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.

Background:

Hand grip strength is frequently used as a measurement of muscle strength, especially among older adults. Muscle strength is only one of the many components in postural control and it is currently unclear to what extent hand grip strength is associated with postural control. The aim was to analyze the association between hand grip strength and lower limb muscle strength, and postural control among older adults.

Methods:

Forty-five community-dwelling individuals over 70 years of age provided isometric hand grip strength and lower limb strength (including hip extension and abduction, knee flexion and extension, and ankle dorsiflexion and plantarflexion), as well as postural control measurements. In the latter, center of pressure excursions were recorded for quiet stance and limits of stability tests on a force plate. Orthogonal projection of latent structures regression models were used to analyze associations between hand grip strength and lower limb strength as well as postural control, respectively.

Results:

Lower limb strength explained 74.4% of the variance in hand grip strength. All lower limb muscle groups were significantly associated with hand grip strength. In a corresponding model, postural control measured with center of pressure excursions explained 20.7% of the variance in a statistically significant, albeit weak, model.

Conclusions:

These results support that hand grip strength is a valid method to estimate lower limb strength among older adults on a group level. However, strength measurements seem insufficient as a substitute for measuring postural control, and therefore specific balance tests are necessary.

Oral films (OFs) continue to attract attention as drug delivery systems, particularly for pedatric and geriatric needs. However, immiscibility between different polymers limits the full potential of OFs from being explored. One example is pullulan (PUL), a novel biopolymer which often has to be blended with other polymers to reduce cost and alter its mechanical properties. In this study, the state-of-the-art in fabrication techniques, three-dimensional (3D) printing was used to produce hybrid film structures of PUL and hydroxypropyl methylcellulose (HPMC), which were loaded with caffeine as a model drug. 3D printing was used to control the spatial deposition of films. HPMC was found to increase the mean mechanical properties of PUL films, where the tensile strength, elastic modulus and elongation break increased from 8.9 to 14.5 MPa, 1.17 to 1.56 GPa and from 1.48% to 1.77%, respectively. In addition, the spatial orientation of the hybrid films was also explored to determine which orientation could maximize the mechanical properties of the hybrid films. The results revealed that 3D printing could modify the mechanical properties of PUL whilst circumventing the issues associated with immiscibility.

Rheology is an indispensable tool for formulation development, which when harnessed, can both predict a material’s performance and provide valuable insight regarding the material’s macrostructure. However, rheological characterizations are under-utilized in 3D printing of drug formulations. In this study, viscosity measurements were used to establish a mathematical model for predicting the printability of fused deposition modelling 3D printed tablets (Printlets). The formulations were composed of polycaprolactone (PCL) with different amounts of ciprofloxacin and polyethylene glycol (PEG), and different molecular weights of PEG. With all printing parameters kept constant, both binary and ternary blends were found to extrude at nozzle temperatures of 130, 150 and 170 C. In contrast PCL was unextrudable at 130 and 150 C. Three standard rheological models were applied to the experimental viscosity measurements, which revealed an operating viscosity window of between 100-1000 Pa.s at the apparent shear rate of the nozzle. The drug profile of the printlets were experimentally measured over seven days. As a proof-of-concept, machine learning models were developed to predict the dissolution behaviour from the viscosity measurements. The machine learning models were discovered to accurately predict the dissolution profile, with the highest f₂ similarity score value of 90.9 recorded. Therefore, the study demonstrated that using only the viscosity measurements can be employed for the simultaneous high-throughput screening of formulations that are printable and with the desired release profile.

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.

Understanding the performance of the human brain to stabilize the body remains an open fundamental research question. In this article, we study the hypothesis of internal model of the Central Nervous System (CNS) by a novel proposed architecture based on a recurrent neural network. The overall objective of the article and the main contribution stems from demonstrating the capability of replicating the balancing mechanisms of the brain by training the proposed bio-inspired network architecture with human balancing data and in the sequel applying the resulting control structure for controlling a single link inverted pendulum. Towards this direction, the body kinetics and kinematics measurements of forty-five subjects during upright stance trails were collected and utilized for training the proposed neural network. The efficacy of the proposed scheme will be proven through multiple simulation results with a single link inverted pendulum, where it will be demonstrated that the brain-inspired control scheme achieves a proper balance.

The human body is mechanically unstable, while the brain as the main controller, is responsible to maintain our balance. However, the mechanisms of the brain towards balancing are still an open research question and thus in this article, we propose a novel modeling architecture for replicating and understanding the fundamental mechanisms for generating balance in the humans. Towards this aim, a nonlinear Recurrent Neural Network (RNN) has been proposed and trained that has the ability to predict the performance of the Central Nervous System (CNS) in stabilizing the human body with high accuracy and that has been trained based on multiple collected human based balancing data and by utilizing system identification techniques. One fundamental contribution of the article is the fact that the obtained network, for the balancing mechanisms, is experimentally evaluated on a single link inverted pendulum that replicates the basic model of the human balance and can be directly extended in the area of humanoids and balancing exoskeletons.