Complete Inertial Pose Dataset: from raw measurements to pose with low-cost and high-end MARG sensors
This provides a resource for researchers and developers to benchmark and develop algorithms for inertial pose estimation, with applications in human movement analysis and ergonomic assessment, though it is incremental as it focuses on dataset creation rather than novel methods.
The authors tackled the lack of comprehensive datasets for inertial pose estimation by presenting two datasets with low-cost and high-end MARG sensor data, totaling 3.5 million samples synchronized with ground-truth motion capture at 60 Hz, and validated the data quality with a simple end-to-end method.
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, performing 6 types of sequences (ranging from calibration, to daily-activities and random movements). It presents a high degree of variability and complex dynamics with almost complete range-of-motion, while containing common sources of error found on real conditions. This amounts to 3.5M samples, synchronized with a ground-truth inertial motion capture system at 60hz. A simple end-to-end inertial pose method was briefly described and used to validate the quality of the data in both acquisitions. This database may contribute to assess, benchmark and develop novel algorithms for each of the pipelines' processing steps, with applications in classic or data-driven inertial pose estimation algorithms, human movement understanding and forecasting and ergonomic assessment in industrial or rehabilitation settings. All the data is freely available on an online database and accompanied with code to process and analyze the complete data pipeline.