ArticleSensors (Basel, Switzerland)2020
Estimating Lower Limb Kinematics Using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review.Frontiers in bioengineering and biotechnology · 2024Pooled it
- Wearables for Telemonitoring in ATTR-Amyloidosis: Current Perspectives.Sensors (Basel, Switzerland) · 2026Review
- Modified Extended Kalman Filter and Long Short-Term Memory-Based Framework for Reliable Stride-Length Estimation Using Inertial Sensors.Sensors (Basel, Switzerland) · 2026Article
- Multimodal inverse kinematics significantly improves IMU-based biomechanical analyses.Scientific reports · 2025Article
- Optimization of MIMU Mounting Position on Shank in Posture Estimation Considering Muscle Protuberance.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Estimation of Lower Limb Posture and Joint Angles Using Wearable IMUs: Reproduced Hemiparetic and Normal Gait.Japanese journal of comprehensive rehabilitation science · 2025Article
- Evaluating the Performance of Joint Angle Estimation Algorithms on an Exoskeleton Mock-Up via a Modular Testing Approach.Sensors (Basel, Switzerland) · 2024Article
- Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors.Frontiers in bioengineering and biotechnology · 2023Article
- Lower Body Joint Angle Prediction Using Machine Learning and Applied Biomechanical Inverse Dynamics.Sensors (Basel, Switzerland) · 2022Article
- An engineer's perspective on the mechanisms and applications of wearable inertial sensors.Journal of spine surgery (Hong Kong) · 2022Article
- Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
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Abstract
Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were 7.6±2.6∘ and 6.6±2.7∘, respectively, while the correlation coefficients were 0.95±0.03 and 0.87±0.16, respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels (σ=0.2 m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.
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