  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.
