Towards Cross-Modal Comparison of Human Motion Data

Helten, Thomas; Müller, Meinard; Tautges, Jochen; Weber, Andreas; Seidel, Hans‐Peter

doi:10.1007/978-3-642-23123-0_7

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, IMUs still have their own limitations such as susceptibility to noise and limited dynamic range. The accuracy of inertial sensor-based estimation can be improved by using zero velocity updates as in [ 7 ], or taking advantage of the relative position and attitude of multiple sensors [ 8 , 9 ]. In [ 7 ], a robot arm control with an automatic calibration function based on inertial sensors is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…The authors state that the drift of the sensors is clearly removed by applying zero velocity updates. In [ 8 ], Helten et al introduce another way which takes into account the relative position and attitude of multiple sensors to improve the accuracy of human motion estimation. Using the same method, Tao et al [ 9 ] estimate limb movement.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensor Saturation Compensated Smoothing Algorithm for Inertial Sensor Based Motion Tracking

Dang

Suh

2014

Sensors

View full text Add to dashboard Cite

In this paper, a smoothing algorithm for compensating inertial sensor saturation is proposed. The sensor saturation happens when a sensor measures a value that is larger than its dynamic range. This can lead to a considerable accumulated error. To compensate the lost information in saturated sensor data, we propose a smoothing algorithm in which the saturation compensation is formulated as an optimization problem. Based on a standard smoothing algorithm with zero velocity intervals, two saturation estimation methods were proposed. Simulation and experiments prove that the proposed methods are effective in compensating the sensor saturation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%