Reliability of assessing postural control during seated balancing using a physical human-robot interaction

The effects of osteopathic manipulative treatment on pain and disability in patients with chronic low back pain: a single-blinded randomized controlled trial

CONTEXT

The evidence for the efficacy of osteopathic manipulative treatment (OMT) in the management of low back pain (LBP) is considered weak by systematic reviews, because it is generally based on low-quality studies. Consequently, there is a need for more randomized controlled trials (RCTs) with a low risk of bias.

OBJECTIVES

The objective of this study is to evaluate the efficacy of an OMT intervention for reducing pain and disability in patients with chronic LBP.

METHODS

A single-blinded, crossover, RCT was conducted at a university-based health system. Participants were adults, 21-65 years old, with nonspecific LBP. Eligible participants (n=80) were randomized to two trial arms: an immediate OMT intervention group and a delayed OMT (waiting period) group. The intervention consisted of three to four OMT sessions over 4-6 weeks, after which the participants switched (crossed-over) groups. The primary clinical outcomes were average pain, current pain, Patient-Reported Outcomes Measurement Information System (PROMIS) 29 v1.0 pain interference and physical function, and modified Oswestry Disability Index (ODI). Secondary outcomes included the remaining PROMIS health domains and the Fear Avoidance Beliefs Questionnaire (FABQ). These measures were taken at baseline (T0), after one OMT session (T1), at the crossover point (T2), and at the end of the trial (T3). Due to the carryover effects of OMT intervention, only the outcomes obtained prior to T2 were evaluated utilizing mixed-effects models and after adjusting for baseline values.

RESULTS

Totals of 35 and 36 participants with chronic LBP were available for the analysis at T1 in the immediate OMT and waiting period groups, respectively, whereas 31 and 33 participants were available for the analysis at T2 in the immediate OMT and waiting period groups, respectively. After one session of OMT (T1), the analysis showed a significant reduction in the secondary outcomes of sleep disturbance and anxiety compared to the waiting period group. Following the entire intervention period (T2), the immediate OMT group demonstrated a significantly better average pain outcome. The effect size was a 0.8 standard deviation (SD), rendering the reduction in pain clinically significant. Further, the improvement in anxiety remained statistically significant. No study-related serious adverse events (AEs) were reported.

CONCLUSIONS

OMT intervention is safe and effective in reducing pain along with improving sleep and anxiety profiles in patients with chronic LBP.

Policy Design for an Ankle-Foot Orthosis Using Simulated Physical Human-Robot Interaction via Deep Reinforcement Learning

This paper presents a novel approach for designing a robotic orthosis controller considering physical human-robot interaction (pHRI). Computer simulation for this human-robot system can be advantageous in terms of time and cost due to the laborious nature of designing a robot controller that effectively assists humans with the appropriate magnitude and phase. Therefore, we propose a two-stage policy training framework based on deep reinforcement learning (deep RL) to design a robot controller using human-robot dynamic simulation. In Stage 1, the optimal policy of generating human gaits is obtained from deep RL-based imitation learning on a healthy subject model using the musculoskeletal simulation in OpenSim-RL. In Stage 2, human models in which the right soleus muscle is weakened to a certain severity are created by modifying the human model obtained from Stage 1. A robotic orthosis is then attached to the right ankle of these models. The orthosis policy that assists walking with optimal torque is then trained on these models. Here, the elastic foundation model is used to predict the pHRI in the coupling part between the human and robotic orthosis. Comparative analysis of kinematic and kinetic simulation results with the experimental data shows that the derived human musculoskeletal model imitates a human walking. It also shows that the robotic orthosis policy obtained from two-stage policy training can assist the weakened soleus muscle. The proposed approach was validated by applying the learned policy to ankle orthosis, conducting a gait experiment, and comparing it with the simulation results.

Stability threshold during seated balancing is sensitive to low back pain and safe to assess

Challenging trunk neuromuscular control maximally using a seated balancing task is useful for unmasking impairments that may go unnoticed with traditional postural sway measures and appears to be safe to assess in healthy individuals. This study investigates whether the stability threshold, reflecting the upper limits in trunk neuromuscular control, is sensitive to pain and disability and is safe to assess in low back pain (LBP) patients. Seventy-nine subjects with non-specific LBP balanced on a robotic seat while rotational stiffness was gradually reduced. The critical rotational stiffness, K_Crit, that marked the transition between stable and unstable balance was used to quantify the individual's stability threshold. The effects of current pain, 7-day average pain, and disability on K_Crit were assessed, while controlling for age, sex, height, and weight. Adverse events (AEs) recorded at the end of the testing session were used to assess safety. Current pain and 7-day average pain were strongly associated with K_Crit (current pain p < 0.001, 7-day pain p = 0.023), reflecting that people experiencing more pain have poorer trunk neuromuscular control. There was no evidence that disability was associated with K_Crit, although the limited range in disability scores in subjects may have impacted the analysis. AEs were reported in 13 out of 79 total sessions (AE Severity: 12 mild, 1 moderate; AE Relatedness: 1 possibly, 11 probably, 1 definitely-related to the study). Stability threshold is sensitive to pain and appears safe to assess in people with LBP, suggesting it could be useful for identifying trunk neuromuscular impairments and guiding rehabilitation.

Perceptual adaptation during a balancing task in the seated posture and its theoretical model

This paper proposes a theoretical model of the control and perception mechanism in human balance. Human balance perception is evaluated by the subjective upright posture, the posture at which a person does not feel he/she is at an incline. Our balance experiments in the seated posture showed that the subjective upright posture changed after the balancing task where the participants needed to incline to maintain their balance. This paper aimed to explain this adaptive phenomenon by reproducing the experimental results using computer simulations. Hypothesizing that "humans gradually come to recognize the posture they need to take to maintain their balance as being upright," an adaptation rule for subjective upright posture is defined, so that it approaches the averaged posture in the period of the balancing task. For the balance control, center of pressure feedback is adopted. As a result, the similar changes in subjective upright posture are simulated with a two-link model with a base link, implying that our hypothesis is one possible explanation on the mechanism for human balance control and perception.

Quantifying trunk neuromuscular control using seated balancing and stability threshold

Performance during seated balancing is often used to assess trunk neuromuscular control, including evaluating impairments in back pain populations. Balancing in less challenging environments allows for flexibility in control, which may not depend on health status but instead may reflect personal preferences. To make assessment less ambiguous, trunk neuromuscular control should be maximally challenged. Thirty-four healthy subjects balanced on a robotic seat capable of adjusting rotational stiffness. Subjects balanced while rotational stiffness was gradually reduced. The rotational stiffness at which subjects could no longer maintain balance, defined as critical stiffness (k_Crit), was used to quantify the subjects' trunk neuromuscular control. A higher k_Crit reflects poorer control, as subjects require a more stable base to balance. Subjects were tested on three days separated by 24 hours to assess test-retest reliability. Anthropometric (height and weight) and demographic (age and sex) influences on k_Crit and its reliability were assessed. Height and age did not affect k_Crit; whereas, being heavier (p < 0.001) and female (p = 0.042) significantly increased k_Crit. Reliability was also affected by anthropometric and demographic factors, highlighting the potential problem of inflated reliability estimates from non-control related attributes. k_Crit measurements appear reliable even after removing anthropometric and demographic influences, with adjusted correlations of 0.612 (95%CI: 0.433-0.766) versus unadjusted correlations of 0.880 (95%CI: 0.797-0.932). Besides assessment, trainers and therapists prescribing exercise could use the seated balance task and k_Crit to precisely set difficulty level to a percentage of the subject's stability threshold to optimize improvements in trunk neuromuscular control and spine health.

Inferring Control Intent during Seated Balance using Inverse Model Predictive Control

Patients with Low Back Pain (LBP) are suggested to follow a protective coping strategy. Therefore, rehabilitation of these patients requires estimating their motor control strategies (the control intent). In this letter, we present an approach that infers the control intent by solving an inverse Model Predictive Control (iMPC) problem. The standard Model Predictive Control (MPC) structure includes constraints, therefore, it allows us to model the physiological constraints of motor control. We devised an iMPC algorithm to solve iMPC problems with experimentally collected output trajectories. We used experimental data of one healthy subject during a seated balance test that used a physical Human-Robot Interaction (pHRI). Results show that the estimated MPC weights reflected the task instructions given to the subject and yielded an acceptable goodness of fit. The iMPC solution suggests that the subject's control intent was dominated by minimizing the squared sum of a combination of the upper-body and lower-body angles and velocities.

Feasibility of Incorporating Test-Retest Reliability and Model Diversity in Identification of Key Neuromuscular Pathways During Head Position Tracking

To study the complex neuromuscular control pathways in human movement, biomechanical parametric models and system identification methods are employed. Although test-retest reliability is widely used to validate the outcomes of motor control tasks, it was not incorporated in system identification methods. This study investigates the feasibility of incorporating test-retest reliability in our previously published method of selecting sensitive parameters. We consider the selected parameters via this novel approach to be the key neuromuscular parameters, because they meet three criteria: reduced variability, improved goodness of fit, and excellent reliability. These criteria ensure that the parameter variability is below a user-defined value, the number of these parameters is maximized to enhance goodness of fit, and their test-retest reliability is above a user-defined value. We measured variability, the goodness of fit, and reliability using Fisher information matrix, variance accounted for, and intraclass correlation, respectively. We also incorporated model diversity as a fourth optional criterion to narrow down the solution space of key parameters. We applied this approach to the head position tracking tasks in axial rotation and flexion/extension. A total of forty healthy subjects performed the tasks during two visits. With variability and reliability measures ≤0.35 and ≥0.75, respectively, we selected three key parameters out of twelve with the goodness of fit >69%. The key parameters were associated with at least two neuromuscular pathways out of four modeled pathways (visual, proprioceptive, vestibular, and intrinsic), which is a measure of model diversity. Therefore, it is feasible to incorporate reliability and diversity in system identification of key neuromuscular pathways in our application.