Inferring Control Intent during Seated Balance using Inverse Model Predictive Control

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.

Human-in-the-Loop Robot Control for Human-Robot Collaboration: HUMAN INTENTION ESTIMATION AND SAFE TRAJECTORY TRACKING CONTROL FOR COLLABORATIVE TASKS

This article provides a perspective on estimation and control problems in cyberphysical human systems (CPHSs) that work at the intersection of cyberphysical systems and human systems. The article also discusses solutions to some of the problems in CPHSs. One example of a CPHS is a close-proximity human-robot collaboration (HRC) in a manufacturing setting. The issue of the joint operation's efficiency and human factors, such as safety, attention, mental states, and comfort, naturally arise in the HRC context. By considering human factors, robots' actions can be controlled to achieve objectives, including safe operations and human comfort. Alternately, questions arise when robot factors are considered. For example, can we provide direct inputs and information to humans about an environment and the robots in the area such that the objectives of safety, efficiency, and comfort can be satisfied by considering the robots' current capabilities? The article discusses specific problems involved in HRC related to controlling a robot's motion by taking the current actions of the human in the loop with the robot's control system. To this end, two main challenges are discussed: 1) inferring the intention behind human actions by analyzing a person's motion as observed through skeletal tracking and gaze data and 2) a controller design that keeps robot motion constrained to a boundary in a 3D space by using control barrier functions. The intention inference method fuses skeleton-joint tracking data obtained using the Microsoft Kinect sensor and human gaze data gathered from red-green-blue Kinect images. The direction of a human's hand-reaching motion and a goal-reaching point is estimated while performing a joint pick-and-place task. The trajectory of the hand is estimated forward in time based on the gaze and hand motion data at the current time instance. A barrier function method is applied to generate safe robot trajectories along with forecast hand movements to complete the collaborative displacement of an object by a person and a robot. An adaptive controller is then used to track the reference trajectories using the Baxter robot, which is tested in a Gazebo simulation environment.

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.