Abstract
The occurrence of falls is an urgent challenge in our aging society. For wearable devices that actively prevent falls or mitigate their consequences, a critical prerequisite is knowledge on the user's current state of balance. To keep such wearable systems practical and to achieve high acceptance, only very limited sensor instrumentation is possible, often restricted to inertial measurement units at waist level. We propose to augment this limited sensor information by combining it with additional knowledge on human gait, in the form of an observer concept. The observer contains a combination of validated concepts to model human gait: A spring-loaded inverted pendulum model with articulated upper body, where foot placement and stance leg are controlled via the extrapolated center of mass (XCoM) and the virtual pivot point (VPP), respectively. State estimation is performed via an Additive Unscented Kalman Filter (Additive UKF). We investigated sensitivity of the proposed concept to model uncertainties, and we evaluated observer performance with real data from human subjects walking on a treadmill. Data was collected from an Inertial Measurement Unit (IMU) placed near the subject's center of mass (CoM), and observer estimates were compared to the ground truth as obtained via infrared motion capture. We found that the root mean squared deviation did not exceed 13cm on position, 22cm/s on velocity (0.56m/s-1.35m/s), 1.2degrees on orientation and 17degrees/s on angular velocity.
Highlights
Falls pose a major problem, especially in our aging society
We propose to estimate the state of balance by combining local sensor measurements with a simple model of mechanics and control of human gait, in the form of an observer concept
The minimum and maximum values of the outcome measures are given in Table 5, with an input error of 60% of the maximum set error
Summary
Falls pose a major problem, especially in our aging society. Balance dysfunction was found to be a considerable risk factor for falls (Rubenstein, 2006). Wearable robotic devices could play a role in preventing falls, or at least mitigating their consequences, by providing balance assistance in daily life activities. This would result in increased safety and independence of the elderly. Examples for such systems are the balance-assisting gyroscopic backpack (Li and Vallery, 2012), the hip orthosis (Giovacchini et al, 2014), and airbags to reduce fall injuries (Tamura et al, 2009)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
