Ambulatory Estimation of XCoM using Pressure Insoles and IMUs

Ambulatory gait assessment using minimal sensors has quite an impact for different applications requiring localised sensing. ForceShoes™ was developed as one such solution. It consists of two IMUs, and two 6DoF force and moment (F&M) sensors on each foot1. Additionally, an ultrasound system was added 2. The complete system, also referred to as Ambulatory Gait and Balance System (AGBS), is used to measure ambulatory kinematics and kinetics of the feet while walking. The AGBS has been validated against standard systems2,3. Using the measured F&M, and position estimations from IMUs, the low and high-frequency information of Center of Mass (CoM) is estimated. This was used to estimate the Extrapolated Center of Mass (XCoM)4. XCoM along with base of support provides information about stability during walking4. The unique advantage of the AGBS is its portability and ambulatory measurement when compared to standard systems. The F&M sensors in the AGBS however, are quite bulky, making it heavier and taller than normal shoes. As an alternative, using 1D pressure sensors was studied. Pressure sensors are thin and easy to slip as insoles in shoes. Therefore, they show potential in making the ambulatory system less bulky

Adaptive Lower Limb Pattern Recognition for Multi-Day Control

Pattern recognition in EMG-based control systems suffer from increase in error rate over time, which could lead to unwanted behavior. This so-called concept drift in myoelectric control systems could be caused by fatigue, sensor replacement and varying skin conditions. To circumvent concept drift, adaptation strategies could be used to retrain a pattern recognition system, which could lead to comparable error rates over multiple days. In this study, we investigated the error rate development over one week and compared three adaptation strategies to reduce the error rate increase. The three adaptation strategies were based on entropy, on backward prediction and a combination of backward prediction and entropy. Ten able-bodied subjects were measured on four measurement days while performing gait-related activities. During the measurement electromyography and kinematics were recorded. The three adaptation strategies were implemented and compared against the baseline error rate and against adaptation using the ground truth labels. It can be concluded that without adaptation the baseline error rate increases significantly from day 1 to 2, but plateaus on day 2, 3 and 7. Of the three tested adaptation strategies, entropy based adaptation showed the smallest increase in error rate over time. It can be concluded that entropy based adaptation is simple to implement and can be considered a feasible adaptation strategy for lower limb pattern recognition

Portable Gait Lab: Tracking Relative Distances of Feet and CoM Using Three IMUs

Ambulatory estimation of gait and balance parameters requires knowledge of relative feet and centre of mass (CoM) positions. Inertial measurement units (IMU) placed on each foot, and on the pelvis are useful in tracking these segments over time, but cannot track the relative distances between these segments. Further, drift due to strapdown inertial navigation results in erroneous relative estimates of feet and CoM positions after a few steps. In this study,we track the relative distances using the assumptions of the Centroidal Moment Pivot (CMP) theory. An Extended Kalman filter approach was used to fuse information from different sources: strapdown inertial navigation, commonly used constraints such as zero velocity updates, and relative segment distances from the CMP assumption; to eventually track relative feet and CoM positions. These estimates were expressed in a reference frame defined by the heading of each step. The validity of this approach was tested on variable gait. The step lengths and step widths were estimated with an average absolute error of 4.6±1.5cmand3.8±1.5cm respectively when compared against the reference VICON. Additionally, we validated the relative distances of the feet and the CoM, and further, show that the approach proves useful in identifying asymmetric gait patterns.We conclude that a three IMU approach is feasible as a portable gait lab for ambulatory measurement of foot and CoM positions in daily life

Gait and Dynamic Balance Sensing Using Wearable Foot Sensors

Remote monitoring of gait performance offers possibilities for objective evaluation, and tackling impairment in motor ability, gait, and balance in populations such as elderly, stroke, multiple sclerosis, Parkinson’s, etc. This requires a wearable and unobtrusive system capable of estimating ambulatory gait and balance measures, such as Extrapolated Centre of Mass (XCoM) and dynamic Margin of Stability (MoS). These estimations require knowledge of 3D forces and moments (F&M), and accurate foot positions. Though an existing Ambulatory Gait and Balance System (AGBS) consisting of 3D F&M sensors, and inertial measurement units (IMUs) can be used for the purpose, it is bulky and conspicuous. Resistive pressure sensors were investigated as an alternative to the onboard 3D F&M sensors. Subject specific regression models were built to estimate 3D F&M from 1D plantar pressures. The model was applicable for different walking speeds. Different pressure sensor configurations were studied to optimise system complexity and accuracy. Using resistive sensors only under the toe and heel, we were able to estimate the XCoM with a mean absolute RMS error of 2.20.3 cm in the walking direction while walking at a preferred speed, when compared to the AGBS. For the same case, the XCoM was classified as ahead or behind the Base of Support correctly at 97.7 1.7%. In conclusion, the study shows that pressure sensors, minimally under the heel and toe, offer a lightweight and inconspicuous alternative for F&M sensing, towards estimating ambulatory gait and dynamic balance

A method, a system and a computer program product for estimating positions of a subject's feet and centre of mass relative to each other

The invention relates to a method for estimating positions of a subject’s feet and centre of mass relative to each other during gait. The method comprises a step of collecting measurement data from a first inertial measurement unit located at a first foot or first shank of the subject, from a second inertial measurement unit located at a second foot or second shank of the subject, and from a third inertial measurement unit located at a pelvis of the subject. Further, the method includes a step of evaluating relative positions of the first and second foot and the center of mass of the subject over time, using the measurement data of the first, second and third inertial measurement unit. The method may include a step of applying the assumption that a moment around a center of mass of the subject vanishes for determining an estimation of relative foot positions

Comparison of kinematics of imposed head movements and head movements in free space

Main research question: The correct positioning of the head and neck is essential during electrical wheelchair use. Therefore, proper head support is important for persons with impaired head stabilization and/or positioning. Existing systems often provide only static, fixed head support, while during daily life support of different head support positions and position changes are needed. Therefore, an adaptive, dynamic head support is being developed by the authors. As design input, the kinematics of the head in free space and during imposed and restricted head movements were studied to define the degrees of freedom and movements that the system should be able to support. Research methods: An observational study (one measurement session) was set up with non-impaired individuals. Besides head movement in free space, imposed passive and restricted active head movements were measured, using a measurement device that allowed the head to rotate around fixed rotation points. Participants performed flexion/extension, lateral rotation, lateral flexion, and a combination of lateral rotation and lateral flexion. Results: A total of nineteen participants were included in the study. For this abstract, the kinematic data of thirteen participants were studied. In flexion-extension, the rotation was isolated with minimal rotation over the other two axes (< 5 degrees). Lateral rotation showed variable flexion/extension and contralateral lateral flexion at the movement extremes. Lateral flexion showed variable rotations over the other axes. For the imposed head movements, especially in flexion-extension but also in lateral flexion there was a mismatch at the movement extremes in which the head lost contact with the measurement device. In lateral rotation, the head and measurement device followed approximately the same path. However, in the combined trial, together with lateral rotation, the head showed contralateral lateral flexion (10-15 degrees). Conclusions: The research results provide input for the movement paths to be implemented in the new head support. In order to approximate the head kinematics in free space and to account for user variability, flexion extension, lateral rotation and lateral flexion movements should not be controlled over fixed rotation points. Rotations should be combined and the translations of the biomechanical rotation axes should also be incorporated