TY - GEN

T1 - Sparse magnetometer-free real-time inertial hand motion tracking

AU - Grapentin, Aaron

AU - Lehmann, Dustin

AU - Zhupa, Ardjola

AU - Seel, Thomas

PY - 2020

Y1 - 2020

N2 - Hand motion tracking is a key technology in several applications including ergonomic workplace assessment, human-machine interaction and neurological rehabilitation. Recent technological solutions are based on inertial measurement units (IMUs). They are less obtrusive than exoskeleton-based solutions and overcome the line-of-sight restrictions of optical systems. The number of sensors is crucial for usability, unobtrusiveness, and hardware cost. In this paper, we present a real-time capable, sparse motion tracking solution for hand motion tracking that requires only five IMUs, one on each of the distal finger segments and one on the back of the hand, in contrast to recently proposed full-setup solution with 16 IMUs. The method only uses gyroscope and accelerometer readings and avoids magnetometer readings, which enables unrestricted use in indoor environments, near ferromagnetic materials and electronic devices. We use a moving horizon estimation (MHE) approach that exploits kinematic constraints to track motions and performs long-term stable heading estimation. The proposed method is validated experimentally using a recently developed sensor system. It is found that the proposed method yields qualitatively good agreement of the estimated and the actual hand motion and that the estimates are long-term stable. The root-mean-square deviation between the fingertip position estimates of the sparse and the full setup are found to be in the range of 1 cm. The method is hence highly suitable for unobtrusive and non-restrictive motion tracking in a range of applications.

AB - Hand motion tracking is a key technology in several applications including ergonomic workplace assessment, human-machine interaction and neurological rehabilitation. Recent technological solutions are based on inertial measurement units (IMUs). They are less obtrusive than exoskeleton-based solutions and overcome the line-of-sight restrictions of optical systems. The number of sensors is crucial for usability, unobtrusiveness, and hardware cost. In this paper, we present a real-time capable, sparse motion tracking solution for hand motion tracking that requires only five IMUs, one on each of the distal finger segments and one on the back of the hand, in contrast to recently proposed full-setup solution with 16 IMUs. The method only uses gyroscope and accelerometer readings and avoids magnetometer readings, which enables unrestricted use in indoor environments, near ferromagnetic materials and electronic devices. We use a moving horizon estimation (MHE) approach that exploits kinematic constraints to track motions and performs long-term stable heading estimation. The proposed method is validated experimentally using a recently developed sensor system. It is found that the proposed method yields qualitatively good agreement of the estimated and the actual hand motion and that the estimates are long-term stable. The root-mean-square deviation between the fingertip position estimates of the sparse and the full setup are found to be in the range of 1 cm. The method is hence highly suitable for unobtrusive and non-restrictive motion tracking in a range of applications.

UR - http://www.scopus.com/inward/record.url?scp=85096144015&partnerID=8YFLogxK

U2 - 10.1109/mfi49285.2020.9235262

DO - 10.1109/mfi49285.2020.9235262

M3 - Conference contribution

T3 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems

SP - 94

EP - 100

BT - 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

ER -