Magnetometer-free Realtime Inertial Motion Tracking by Exploitation of Kinematic Constraints in 2-DoF Joints

Laidig, Daniel; Lehmann, Dustin; Bégin, Marc-André; Seel, Thomas

doi:10.1109/embc.2019.8857535

Cited by 33 publications

(25 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another approach is the use of kinematic constraints to determine joint angles from IMU data. Based on different joints, this approach reveals very good results (Müller et al, 2017;Laidig et al, 2019;Nowka et al, 2019). Nevertheless, it was not analyzed recently for gait analysis.…”

Section: Kinematics and Kineticsmentioning

confidence: 99%

“…Therefore, the use of machine learning algorithms or the exploitation of kinematic constraints seems to be most promising. The most recent advancements of the kinematic-constraint-based approaches (Laidig et al, 2017(Laidig et al, , 2019Müller et al, 2017;Nowka et al, 2019) have not been evaluated on gait analysis. Seel et al (2014) evaluated the knee and ankle joint sagittal plane angle achieving deviations to the gold standard of less then 1 • .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network

Mundt

Koeppe

David

et al. 2020

Front. Bioeng. Biotechnol.

122

104

View full text Add to dashboard Cite

Section: Kinematics and Kineticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network

Mundt

Koeppe

David

et al. 2020

Front. Bioeng. Biotechnol.

122

104

View full text Add to dashboard Cite

“…For joints with two DoF, Laidig et al [14] recently proposed an inertial motion tracking method with an orientation-based constraint that overcomes the need for magnetometer measurements. Long-time stable and drift-free relative orientations and joint angles were tracked.…”

mentioning

confidence: 99%

Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

et al. 2020

View full text Add to dashboard Cite

The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is known to drift over time, resulting in drifting joint kinematics. This study proposes a novel joint kinematic estimation method that tightly incorporates the connection between adjacent segments within a sensor fusion algorithm, to obtain drift-free joint kinematics. Drift in the joint kinematics is eliminated solely by utilizing common information in the accelerometer and gyroscope measurements of sensors placed on connecting segments. Both an optimization-based smoothing and a filtering approach were implemented. Validity was assessed on a robotic manipulator under varying measurement durations and movement excitations. Standard deviations of the estimated relative sensor orientations were below 0.89 • in an optimization-based smoothing implementation for all robot trials. The filtering implementation yielded similar results after convergence. The method is proven to be applicable in biomechanics, with a prolonged gait trial of 7 minutes on 11 healthy subjects. Three-dimensional knee joint angles were estimated, with mean RMS errors of 2.14 • , 1.85 • , 3.66 • in an optimization-based smoothing implementation and mean RMS errors of 3.08 • , 2.42 • , 4.47 • in a filtering implementation, with respect to a golden standard optical motion capture reference system. Tommy Verbeerst received the M.Sc. degree in electrical engineering from KHBO, Ostend, Belgium, in 2008. Since 2013, he has been working with KU Leuven and UC Vives. He is affiliated with the Department of Electrical Engineering (ESAT), KU Leuven Campus Bruges, Belgium. His current research interest includes the fields of engineering education, robotics, and machine-vision. Mark Versteyhe received the M.Sc. degree in mechanical engineering and the Ph.D. degree in applied sciences, from KU Leuven in 1995 and 2000, respetively.He has worked 16 years in industry in various functions linked to research and innovation. Since October 2016, he has been a Professor with KU Leuven's Faculty of Engineering Technology, Technology Campus Brugge, where he co-ordinates the research effort on connected mechatronics. His research focus lies in studying and applying the holistic paradigm of mechatronic system design. His special interest goes to "Dependability" which encompasses reliabilityavailability-robustness and security of a system and "Distributed Systems" which are treated as a complex ecosystem of machines and humans that are connected within the Industry 4.0 paradigm shift.Kurt Claeys received the M.Sc. degree in musculoskeletal rehabilitation sciences and physiotherapy from the University of Ghent, Belgium, in 1993, and the Ph.D. degree in orthopedic manual therapist from the IRSK-WINGS institute Ieper, Belgium, in 2005, and the Ph.D. degree from KU Leuven, Belgium,...

show abstract

“…During the considered flutter kick motion of the legs, the hip and knee move approximately like hinge joints -flexion/extension is the dominant motion, while adduction/abduction and internal rotation occur only to a limited degree. We exploit these approximate kinematic constraints by using a recently developed relative-heading tracking algorithm [33]. That algorithm takes the orientation quaternions of both segments adjacent to the joint and corrects the heading of the distal segment's orientation such that the joint constraint is fulfilled in a weighted least-squares sense.…”

Section: Joint and Roll Angle Estimationmentioning

confidence: 99%

Supporting front crawl swimming in paraplegics using electrical stimulation: a feasibility study

Wiesener

Spieker

Axelgaard

et al. 2020

J NeuroEngineering Rehabil

Self Cite

View full text Add to dashboard Cite

Background: Participation in physical and therapeutic activities is usually severely restricted after a spinal cord injury (SCI). Reasons for this are the associated loss of voluntary motor function, inefficient temperature regulation of the affected extremities, and early muscle fatigue. Hydrotherapy or swim training offer an inherent weight relief, reduce spasticity and improve coordination, muscle strength and fitness. Methods: We present a new hybrid exercise modality that combines functional electrical stimulation (FES) of the knee extensors and transcutaneous spinal cord stimulation (tSCS) with paraplegic front crawl swimming. tSCS is used to stimulate the afferent fibers of the L2-S2 posterior roots for spasticity reduction. By activating the tSCS, the trunk musculature is recruited at a motor level. This shall improve trunk stability and straighten the upper body. Within this feasibility study, two complete SCI subjects (both ASIA scale A, lesion level Th5/6), who have been proficient front crawl swimmers, conducted a 10-week swim training with stimulation support. In an additional assessment swim session nine months after the training, the knee extension, hip extension, and trunk roll angles where measured using waterproof inertial measurement units (IMUs) and compared for different swimming conditions (no stimulation, tSCS, FES, FES plus tSCS). Results: For both subjects, a training effect over the 10-week swim training was observed in terms of measured lap times (16 m pool) for all swimming conditions. Swimming supported by FES reduced lap times by 15.4% and 8.7% on average for Subject A and Subject B, respectively. Adding tSCS support yielded even greater mean decreases of 19.3% and 20.9% for Subjects A and B, respectively. Additionally, both subjects individually reported that swimming with tSCS for 30-45 minutes eliminated spasticity in the lower extremities for up to 4 hours beyond the duration of the session. Comparing the median as well as the interquartile range of all different settings, the IMU-based motion analysis revealed that FES as well as FES+tSCS improve knee extension in both subjects, while hip extension was only increased in one subject. Trunk roll angles were similar for all swimming conditions. tSCS had no influence on the knee and hip joint angles. Both subjects reported that stimulation-assisted swimming is comfortable, enjoyable, and they would like to use such a device for recreational training and rehabilitation in the future.

show abstract

Magnetometer-free Realtime Inertial Motion Tracking by Exploitation of Kinematic Constraints in 2-DoF Joints

Cited by 33 publications

References 20 publications

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network

Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

Supporting front crawl swimming in paraplegics using electrical stimulation: a feasibility study

Contact Info

Product

Resources

About