We used a neuromusculoskeletal model of bipedal walking to examine the effects of foot-ground friction conditions and gait patterns on slip- and trip-induced falls. We developed three two-dimensional neuro-musculoskeletal models in a self-organized manner representing young adults, elderly non-fallers, and elderly fallers. We simulated walking under different foot-ground friction conditions. The static friction coefficient between the foot and the ground was varied from 0.05 to 2.0. Under low friction conditions, the three gait models demonstrated slip-induced falls. The elderly faller model experienced the most slip. This is because the RCOF was higher in the elderly faller model due to its short stride length but much smaller foot clearance. Under high friction conditions, only the elderly faller model demonstrated trip-induced falls. Based on the analysis using the margin of stability, the forward postural stability of the model gradually decreased under high-friction conditions, with the toe of the swing foot contacting the ground and subsequently falling forward. These results imply that there is an optimal coefficient of friction for the ground to prevent slip- and trip-induced falls by people with less stable gaits, which may provide new insights into the design of shoes and floor surfaces for the elderly.

Background: Pelvis fractures are commonly stabilized by surgical implants to facilitate their healing. However, such implants immobilize the iliosacral joint for up to a year until removal. We report how iliosacral joint immobilization affects the walking of patients. Methods: The gaits of patients with immobilized sacroiliac joints after unstable pelvic fracture (n = 8; mean age: 45.63 ± 23.19; five females and three males) and sex- and age-matched healthy control individuals (n = 8; mean age: 46.50 ± 22.91; five females and three males) were recorded and analyzed using a motion capture system. The forces between the tread and feet were also recorded. Standard gait parameters as well as dynamic patterns of joint angles and moments of the lower extremities were analyzed using the simulation software OpenSim. Results: With the exception of hip extensor strength, the monitored joint parameters of the patients showed task-dependent deviations during walking, i.e., plantarflexor force was increased when stepping on an elevated surface, as were hip flexion and extensor moments, knee flexion and extensor moments, as well as ankle dorsiflexion and the associated negative plantarflexor force during stance on the elevated surface. Conclusions: Iliosacral joint fixation causes reduced forward and upward propulsion and requires an extended range of hip motion in the sagittal plane. Patients show significant mobility limitation after iliosacral screw fixation.

The interplay between articular cartilage (AC) and subchondral bone (SB) plays a pivotal role in cartilage homeostasis and functionality. As direct connective pathways between the two are poorly understood, we examined the location-dependent characteristics of the 3D microchannel network within the SB that connects the basal cartilage layer to the bone marrow (i.e. cartilage-bone marrow microchannel connectors; CMMC). 43 measuring points were defined on five human cadaveric femoral heads with no signs of osteoarthritis (OA) (age ≤ 60), and cartilage-bone cylinders with diameters of 2.00 mm were extracted for high-resolution scanning (n = 215). The micro-CT data were categorized into three groups (load-bearing region: LBR, n = 60; non-load-bearing region: NLBR, n = 60; and the peripheral rim: PR, n = 95) based on a gait analysis estimation of the joint reaction force (young, healthy cohort with no signs of OA). At the AC-SB interface, the number of CMMC in the LBR was 1.8 times and 2.2 times higher compared to the NLBR, and the PR, respectively. On the other hand, the median Feret size of the CMMC were smallest in the LBR (55.2 µm) and increased in the NLBR (73.5 µm; p = 0.043) and the PR (89.1 µm; p = 0.043). AC thickness was positively associated with SB thickness (Pearson's r = 0.48; p < 1e-13), CMMC number. (r = 0.46; p < 1e-11), and circularity index (r = 0.61; p < 1e-38). In conclusion, our data suggest that regional differences in the microchannel architecture of SB might reflect regional differences in loading.

Human-machine interfaces (HMI) play a pivotal role in the rehabilitation and daily assistance of lower-limb amputees. The brain of such interfaces is a control model that detects the user's intention using sensor input and generates corresponding output (control commands). With recent advances in technology, AI-based policies have gained attention as control models for HMIs. However, supervised learning techniques require affluent amounts of labeled training data from the user, which is challenging in the context of lower-limb rehabilitation. Moreover, a static pre-trained model does not take the temporal variations in the motion of the amputee (e.g., due to speed, terrain) into account. In this study, we aimed to address both of these issues by creating an incremental training approach for a torque prediction model using incomplete user-specific training data and biologically inspired temporal patterns of human gait. To reach this goal, we created a hybrid of two distinct approaches, a generic inter-individual and an adapting individual-specific model that exploits the inter-limb synergistic coupling during human gait to learn a function that predicts the torque at the ankle joint continuously based on the kinematic sequences of the hip, knee, and shank. An inter-individual generic base model learns temporal patterns of gait from a set of able-bodied individuals and predicts the gait patterns for a new individual, while the individual-specific adaptation model learns and predicts the temporal patterns of gait specific to a particular individual. The iterative training using the hybrid model was validated on eight able-bodied and five transtibial amputee subjects. It was found that, with the addition of estimators fitted to individual-specific data, the accuracy significantly increased from the baseline inter-individual model and plateaued within two to three iterations.

Tripping while walking has been identified as the most common cause of falls among the elderly as they tend to utilize a shuffling gait while walking, which increases the risk of falling. Since tripping occurs when toes make unexpected contact with objects on the floor, a number of studies have investigated the impact of foot clearance on the risk of tripping. However, only a few studies have examined the effects of foot–floor friction on the risk of tripping. Thus, in this current study, we investigated the effect of foot–floor friction on the probability of trip-induced falls during normal and shuffling gaits in a computational simulation study. We used a computational model with neural rhythm generators and neuromusculoskeletal systems to simulate gait in a self-organized manner. By changing the parameters of the neural rhythm generator, gait parameters such as step length, cadence, and foot clearance were automatically reduced, which simulated the shuffling gait. To alter the foot–floor friction, we changed the spring coefficient ratio of the floor in horizontal and vertical directions. As per our results, it was determined that slip-induced falls occurred under low foot–floor friction conditions in both normal and shuffling gaits, whereas trip-induced falls occurred under high foot–floor friction conditions only with a shuffling gait. These results suggest that optimal foot–floor friction may prevent trip- and slip-induced falls among the elderly.

Transfemoral amputation substantially impairs locomotion. To restore the lost locomotive capability, amputees rely on knee-ankle prostheses. Theoretically, active knee-ankle prostheses may better support natural gait than their passive counterparts by replacing the missing muscle function. The control algorithms of such active devices need to comprehend the user’s locomotive intention and convert them into control commands for actuating the prosthesis. For an active knee-ankle prosthesis, the gait variables to be controlled to allow the desired locomotion could be the knee angle, knee moment, ankle angle, ankle moment. In this paper, a random forest regression model is employed for the continuous prediction of these gait variables for level ground walking at self-selected normal speed. Experimentally obtained thigh kinematics were the input to the random forest model. The proposed method could predict the angles and moments of the knee and ankle with high accuracy (mean $R^{2}$ value of 0.97 for ankle angle, 0.98 for ankle moment, 0.99 for knee angle, 0.95 for knee moment across four able-bodied subjects). The proposed strategy shows potential for continuously controlling an active knee-ankle prosthesis for transfemoral amputees, whose thigh angular motion can be used to infer the required prosthetic moments or angles.

Intelligent control strategies for active biomimetic prostheses could exploit the inter-joint coordination of limbs in human gait in order to mimic the functioning of a biological joint. A machine learning regression model could be employed to learn an input-output relationship between the coordinated limb motion in human gait and predict the motion of a particular limb/joint given the motion of other limbs/joints. Such a model could be potentially used as a controller for an intelligent prosthesis which aims to restore the functioning similar to an intact biological joint. For this, the model needs to be tailored for each user by learning the gait pattern specific to the user. The challenge of training such machine learning regression models in prosthetic control is that, the desired reference output cannot be obtained from an amputee due to the missing limb. In this study, we investigate the feasibility of using two different methods for training a random forest algorithm using incomplete amputee-specific data to predict the ankle kinematics and dynamics from hip, knee, and shank kinematics. First is an inter-subject approach which learns a generalized input-output relationship from a group of able-bodied individuals and then applies this generalized relationship to amputees. Second is a subject-specific approach which maps the amputee's inputs to a desired normative reference output calculated from able-bodied individuals. The subject-specific model outperformed the inter-subject model in predicting the ankle angle and moment in most cases and can be potentially used for devising a control strategy for an intelligent biomimetic ankle.

Mechanisms behind how Parkinson’s disease (PD) affects cortical control of muscles during walking remain unclear. As PD typically develops in the elderly, investigation of aging effects is important to examine deficits regarding PD. Here, we demonstrated that PD causes weak corticomuscular synchronization during walking, but aging does not. This lower-than-normal corticomuscular communication may cause impaired muscle control during walking.

Electrical stimulation for inducing muscle contraction can be divided into peripheral nerve stimulation (PNS) and motor point stimulation (MPS). Although the neural pathways activated by PNS have been well studied, those by MPS are still unclear. Here we investigated whether MPS activates Ia-sensory nerves and induces antidromic firing of motor nerves. Ten able-bodied males and females participated in this study. We confirmed that soleus MPS did not induce the H-reflex while soleus PNS did. Furthermore, MPS of the tibialis anterior muscle did not induce the reciprocal inhibition of soleus muscle while PNS did. For testing the effect of MPS on motor neuron excitability, we examined the H-reflex modulation by soleus MPS. When the conditioning and test interval was under 100-ms and the conditioning stimulus intensity was above 30-mA, soleus MPS induced the H-reflex inhibition. This suggests that soleus MPS produces antidromic firing that can induce after-hyperpolarization. These results suggest that MPS predominantly activates the motor nerve without depolarizing the Iasensory nerve. Since MPS is applicable to larger number of muscles compared to PNS, utilizing MPS can lead to more versatile neuromodulation of the spinal cord.

Lower limb amputations impair normal locomotion. This calls for the use of prosthetic devices to restore the lost or disabled functionality. Most of the commercially available prostheses offer only passive assistance with limited capacity. On the other hand, active prostheses may better restore movement, by supporting missing muscle function with additional motor power. The control algorithms of such embedded motors must understand the users locomotive intention to produce the required locomotion similar to that of an able-bodied individual. For individuals with transtibial amputation, the control algorithm should produce the desired locomotion by controlling an active ankle joint to generate appropriate ankle angle and ankle moment. In this paper, a strategy is proposed for the continuous estimation of ankle angle and ankle moment during walking using a support vector regression approach. Experimentally obtained hip and knee joint motion data were provided as the inputs to the support vector regression model. It is shown that, for level ground walking at self-selected speed, the proposed method could predict the ankle angle and moment with high accuracy (mean R 2 value of 0.98 for ankle angle and 0.97 for ankle moment).

Transfemoral amputees use knee-ankle prostheses to restore impaired locomotion. Active prostheses can potentially overcome the limitations of passive ones by better supporting missing muscle function with additional motor power. The control algorithms of such embedded motors should be tailored to understand the users locomotive intention and translate them into the required locomotion similar to that of an able-bodied individual. For individuals with transfemoral amputation, the control algorithm should produce the desired locomotion by controlling an active knee and ankle joint to generate appropriate knee angle, knee moment, ankle angle and ankle moment. Machine learning models could be utilized to develop control algorithms for active prosthesis. In this paper, a random forest strategy is proposed for the continuous prediction of the angles and moments of the knee and ankle during walking. Experimentally obtained thigh motion data were provided as the input to the random forest model. It is shown that, for level ground walking at self-selected speed, the proposed method could predict the angles and moments of the knee and ankle with high accuracy (mean $R^{2}$ value of 0.995 for knee angle, 0.951 for knee moment, 0.966 for ankle angle, and 0.941 for ankle moment). The proposed strategy shows potential for continuously controlling an active ankle-knee prosthesis for a transfemoral amputee, whose thigh angular motion can be used to infer the required prosthetic moments and angles.

Parkinson’s disease (PD) is known to increase asymmetry and variability of bilateral movements. However, the mechanisms of such abnormalities are not fully understood. Here, we aimed to investigate whether kinematic abnormalities are related to cortical participation during bilateral, cyclical ankle movements, which required i) maintenance of a specific frequency and ii) bilateral coordination of the lower limbs in an anti-phasic manner. We analyzed electroencephalographic and electromyographic signals from nine men with PD and nine aged-matched healthy men while they sat and cyclically dorsi- and plantarflexed their feet. This movement was performed at a similar cadence to normal walking under two conditions: i) self-paced and ii) externally paced by a metronome. Participants with PD exhibited reduced range of motion and more variable bilateral coordination. However, participants with and without PD did not differ in the magnitude of corticomuscular coherence between the midline cortical areas and tibialis anterior and medial gastrocnemius muscles. This finding suggests that either the kinematic abnormalities were related to processes outside linear corticomuscular communication or PD-related changes in neural correlates maintained corticomuscular communication but not motor performance.

In humans, the midline primary motor cortex is active during walking. However, the exact role of such cortical participation is unknown. To delineate the role of the primary motor cortex in walking, we examined whether the primary motor cortex would activate leg muscles during movements that retained specific requirements of walking (i.e., locomotive actions). We recorded electroencephalographic and electromyographic signals from 15 healthy, young men while they sat and performed bilateral, cyclical ankle movements. During dorsiflexion, near-20-Hz coherence increased cyclically between the midline primary motor cortex and the co-contracting antagonistic pair (i.e., tibialis anterior and medial gastrocnemius muscles) in both legs. Thus, we have shown that dynamic increase in corticomuscular coherence, which has been observed during walking, also occurs during simple bilateral cyclical movements of the feet. A possible mechanism for such coherence is corticomuscular communication, in which the primary motor cortex participates in the control of movement. Furthermore, because our experimental task isolated certain locomotive actions, the observed coherence suggests that the human primary motor cortex may participate in these actions (i.e., maintaining a specified movement frequency, bilaterally coordinating the feet, and stabilizing the posture of the feet). Additional studies are needed to identify the exact cortical and subcortical interactions that cause corticomuscular coherence and to further delineate the functional role of the primary motor cortex during bilateral cyclical movements such as walking.

In this pilot study, we examined how effectively functional electrical stimulation (FES) and passive stepping mitigated orthostatic hypotension in participants with chronic spinal cord injury (SCI). While being tilted head-up to 70 ° from the supine position, the participants underwent four 10-min conditions in a random sequence: 1) no intervention, 2) passive stepping, 3) isometric FES of leg muscles, and 4) FES of leg muscles combined with passive stepping. We found that FES and passive stepping independently mitigated a decrease in stroke volume and helped to maintain the mean blood pressure. The effects of FES on stroke volume and mean blood pressure were greater than those of passive stepping. When combined, FES and passive stepping did not interfere with each other, but they also did not synergistically increase stroke volume or mean blood pressure. Thus, the present study suggests that FES delivered to lower limbs can be used in individuals with SCI to help them withstand orthostatic stress. Additional studies are needed to confirm whether this use of FES is applicable to a larger population of individuals with SCI.

Linked-segment representations of human body dynamics have been used extensively in biomechanics, ergonomics, and rehabilitation research to systemize thinking, make predictions, and suggest novel experiments. In the scope of upper body biomechanics, these models play an even more essential role as the human spine dynamics are difficult to study in vivo. No study exists to date, however, that specifically disseminates the technical details of a comprehensive three-dimensional model of the upper body for the purpose of estimating spinal joint torques and forces for a wide range of scenarios. Consequently, researchers are still bound to develop and implement their own models. Therefore, the objective of this study was to design a dynamic model of the upper body that can comprehensively estimate spinal joint torques and forces from upper body kinematics. The proposed three-dimensional model focuses on the actions of the lumbar and cervical vertebrae and consists of five lumbar segments (L1 to L5), the thorax, six cervical segments (C2 to C7), and the head. Additionally, the model: (1) is flexible regarding the kinematic nature of the spinal joints (free, constrained, or fixed); (2) incorporates all geometric and mass-inertia parameters from a single, high-resolution source; and (3) can be feasibly implemented via different inverse dynamics formulations. To demonstrate its practicality, the model was finally employed to estimate the lumbar and cervical joint torques during perturbed sitting using experimental motion data. Considering the growing importance of mathematical predictions, the developed model should become an important resource for researchers in different fields.

Linked-segment representations of the human body have been used extensively in biomechanics, ergonomics, and rehabilitation research to systemize thinking, make predictions, and suggest novel experiments [1]. In the particular scope of head and trunk biomechanics, they play an even more essential role as the dynamics of the spine are difficult to study in vivo compared to most other structures of the human body. Technical difficulties either preclude direct, yet noninvasive measurements of parameters (such as joint torques) or make experiments very time consuming and error prone [1,2]. As a result, mathematical modeling techniques are crucial for studying the dynamics of the upper body and to estimate joint torques and constraint forces for a wide range of scenarios.

Dynamic models of the human trunk have been extensively used to investigate the biomechanics of lower back pain and postural instability in different populations. Despite their diverse applications, previous models rely on intrinsic upper body segment parameters (UBSP), e.g., each segment's mass-inertia characteristics. However, a comprehensive UBSP set allowing state-of-the-art, three-dimensional (3D) dynamic modeling does not exist to date. Therefore, our objective was to establish a UBSP set of all vertebral trunk segments that is accurate and complete. Based on high-resolution, transverse color images, anatomical structures of the Male Visible Human (MVH) were digitally reconstructed via commercial software. Subsequently, we identified the 3D spinal joint and 3D center of mass coordinates, the mass, and the moment of inertia tensor for 24 vertebral trunk segments and 4 upper limb segments (two segments per arm). Since the MVH images are public domain, the parameters are uniquely verifiable and expandable to also include lower limb parameters. To demonstrate the UBSP set's practicality, the parameters were finally implemented in a previously proposed inverse dynamics model of the upper body. Our findings reveal that an accurate and complete UBSP set has been obtained that will be beneficial to (1) systemize thinking in postural control studies; (2) quantify the effect of impact forces on the head and trunk (e.g., during whiplash); (3) suggest population-specific experiments based on theoretical insights into trunk dynamics (e.g., regarding lower back pain); or (4) assess the feasibility of new surgical techniques (e.g., spinal fusion) and neuroprostheses (e.g., after spinal cord injury).