慣性センサおよび力センサを用いた立ち上がり時の関節角度推定手法に関する研究

Standing-up motion from a chair is directly connected with walking and which is frequently performed every day. It is difficult for some elders because of the weakened function of muscles or motor. The training of standing-up motion and assisting the elderly with the standing-up motion from a chair is important to the elderly Quality of Life (QOL). Analysis of the posture parameters during standing up motion is useful for the physical therapists and care-giver in rehabilitation training or movement assist. The motion capture system can measure the movement of body posture in any direction precisely. However, it is difficult to use in daily life because of high cost and specific requirements for the space. And the use of motion capture system will give unpleasant feeling to users because many reflective makers are attached in the body. The purpose of this study is to develop a new estimation system, which can be used in daily life for angle estimation of extension phase during standing-up motion. This paper discusses the estimation system consist of: 1) the estimation of body joint angles and COG during extension phase; 2) the improvement of the proposed system for angle estimation. In 1), an estimation model was proposed that was able to estimate knee and ankle joint angles by combining angle and acceleration of trunk, which came from the inertial sensor attached to the chest of users during the extension phase. The estimate result of joint angle shows higher accuracy than previous research. In 2), in order to expand the use of proposed system and improve the estimation accuracy of proposed system, we estimated the initial angle of knee and ankle by combining foot pressure which measured by a force sensor plate before standing-up motion. The estimation model of initial lower limb angle shows high accuracy. It can be used for angle estimation of extension phase even though the initial knee and ankle joint angle were unknown.九州工業大学博士学位論文 学位記番号：生工博甲第317号 学位授与年月日：平成30年3月23日1 Introduction|2 Previous Researches|3 Angle Estimation of Extension Phase|4 Estimation of Initial Lower Limb Angle|5 Conclusion and Future Work九州工業大学平成29年

A review of computer vision-based approaches for physical rehabilitation and assessment

The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered

Recognition and Estimation of Human Finger Pointing with an RGB Camera for Robot Directive

In communication between humans, gestures are often preferred or complementary to verbal expression since the former offers better spatial referral. Finger pointing gesture conveys vital information regarding some point of interest in the environment. In human-robot interaction, a user can easily direct a robot to a target location, for example, in search and rescue or factory assistance. State-of-the-art approaches for visual pointing estimation often rely on depth cameras, are limited to indoor environments and provide discrete predictions between limited targets. In this paper, we explore the learning of models for robots to understand pointing directives in various indoor and outdoor environments solely based on a single RGB camera. A novel framework is proposed which includes a designated model termed PointingNet. PointingNet recognizes the occurrence of pointing followed by approximating the position and direction of the index finger. The model relies on a novel segmentation model for masking any lifted arm. While state-of-the-art human pose estimation models provide poor pointing angle estimation accuracy of 28deg, PointingNet exhibits mean accuracy of less than 2deg. With the pointing information, the target is computed followed by planning and motion of the robot. The framework is evaluated on two robotic systems yielding accurate target reaching

The Development of an assistive chair for elderly with sit to stand problems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyStanding up from a seated position, known as sit-to-stand (STS) movement, is one of the most frequently performed activities of daily living (ADLs). However, the aging generation are often encountered with STS issues owning to their declined motor functions and sensory capacity for postural control. The motivated is rooted from the contemporary market available STS assistive devices that are lack of genuine interaction with elderly users. Prior to the software implementation, the robot chair platform with integrated sensing footmat is developed with STS biomechanical concerns for the elderly. The work has its main emphasis on recognising the personalised behavioural patterns from the elderly users’ STS movements, namely the STS intentions and personalised STS feature prediction. The former is known as intention recognition while the latter is defined as assistance prediction, both achieved by innovative machine learning techniques. The proposed intention recognition performs well in multiple subjects scenarios with different postures involved thanks to its competence of handling these uncertainties. To the provision of providing the assistance needed by the elderly user, a time series prediction model is presented, aiming to configure the personalised ground reaction force (GRF) curve over time which suggests successful movement. This enables the computation of deficits between the predicted oncoming GRF curve and the personalised one. A multiple steps ahead prediction into the future is also implemented so that the completion time of actuation in reality is taken into account

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction