Observing the State of Balance with a Single Upper-Body Sensor

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 were 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 13 cm on position, 22 cm/s on velocity (0.56-1.35 m/s), 1.2°on orientation, and 17°/s on angular velocity.Keywords: human gait and balance, wearable sensors, state estimation, virtual pivot point, extrapolated center of mass, capture point, additive unscented kalman filter, fall detection

Moving on: measuring movement remotely after stroke

“…rRat Y ) as seen in Table 9.3 was 19.3 ± 3.1 % of the range of the reference values in the worst case. The RMS error in estimating the vertical CoM position using alternative approaches in literature was on average 3.5 ± 1.3 mm (Floor-Westerdijk et al, 2012) and did not exceed 20 mm in another study (Paiman et al, 2016). In our study, it was on average 7 ± 2.4 mm across all walking tasks.…”

“…Additionally, the other studies only compared the differences in a detrended position for an average stride (Floor-Westerdijk et al, 2012), or considered treadmill gait (Paiman et al, 2016). We state the average error in estimating the instantaneous CoM height over the complete gait including initiation, turning, and stopping.…”

“…It may be of interest to include the constraints explored in Sy and colleagues (Sy et al, 2020) where they also estimate joint kinematics. Combining other biomechanical model of gait (Paiman et al, 2016;Sy et al, 2020) with the current study can result in a system that provides complete linear and joint kinematics of the lower limb using a minimal IMU setup.…”

Controller synthesis and clinical exploration of wearable gyroscopic actuators to support human balance

“…Although relatively sophisticated estimation of the state of balance can be performed using minimal instrumentation 40 , the current study used only angular orientation and velocity of the trunk for feedback, estimated 41 from an inertial measurement unit (IMU) located at shoulder height in the GyBAR (Fig. 1a).…”

Virtual Point Control for Step-Down Perturbations and Downhill Slopes in Bipedal Running

“…The majority of bipedal robots use IMUs together with filterbased state estimation techniques to estimate its own trunk state (Hubicki et al, 2016). Paiman et al (2016) proposes a VP based observer for a wearable robotic device, which uses an IMU with an unscented Kalman filter (UKF) to estimate trunk orientation, gyros to estimate trunk angular velocity, and an accelerometer to estimate linear CoM acceleration. Recent studies improve the state estimation accuracy by sensor fusion, and include exteroceptive sensors such as cameras and LIDAR (Wisth et al, 2019;Camurri et al, 2020).…”