Observational balance tests (e.g., Berg Balance Scale) are used to evaluate fall-risk. However, they tend to be subjective, and their reliability and sensitivity can be limited. The use of in-lab equipment for objective balance evaluation has not been common in clinical practice, due to the requirement of an equipped lab space. While inertial measurement units (IMUs) enable objective out-of-lab balance assessment, their accuracy has not been validated. This study aims to investigate the accuracy of IMUs against in-lab equipment for characterizing standing balance. Ten non-disabled individuals participated in a two-minute standing test on a force-plate. Four approaches were used for estimating inter-segmental moments and center of pressure (COP) position in a four-segment model: (1) camera-based bottom-up approach; (2) camera-based top-down approach; (3) IMU-based (accelerometer) top-down approach; and (4) IMU-based (accelerometer and gyroscope) top-down approach. Approaches 2 to 4 resulted in high accuracy compared to the reference, Approach 1. The root-mean-square errors in estimating the segments' orientation, ground reaction forces, COP position, and joint moments were smaller than 0.3°, 0.2 N/kg, 1.5 mm, and 0.016N·m/kg, respectively. Since no significant differences were observed between the accuracy of Approaches 3 and 4, only accelerometer recordings are needed and could be recommended for monitoring standing balance.

Maintaining a stable upright posture is essential for performing activities of daily living, and impaired standing balance may impact an individual’s quality of life. Therefore, accurate and sensitive methods for assessing static balance are crucial for identifying balance impairments, understanding the underlying mechanisms of the balance deficiencies, and developing targeted interventions to improve standing balance and prevent falls. This review paper first explores the methods to quantify standing balance. Then, it reviews traditional posturography and recent advancements in using wearable inertial measurement units (IMUs) to assess static balance in two populations: older adults and those with incomplete spinal cord injury (iSCI). The inclusion of these two groups is supported by their large representation among individuals with balance impairments. Also, each group exhibits distinct aspects in balance assessment due to diverse underlying causes associated with aging and neurological impairment. Given the high vulnerability of both demographics to balance impairments and falls, the significance of targeted interventions to improve standing balance and mitigate fall risk becomes apparent. Overall, this review highlights the importance of static balance assessment and the potential of emerging methods and technologies to improve our understanding of postural control in different populations.

Individuals with spinal cord injury suffer from seated instability due to impaired trunk neuromuscular function. Monitoring seated stability toward the development of closed-loop controlled neuroprosthetic technologies could be beneficial for restoring trunk stability during sitting in affected individuals. However, there is a lack of (1) a biomechanical characterization to quantify the relationship between the trunk kinematics and sitting balance; and (2) a validated wearable biomedical device for assessing dynamic sitting posture and fall-risk in real-time. This study aims to: (a) determine the limit of dynamic seated stability as a function of the trunk center of mass (COM) position and velocity relative to the base of support; (b) experimentally validate the predicted limit of stability using traditional motion capture; (c) compare the predicted limit of stability with that predicted in the literature for standing and walking; and (d) validate a wearable device for assessing dynamic seated stability and risk of loss of balance. First, we used a six-segment model of the seated human body for simulation. To obtain the limit of stability, we applied forward dynamics and optimization to obtain the maximum feasible initial velocities of the trunk COM that would bring the trunk COM position to the front-end of the base-of-support for a set of initial COM positions. Second, experimental data were obtained from fifteen able-bodied individuals who maintained sitting balance while base-of-support perturbations were applied with three different amplitudes. A motion capture system and four inertial measurement units (IMUs) were used to estimate the trunk COM motion states (i.e., trunk COM position and velocity). The margin of stability was calculated as the shortest distance of the instantaneous COM motion states to those obtained as the limit of stability in the state-space plane. All experimentally obtained trunk COM motion states fell within the limit of stability. A high correlation and small root-mean-square difference were observed between the estimated trunk COM states obtained by the motion capture system and IMUs. IMU-based wearable technology, along with the predicted limit of dynamic seated stability, can estimate the margin of stability during perturbed sitting. Therefore, it has the potential to monitor the seated stability of wheelchair users affected by trunk instability.

Kinetics assessment of the human head-arms-trunk (HAT) complex via a multisegment model is a useful tool for objective clinical evaluation of several pathological conditions. Inaccuracies in body segment parameters (BSPs) are a major source of uncertainty in the estimation of the joint moments associated with the multisegment HAT. Given the large intersubject variability, there is currently no comprehensive database for the estimation of BSPs for the HAT. We propose a nonlinear, multistep, optimization-based, noninvasive method for estimating individual-specific BSPs and calculating joint moments in a multisegment HAT model. Eleven nondisabled individuals participated in a trunk-bending experiment and their body motion was recorded using cameras and a force plate. A seven-segment model of the HAT was reconstructed for each participant. An initial guess of the BSPs was obtained by individual-specific scaling of the BSPs calculated from the male visible human (MVH) images. The intersegmental moments were calculated using both bottom-up and top-down inverse dynamics approaches. Our proposed method adjusted the scaled BSPs and center of pressure (COP) offsets to estimate optimal individual-specific BSPs that minimize the difference between the moments obtained by top-down and bottom-up inverse dynamics approaches. Our results indicate that the proposed method reduced the error in the net joint moment estimation (defined as the difference between the net joint moment calculated via bottom-up and top-down approaches) by 79.3% (median among participants). Our proposed method enables an optimized estimation of individual-specific BSPs and, consequently, a less erroneous assessment of the three-dimensional (3D) kinetics of a multisegment HAT model.

Objective. Determining the roles of underlying mechanisms involved in stabilizing the human trunk during sitting is a fundamental challenge in human motor control. However, distinguishing their roles requires understanding their complex interrelations and describing them with physiologically meaningful neuromechanical parameters. The literature has shown that such mechanistic understanding contributes to diagnosing and improving impaired balance as well as developing assistive technologies for restoring trunk stability. This study aimed to provide a comprehensive characterization of the underlying neuromuscular stabilization mechanisms involved in human sitting. Approach. This study characterized passive and active stabilization mechanisms involved in seated stability by identifying a nonlinear neuromechanical physiologically-meaningful model in ten able-bodied individuals during perturbed sitting via an adaptive unscented Kalman filter to account for the nonlinear time-varying process and measurement noises. Main results. We observed that the passive mechanism provided instant resistance against gravitational disturbances, whereas the active mechanism provided delayed complementary phasic response against external disturbances by activating appropriate trunk muscles while showing non-isometric behavior. The model predicted the trunk sway behavior during perturbed sitting with high accuracy and correlation (average: 0.0007 (rad²) and 86.77%). This allows a better mechanistic understanding of the roles of passive and active stabilization mechanisms involved in sitting. Significance. Our characterization approach accounts for the inherently nonlinear behavior of the neuromuscular mechanisms and physiological uncertainties, while allowing for real-time tracking and correction of parameters’ variations due to external disturbances and muscle fatigue. The outcome of our research, for the first time, (a) allows a better mechanistic understanding of the roles of passive and active stabilization mechanisms involved in sitting; (b) enables objective evaluation and targeted rehabilitative interventions for impaired balance; facilitate bio-inspired designs of assistive technologies, and (c) opens new horizons in mathematical identification of neuromechanical mechanisms employed in the stable control of human body postures and motions.

Objective. Characterizing the task goals of the neural control system for achieving seated stability has been a fundamental challenge in human motor control research. This study aimed to experimentally identify the task goals of the neural control system for seated stability. Approach. Ten able-bodied young individuals participated in our experiments, which allowed us to measure their body motion and muscle activity during perturbed sitting. We used a nonlinear neuromechanical model of the seated human, along with a full-state feedback linearization approach and optimal control theory for identifying the neural control system and characterizing its task goals. Main results. We demonstrated that the neural feedback for trunk stability during seated posture uses angular position, velocity, acceleration, and jerk in a linearized space. The mean squared error between the predicted and measured motor commands was less than 0.6% among all trials and participants, with a median correlation coefficient r of more than 0.9. Our identified optimal neural control primarily used trunk angular acceleration and near-minimum muscle activation to achieve seated stability while keeping the deviations of the trunk angular position and acceleration sufficiently small. Significance. Our proposed approach to neural control system identification relied on a performance criterion (e.g. cost function) explaining what the functional goal is and subsequently, finds the control law that leads to the best performance. Therefore, instead of assuming what control schemes the neural control might utilize (e.g. proportional-integral-derivative control), optimal control allows the motor task and the neuromechanical model to dictate a control law that best describes the physiological process. This approach allows for a mechanistic understanding of the neuromuscular mechanisms involved in seated stability and for inferring the task goals used by the neural control system to achieve the targeted motor behavior. Such neural control characterization can contribute to the development of objective balance evaluation tools and of bio-inspired assistive neuromodulation technologies.