Complete Inertial Pose Dataset: from raw measurements to pose with low-cost and high-end MARG sensors
Manuel Palermo, Sara Cerqueira, Jo\~ao Andr\'e, Ant\'onio Pereira,, Cristina P. Santos

TL;DR
This paper introduces two comprehensive inertial pose datasets using low-cost and high-end MARG sensors, enabling development and benchmarking of pose estimation algorithms with real-world variability and complex movements.
Contribution
It provides the first complete inertial pose datasets from raw measurements to pose, including calibration, fusion, and kinematics, with extensive variability and real-world conditions.
Findings
Datasets contain 3.5 million samples synchronized with ground truth.
Data includes diverse movements from daily activities to complex motions.
The datasets support benchmarking and development of inertial pose estimation algorithms.
Abstract
The use of wearable technology for posture monitoring has been expanding due to its low-intrusiveness and compliance with daily use requirements. However, there are still open challenges limiting its widespread use, especially when dealing with low-cost systems. Most solutions falls either into fully functioning commercial products with high costs, or ad-hoc solutions with lower performance. Moreover, there are few datasets available, from which complete and general solutions can be derived. This work presents 2 datasets, containing low-cost and high-end Magnetic, Angular Rate, and Gravity (MARG) sensor data respectively. It provides data for the analysis of the complete inertial pose pipeline, from raw measurements, to sensor-to-segment calibration, multi-sensor fusion, skeleton kinematics, to the complete human pose. Multiple trials were collected with 21 and 10 subjects respectively,…
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Taxonomy
TopicsBalance, Gait, and Falls Prevention · Non-Invasive Vital Sign Monitoring · Hand Gesture Recognition Systems
