Human Motion Capture and Identification for Assistive Systems Design in Rehabilitation. Pubudu N. Pathirana

      

      Table of Contents

      1  Cover

      2  Title Page

      3  Copyright

      4  1 Introduction 1.1 Human Body – Kinematic Perspective 1.2 Musculoskeletal Injuries and Neurological Movement Disorders 1.3 Sensors in Telerehabilitation 1.4 Model‐based State Estimation and Sensor Fusion 1.5 Human Motion Encoding in Telerehabilitation 1.6 Patients' Performance Evaluation

      5  2 Kinematic Performance Evaluation with Non‐wearable Sensors 2.1 Introduction 2.2 Fusion 2.3 Encoder 2.4 ADL Kinematic Performance Evaluation 2.5 Summary

      6  3 Biokinematic Measurement with Wearable Sensors 3.1 Introduction 3.2 Introduction to Quaternions 3.3 Wahba's Problem 3.4 Quaternion Propagation 3.5 MARG (Magnetic Angular Rates and Gravity) Sensor Arrays‐based Algorithm 3.6 Model‐based Estimation of Attitude with IMU Data 3.7 Robust Optimisation‐based Approach for Orientation Estimation 3.8 Implementation of the Orientation Estimation 3.9 Computer Simulations 3.10 Experimental Setup 3.11 Results and Discussion 3.12 Conclusion

      7  4 Capturing Finger Movements 4.1 Introduction 4.2 System Overview 4.3 Accuracy Improvement of Total Active Movement and Proximal Interphalangeal Joint Angles 4.4 Simulation 4.5 Trial Procedure 4.6 Results 4.7 Discussions 4.8 Approaching Finger Movement with a New Perspective 4.9 Reachable Space 4.10 Boundary of the Reachable Space 4.11 Area of the Reachable Space 4.12 Experiments 4.13 Results and Discussion 4.14 Conclusion and Future Work

      8  5 Non‐contact Measurement of Respiratory Function via Doppler Radar 5.1 Introduction 5.2 Fundamental Operation of Microwave Doppler Radar 5.3 Signal Processing Approach 5.4 Common Data Acquisitions Setup 5.5 Capturing the Dynamics of Respiration 5.6 Capturing Special Breathing Patterns 5.7 Removal of Motion Artefacts from Doppler Radar‐based Respiratory Measurements 5.8 Separation of Doppler Radar‐based Respiratory Signatures

      9  6 Appendix 6.1 Static Estimators 6.2 Model‐based Estimators 6.3 Particle Filter

      10  Bibliography

      11  Index

      12  End User License Agreement

      List of Tables

      1 Chapter 1Table 1.1 Examples of musculoskeletal injuries in joints of the upper extremi...Table 1.2 Examples of musculoskeletal injuries in joints of the lower extremi...Table 1.3 Comparison of basic technical specifications between two versions o...

      2 Chapter 2Table 2.1 Example human movements.Table 2.2 Comparison (in the presence of missing information) of accuracy and...Table 2.3 Sensor motion types.Table 2.4 Comparison between SG and LS.Table 2.5 Shape and dynamics for four motions. In each left figure, the colou...Table 2.6 Parameters used by filters to process trajectories with the tempora...Table 2.7 The comparison of curvatures and torsions for two trajectories inde...Table 2.8 Trajectories and decomposition of complex motions. In each top figu...Table 2.9 The definition of three kinematic severity levels of involuntary mo...Table 2.10 Parameters used to simulate two groups of trajectories. These two ...Table 2.11 Demographic data of subjects.Table 2.12 Cohen's kappa (

) between various automated approaches and the hum...

      3 Chapter