Wearable Sensing

a novel two-electrode, frequency-scan electrical impedance tomography (EIT) system for gesture recognition

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The research were incorporated an extended cohort monitoring campaign, validation of an existing exposure model and development of a predictive model for COPD exacerbations evaluated against historical electronic health records.A miniature personal sensor unit were manufactured for the study from a prototype developed at the University of Cambridge. The units monitored GPS position, temperature, humidity, CO, NO, NO2, O3, PM10 and PM2.5.Three 6-month cohort monitoring campaigns were carried out, each including of 65 COPD patients.

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It's a monitoring of automatic grow system, is a study about soil moisture control. We take reads from soil moisture capacitive sensor and DHT11 for a experiment about control of agriculture parameters

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Any work using this dataset should cite this paper as follows:

Nirmalya Thakur and Chia Y. Han, "Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions", Journal of Data, Volume 6, Issue 8, pp. 1-21, 2021.

Abstract

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Objective: Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation for conditions like osteoarthritis, stroke, and Parkinson's disease. Optical motion capture systems are the standard for estimating kinematics, but the equipment is expensive and requires a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture.

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A dataset of beach volleyball serves movements annotated in 4 types (i.e. long float, long top spin, short float, short top spin) and free games. The dataset comprises the data of 10 users in 4 sessions, recorded using 4 inertial sensors per user.

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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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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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69 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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