Leg-Joint Angle Estimation from a Single Inertial Sensor Attached to Various Lower-Body Links during Walking Motion +
Footnote †: This paper is an extended version of our previously published paper: [PERSON]; [PERSON]; [PERSON], [PERSON]: Deep-Learning-Based Leg-Joint Angle Estimation from a Single Pelvis Inertial Sensor. In Proceedings of the 2022 9 th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Seoul, Republic of Korea, 21-24 August 2022, pp. 1-7. [[https://doi.org/10.1109/BioRob52689.2022.9925420](https://doi.org/10.1109/BioRob52689.2022.9925420)]([https://doi.org/10.1109/BioRob52689.2022.9925420](https://doi.org/10.1109/BioRob52689.2022.9925420)). Footnote †: This paper is an extended version of our previously published paper: [PERSON]; [PERSON]; [PERSON], [PERSON]: Deep-Learning-Based Leg-Joint Angle Estimation from a Single Pelvis Inertial Sensor. In Proceedings of the 2022 9 th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Seoul, Republic of Korea, 21-24 August 2022, pp. [[https://doi.org/10.1109/BioRob52689.2022.925420](https://doi.org/10.1109/BioRob52689.2022.925420)]([https://doi.org/10.1109/BioRob52689.2022.925420](https://doi.org/10.1109/BioRob52689.2022.925420)). Footnote †: This paper is an extended version of our previously published paper: [PERSON]; [PERSON], [PERSON]; [PERSON], [PERSON]: Deep-Learning-Based Leg-Joint Angle Estimation from a Single Pelvis Inertial Sensor. In Proceedings of the 2022 9 th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Seoul, Republic of Korea, 21-24 August 2022, pp. [[https://doi.org/10.1109/BioRob52689.2022.925420](https://doi.org/10.1109/BioRob52689.2022.925420)]([https://doi.org/10.1109/BioRob52689.2022.925420](https://doi.org/10.1109/BioRob52689.2022.925420)). Footnote †: This paper is an extended version of our previously published paper: [PERSON]; [PERSON]; [PERSON], [PERSON]: Deep-Learning-Based Leg-Joint Angle Estimation from a Single Pelvis Inertial Sensor. In Proceedings of the 2022 9 th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Seoul, Republic of Korea, 21-24 August 2022, pp. [[https://doi.org/10.1109/BioRob52689.2022.925420](https://doi.org/10.1109/BioRob52689.2022.925420)]([https://doi.org/10.1109/BioRob52689.2022.925420](https://doi.org/10.1109/BioRob52689.2022.925420)). Footnote †: This paper is an extended version of our previously published paper: Alemidentification, and surgical outcome evaluation [3]. Its importance in the health area has been discussed in various studies. These studies cover areas such as knee and hip osteoarthritis [4], falling risk [5], spinal damage-level determination [6], [PERSON]'s disease diagnosis [7], and facilitating interactive rehabilitation and predictive diagnostics [8; 9]. Moreover, it can also be crucial in sports and robotics applications [10], virtual reality, and character animation applications [11]. Gaits are interpreted by first quantifying them using representative parameters that are easier to understand. These parameters mainly fall into two categories, either spatiotemporal (e.g., speed and stride/step length), kinematic (e.g., hip extension/flexion), or kinetic (e.g., moments and ground reaction forces) parameters [3]. In this paper, the focus is the estimation of the kinematic parameters for the lower half of the body. Leg joints are the key source of degrees of freedom for walking locomotion. Hence, accurately computing the joint angles is vital in understanding human gaits during walking. To do so, the type of sensor employed for data acquisition plays a key role in the accuracy of joint-angle computation. Hence, several data-collection techniques have been investigated over the past few years. Generally, they can be categorized as wearable and nonwearable sensor systems. Nonwearable sensor methods mostly employ a 3D motion capture system using special markers attached to the bodies of subjects.