As the world population continues to age, there is an increased need for efficient systems that can improve rehabilitation standards for the elderly and other motor-impaired subjects. A self-training system that takes into account subject-specific differences would contribute in a positive way to personalized home rehabilitation protocols. Since human balance depends largely on the control of the full body center of mass (CoM), fall risks can be assessed by observing the CoM displacement over the support polygon. The CoM position is often estimated using what are known as anthropometric tables. However, the parameters have been obtained from a standard population and will differ between subjects. Variability across subjects is increased for motor-impaired subjects as anthropometric values vary as a function of age, somatotype, and fitness level. This chapter develops a personalized measure of balance that considers subject-specific variations. This is a step towards the creation of versatile and portable self-balance assessment tools. We have expanded the range of application of the statically equivalent serial chain (SESC) mainly regarding the following points: (1) We have used the SESC method to estimate accurately CoM position with portable sensors like Kinect and Wii balance board. The system was validated by comparing its performance to high-end laboratory equipment: Vicon and force platform. We find that the CoM estimation obtained with SESC and the Kinect performed equivalently to the one based on anthropometric values and high-end equipment. (2) We developed an adaptive interface for self-identification of the SESC parameters by implementing a recursive identification method. This decreased the time necessary to establish the SESC by allowing the subject to perform a self-directed identification. (3) We assess a personalized balance based only on the identified CoM. This chapter also overviews home-based rehabilitation along with other state-of-the-art works.
|ホスト出版物のタイトル||Human Modeling for Bio-Inspired Robotics|
|ホスト出版物のサブタイトル||Mechanical Engineering in Assistive Technologies|
|出版物ステータス||Published - 2017 1 1|
ASJC Scopus subject areas
- Computer Science(all)