Dataset Information

From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data.

ABSTRACT: Wearable technology is expanding for motion monitoring. However, open challenges still limit its widespread use, especially in low-cost systems. Most solutions are either expensive commercial products or lower performance ad-hoc systems. Moreover, few datasets are available for the development of complete and general solutions. This work presents 2 datasets, with low-cost and high-end Magnetic, Angular Rate, and Gravity(MARG) sensor data. Provides data for the complete inertial pose pipeline analysis, starting from raw data, sensor-to-segment calibration, multi-sensor fusion, skeleton-kinematics, to complete Human pose. Contains data from 21 and 10 participants, respectively, performing 6 types of sequences, presenting high variability and complex dynamics with almost complete range-of-motion. Amounts to 3.5 M samples, synchronized with a ground-truth inertial motion capture system. Presents a method to evaluate data quality. This database may contribute to develop novel algorithms for each pipeline's processing steps, with applications in inertial pose estimation algorithms, human movement forecasting, and motion assessment in industrial or rehabilitation settings. All data and code to process and analyze the complete pipeline is freely available.

SUBMITTER: Palermo M

PROVIDER: S-EPMC9525570 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data.

Palermo Manuel M Cerqueira Sara M SM André João J Pereira António A Santos Cristina P CP

Scientific data 20220930 1

Wearable technology is expanding for motion monitoring. However, open challenges still limit its widespread use, especially in low-cost systems. Most solutions are either expensive commercial products or lower performance ad-hoc systems. Moreover, few datasets are available for the development of complete and general solutions. This work presents 2 datasets, with low-cost and high-end Magnetic, Angular Rate, and Gravity(MARG) sensor data. Provides data for the complete inertial pose pipeline ana ...[more]

PMID: 36180479

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Project description:This article presents the capabilities and selected measurement results from the newly developed low-cost air pollution measurement system mounted on an unmanned aerial vehicle (UAV). The system is designed and manufactured by the authors and is intended to facilitate, accelerate, and ensure the safety of operators when measuring air pollutants. It allows the creation of three-dimensional models and measurement visualizations, thanks to which it is possible to observe the location of leakage of substances and the direction of air pollution spread by various types of substances. Based on these models, it is possible to create area audits and strategies for the elimination of pollution sources. Thanks to the usage of a multi-socket microprocessor system, the combination of nine different air quality sensors can be installed in a very small device. The possibility of simultaneously measuring several different substances has been achieved at a very low cost for building the sensor unit: 70 EUR. The very small size of this device makes it easy and safe to mount it on a small drone (UAV). Because of this device, many harmful chemical compounds such as ammonia, hexane, benzene, carbon monoxide, and carbon dioxide, as well as flammable substances such as hydrogen and methane, can be detected. Additionally, a very important function is the ability to perform measurements of PM2.5 and PM10 suspended particulates. Thanks to the use of UAV, the measurement is carried out remotely by the operator, which allows us to avoid the direct exposure of humans to harmful factors. A big advantage is the quick measurement of large spaces, at different heights above the ground, in different weather conditions. Because of the three-dimensional positioning from GPS receiver, users can plot points and use colors reflecting a concentration of measured features to better visualize the air pollution. A human-friendly data output can be used to determine the mostly hazardous regions of the sampled area.

Project description:BackgroundUpper-limb impairments in stroke patients are usually measured in clinical setting using standard clinical assessment. In addition, kinematic analysis using opto-electronic systems has been used in the laboratory setting to map arm recovery. Such kinematic measurements cannot capture the actual function of the upper extremity in daily life. The aim of this study is to longitudinally explore the complementarity of post-stroke upper-limb recovery measured by standard clinical assessments and daily-life recorded kinematics.MethodsThe study was designed as an observational, single-group study to evaluate rehabilitation progress in a clinical and home environment, with a full-body sensor system in stroke patients. Kinematic data were recorded with a full-body motion capture suit during clinical assessment and self-directed activities of daily living. The measurements were performed at three time points for 3 h: (1) 2 weeks before discharge of the rehabilitation clinic, (2) right after discharge, and (3) 4 weeks after discharge. The kinematic analysis of reaching movements uses the position and orientation of each body segment to derive the joint angles. Newly developed metrics for classifying activity and quality of upper extremity movement were applied.ResultsThe data of four stroke patients (three mildly impaired, one sever impaired) were included in this study. The arm motor function assessment improved during the inpatient rehabilitation, but declined in the first 4 weeks after discharge. A change in the data (kinematics and new metrics) from the daily-life recording was seen in in all patients. Despite this worsening patients increased the number of reaches they performed during daily life in their home environment.ConclusionIt is feasible to measure arm kinematics using Inertial Measurement Unit sensors during daily life in stroke patients at the different stages of rehabilitation. Our results from the daily-life recordings complemented the data from the clinical assessments and illustrate the potential to identify stroke patient characteristics, based on kinematics, reaching counts, and work area.Clinical trial registrationhttps://clinicaltrials.gov, identifier NCT02118363.

Dataset Information

From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data.

Publications

From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data.

Similar Datasets

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