Wearable Sensing
This dataset is in support of my Research paper 'Detection of Pancreatic,Ovarian & Prostate Tumor, Cancer and Treatment by Ablation'.Due to computer crash, all work, datasets and old papers lost. Re-work may be submitted.
For Machine design, pls refer, open-access page 'Data and Designs of B-Machines'
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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).
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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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447 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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150 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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581 Views
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.
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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 publication I. Sanz-Pena, J. Blanco and J. H. Kim, "Computer Interface for Real-Time Gait Biofeedback Using a Wearable Integrated Sensor System for Data Acquisition," in IEEE Transactions on Human-Machine Systems, https://doi.org/10.1109/THMS.2021.3090738
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1.Visualization of convolutional neural network layers for one participant at ROI 301 * 301
2.Convolutional neural network structure analysis in Matlab
3.Convolutional neural network Matlab code
4.Videos of brightness mode (B-mode) ultrasound images from two participants during the recorded walking trials at 5 different speeds
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740 Views