground truth positioning information that can be used to evaluate the accuracy of a zero-velocity-aided, foot-mounted inertial navigation system (INS).

Instructions: 

Herein, we provide inertial data (produced from an inertial measurement unit) collected in three different test environments. Each environment contains multiple trajectories, and we provide the raw, foot-mounted inertial measurements for each trajectory. Additionally, we include forms of ground truth for each trajectory (the form of ground truth varies within each test environment). For each trajectory, we include processed results that can be used for benchmarking other systems. Our processed results (six degree-of-freedom trajectory estimates) are generated from a baseline zero-velocity-aided inertial navigation system (INS).

Once the dataset has been downloaded, it should be unpacked into the PyShoe repository in order to use the available tools with the provided dataset: To do so, unzip the results and data folders into the main directory. The correct folder structure is as follows:

| - - pyshoe

|        | - - data

|                | - - hallway

|                | - - stairs

|                | - - vicon

|         | - - results

Following this, we refer you to the readme instructions in the Github repository for detailed instructions on how the data can be used with our open-source INS. See the individual readme files within the various data subdirectories to understand how the dataset is formatted.

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Dataset I mainly consists of 30 subjects, which are respectively composed of gait data collected by mobile phone placed on arm, wrist, hand, waist, and ankle. This dataset is used to verify the impact of the mobile phone's placement on the recognition effect. Dataset II and Dataset III are composed of 113 subjects. Dataset II is the data collected from a mobile phone placed in the hand position, while Dataset III is the gait data collected from a mobile phone placed in the waist position. These two data sets are used primarily to verify the identification effect of the proposed model.

Instructions: 

There are five subfiles in the folder "DataSet I": arm, wrist, hand, waist, and ankle. Each subfile contains training set and test set files, and there are 30 .csv files under each training set and test set belonging to 30 subjects respectively.

The DataSet II and DataSet III folders directly contain two subfiles, the training set, and the test set, because they only collect the gait data of a certain location. There are 113 .csv files in both the training set and test set folders, which belonging to 113 subjects respectively.

       All data sets are raw data that has been stripped of the head and tail useless data. 

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Dataset I mainly consists of 30 subjects, which are respectively composed of gait data collected by mobile phone placed on arm, wrist, hand, waist, and ankle. This dataset is used to verify the impact of the mobile phone's placement on the recognition effect. Dataset II and Dataset III are composed of 113 subjects. Dataset II is the data collected from a mobile phone placed in the hand position, while Dataset III is the gait data collected from a mobile phone placed in the waist position. These two data sets are used primarily to verify the identification effect of the proposed model.

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17 Views

Dataset I mainly consists of 30 subjects, which are respectively composed of gait data collected by mobile phone placed on arm, wrist, hand, waist, and ankle. This dataset is used to verify the impact of the mobile phone's placement on the recognition effect. Dataset II and Dataset III are composed of 113 subjects. Dataset II is the data collected from a mobile phone placed in the hand position, while Dataset III is the gait data collected from a mobile phone placed in the waist position. These two data sets are used primarily to verify the identification effect of the proposed model.

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The uploaded data are for the paper: "A Wearable Skin Temperature Monitoring System for Early Detection of Infections". Baseline kin temperature measurement data from all 5 volunteers (subjects) who wore the wearable band for 3-5 days are included along with 5-day temperature measurement data with anomalies of one volunteer who wore both the smart band and a heating pad. Augmented data generated using the methods described in the paper for COVID-19 infection anomaly detection are also included 

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The given Dataset is record of different age group people either diabetic or non diabetic for theie blood glucose level reading with superficial body features like body temperature, heart rate, blood pressure etc.

The main purpose of the dataset is to understand the effect of blood glucose level on human body. 

The different superficial body parameters show sifnificant variation according to change in blood glucose level.

Instructions: 

The use of dataset to be done for machine learning analysis or study purpose only. No medical implementations to be claimed using the given dataset.

 

 

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The data provided corresponds to the open-source codes and reference images from a computer interface for real-time gait biofeedback using a Wearable Integrated Sensor System for Data Acquisition.This data is the supplmementary material of the study titled Computer Interface for Real-time Gait Biofeedback using a Wearable Integrated Sensor System for Data Acquisition, accepted for publication in the IEEE Transactions on Human-Machine Systems journal (June 2021). 

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Human activity recognition (HAR) has been one of the most prevailing and persuasive research topics in different fields for the past few decades. The main idea is to comprehend individuals’ regular activities by looking at bits of knowledge accumulated from people and their encompassing living environments based on sensor observations. HAR has a great impact on human-robot collaborative work, especially in industrial works. In compliance with this idea, we have organized this year’s Bento Packaging Activity Recognition Challenge.

Last Updated On: 
Thu, 07/01/2021 - 02:09
Citation Author(s): 
Sayeda Shamma Alia, Kohei Adachi, Paula Lago, Nazmun Nahid, Haru Kaneko, Sozo Inoue

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