Falls, Fear of falling (FoF), and low balance confidence pose significant threats to health and independence among older people. This thesis aims to study humans’ postural and balance mechanisms, from analyzing the effect of multisensory inputs on balance performance and balance-related concerns to modeling the postural sway through physical dynamics and data-driven approaches. Mimic human biomechanics and motor control systems on robotic balance applications; and finally, investigate and propose novel technological methods to diagnose and measure the postural sway clinical concerns.

Within this context, this thesis will establish the following main contributions: First, with signal processing and machine learning algorithms, new analysis and methods are developed to detect the balance impairment applied to the posturography data of the quiet stance. These results underscore the importance of considering the sensori motor function and fall-related concerns when examining human postural sway and balance performance and suggest potential affordable avenues for early detection of balance impairment.

Secondly, applying estimation theory, a novel approach has been developed to detect internal disturbances in the neuromuscular control of balance mechanisms. This innovative method holds the potential for identifying FoF in individuals, offering valuable insights into balance-related concerns.

The third main contribution is in the field of human postural sway modeling by perceiving both data-driven and model-based approaches. A new structure for the postural sway that offers a more comprehensive understanding of the underlying neural mechanisms involved in the balance is introduced. Moreover, it has also examined existing model-based approaches for human postural sway modeling, comparing their ability to mimic human data in the upright stance. By carefully evaluating and benchmarking these approaches, each method’s strengths and limitations are examined, and areas for future development are highlighted.

Finally, the proposed data-driven model’s efficacy through simulation and experimental results using a single-link inverted pendulum is demonstrated. Specifically, the data-driven control model achieved a proper balance, proving the approach’s effectiveness. These findings have important implications for developing new interventions to improve balance and reduce the risk of falls in real-world settings, particularly in aging and other high-risk populations.

Time delays in communication networks are one of the main concerns in deploying robots with computation boards on the edge. This article proposes a multi-stage Nonlinear Model Predictive Control (NMPC) that is capable of handling varying network-induced time delays for establishing a control framework being able to guarantee collision-free Micro Aerial Vehicles (MAVs) navigation. This study introduces a novel approach that considers different sampling times by a tree of discretization scenarios contrary to the existing typical multi-stage NMPC where system uncertainties are modeled by a tree of scenarios. Additionally, the proposed method considers adaptive weights for the multi-stage NMPC scenarios based on the probability of time delays in the communication link. As a result of the multi-stage NMPC, the obtained optimal control action is valid for multiple sampling times. Finally, the overall effectiveness of the proposed novel control framework is demonstrated in various tests and different simulation environments.

The human postural control system can maintain our balance in an upright stance. A simplified control model that can mimic the mechanisms of this complex system and adapt to the changes due to aging and injuries is a significant problem that can be used in clinical applications. While the Intermittent Proportional Derivative (IPD) is commonly used as a postural sway model in the upright stance, it does not consider the predictability and adaptability behavior of the human postural control system and the physical limitations of the human musculoskeletal system. In this article, we studied the methods based on optimization algorithms that can mimic the performance of the postural sway controller in the upright stance. First, we compared three optimal methods (Model Predictive Control (MPC), COP-Based Controller (COP-BC) and Momentum-Based Controller (MBC)) in simulation by considering a feedback structure of the dynamic of the skeletal body as a double link inverted pendulum while taking into account sensory noise and neurological time delay. Second, we evaluated the validity of these methods by the postural sway data of ten subjects in quiet stance trials. The results revealed that the optimal methods could mimic the postural sway with higher accuracy and less energy consumption in the joints compared to the IPD method. Among optimal approaches, COP-BC and MPC show promising results to mimic the human postural sway. The choice of controller weights and parameters is a trade-off between the consumption of energy in the joints and the prediction accuracy. Therefore, the capability and (dis)advantage of each method reviewed in this article can navigate the usage of each controller in different applications of postural sway, from clinical assessments to robotic applications.

Background: Aging is associated with a decline in postural control and an increased risk of falls. The Center of Pressure (CoP) trajectory analysis is a commonly used method to assess balance. In this study, we proposed a new method to identify balance impairments in older adults by analyzing their CoP trajectory frequency components, sensory inputs, reaction time, motor functions, and Fall-related Concerns (FrC).

Methods: The study includes 45 older adults aged 75.2(±4.5)75.2(±4.5) years who were assessed for sensory and motor functions. FrC and postural control in a quiet stance with open and closed eyes on stable and unstable surfaces. A Discrete Wavelet Transform (DWT) was used to detect features in frequency scales, followed by the K-means algorithm to detect different clusters. The multinomial logistic model was used to identify and predict the association of each group with the sensorimotor tests and FrC.

Results: The study results showed that by DWT, three distinct groups of subjects could be revealed. Group 2 exhibited the broadest use of frequency scales, less decline in sensorimotor functions, and lowest FrC. The study also found that a decline in sensorimotor functions and fall-related concern may cause individuals to rely on either very low-frequency scales (group 1) or higher-frequency scales (group 3) and that those who use lower-frequency scales (group 1) can manage their balance more successfully than group 3.

Conclusions: Our study provides a new, cost-effective method for detecting balance impairments in older adults. This method can be used to identify people at risk and develop interventions and rehabilitation strategies to prevent falls in this population.

To deploy Micro Aerial Vehicles (MAVs) in real-world applications, there is a need for online methods to cope with uncertainties in localization and external disturbances. In this article, we propose a set of novel real-time embedded Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimation (NMHE) modules for MAV based external disturbance rejection. The NMPC and NMHE are based on the dynamic model of the MAV, thus, avoiding the need for system identification and creating specific aerodynamic models, a benefit that results in a generic solution capable of being independent of the type of the MAVs. As it will be presented, the NMHE estimates the external forces, while the NMPC generates thrust and attitude commands for the low-level controller to compensate the various disturbances that could occur, such as wind gusts, tethered payload, and varying center of gravity. The proposed method is evaluated extensively in multiple experimental results that include the scenarios of position hold against an actuating wind-wall, adding payload, and changing the MAV’s arm configurations.

Deployment of the Micro Aerial Vehicle (MAV) in real-life applications poses multiple challenges, specially when there are external forces such as wind gust or interaction to the environment. Thus, this article proposes a novel Nonlinear Moving Horizon Estimation (NMHE) for estimating the external forces without adding extra sensor to the MAV or using external sensor for force measurements. The proposed method uses only the dynamic model, while avoiding the need to know aerodynamic models or other parameters of the MAV. The NMHE problem is solved using Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization, completely matrix-free, not sensitive to ill conditioning, and involves only simple algebraic operations. The solver has the ability to provide the estimation of the states and external forces, such as wind at a rate of 5 ms, which can be used online for the MAV controller. The proposed method is evaluated in a closed loop simulation environment with position controller in present of measurement noise and varying external forces, while it results to the fast convergent of the state and external force estimations.

Human postural control is a complex system and changes as we age. Frequency based analyses have been argued to be useful to identify altered postural control strategies in balance tasks. The aim of this study was to explore the frequency domain of the quiet stance centre of pressure of older adults with various degrees of fall-related concerns and sensorimotor functioning. We included 45 community dwelling older adults and used a force plate to register 30 seconds of quiet stance with eyes open and closed respectively. We also measured sensory and motor functions, as well as fall-related concerns and morale. We analysed the centre of pressure power spectrum density and extracted the frequency of 4 of its features for each participant. Orthogonal projection of latent structures–discriminant analysis revealed two groups for each quiet stance trial. Group 1 of each trial showed less sensory and motor decline, low/no fall-related concerns and higher frequencies. Group 2 showed more decline, higher fall-related concerns and lower frequencies. During the closed eyes trial, group 1 and group 2 shifted their features to higher frequencies, but only group 1 did so in any significant way. Higher fall-related concerns, sensory and motor decline, and explorative balancing strategies are highly correlated. The control system of individuals experiencing this seems to be highly dependent on vision. Higher fall-related concerns, and sensory and motor decline are also correlated with the inability to adjust to faster, more reactive balancing strategies, when vision is not available.

Fear of falling (FoF) is a major health problem, especially in elders, which can lead to falls, injury, loss of independence, and premature needs of nursing and assistance. However, most of the studies have focused on the psychological aspect of the FoF and there is a significant lack of technological assistance and methodology to detect and eliminate the effects of this fear on maintaining balance. In this article, we propose a novel method to detect the FoF as a quantitative signal. In our proposed novel approach, fear is considered as an internal disturbance inside a Central Nervous System (CNS) that can affect the generated output torque to each joint of the psychical body. By assuming the human body in a quiet stance, as an inverted pendulum model, this disturbance signal is estimated by Moving Horizon Estimation (MHE). For this purpose, the body kinetics and kinematics measurements of forty-five subjects during upright stance trails, as well as the psychological FoF falls efficacy test, were collected and utilized for the estimation and validation of the results. The experimental results show that the subjects with FoF present a higher variation in the estimated signal. This method can sufficiently detect the FoF by the posturographic and motion data, which can be utilized on the future assistive devices for prevention and treatment of the FoF and falls.

Background

Postural control is a complex system. Based on sensorimotor integration, the central nervous system (CNS) maintains balance by sending suitable motor commands to the muscles. Physiological decline due to ageing, affects balance performance through failing postural control – and in turn affects falls self-efficacy and activity participation. Understanding how the CNS adapts to these changes and predicts the appropriate motor commands to stabilize the body, has been a challenge for postural control research the latest years.

Aims

To understand and model the performance of the central nervous system as the controller of the human body.

Methods

Modelling was based on postural control data from 45 older adults (70 years and older). Ankle, knee and hip joint kinematics were measured during quiet stance using a motion capture system. Principal component analysis was used in order to reduce the measured multidimensional kinematics from a set of correlated discrete time series to a set of principal components. The outcome was utilized to predict the motor commands. The adaptive behaviour of the CNS was modelled by recurrent neural network including the efference copy for rapid predictions. The data from joint kinematics and electromyography (EMG) signals of the lower limb muscles were measured and separated into training and test data sets.

Results

The model can predict postural motor commands with very high accuracy regardless of a large physiological variability or balancing strategies. This model has three characteristics: a) presents an adaptive scheme to individual variability, 2) showcases the existence of an efference copy, and 3) is human experimental data driven.

Conclusion

The model can adapt to physical body characteristics and individual differences in balancing behaviour, while successfully predict motor commands. It should therefore be utilised in the continued pursuit of a better understanding of ageing postural control.