Limits in motor control bandwidth during stick balancing

Controlling stick balancing on a linear track: Delayed state feedback or delay-compensating predictor feedback?

A planar stick balancing task was investigated using stabilometry parameters (SP); a concept initially developed to assess the stability of human postural sway. Two subject groups were investigated: 6 subjects (MD) with many days of balancing a 90 cm stick on a linear track and 25 subjects (OD) with only one day of balancing experience. The underlying mechanical model is a pendulum-cart system. Two control force models were investigated by means of numerical simulations: (1) delayed state feedback (DSF); and (2) delay-compensating predictor feedback (PF). Both models require an internal model and are subject to certainty thresholds with delayed switching. Measured and simulated time histories were compared quantitatively using a cost function in terms of some essential SPs for all subjects. Minimization of the cost function showed that the control strategy of both OD and MD subjects can better be described by DSF. The control mechanism for the MD subjects was superior in two aspects: (1) they devoted less energy to controlling the cart's position; and (2) their perception threshold for the stick's angular velocity was found to be smaller. Findings support the concept that when sufficient sensory information is readily available, a delay-compensating PF strategy is not necessary.

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.

Assessing delay and lag in sagittal trunk control using a tracking task

Slower trunk muscle responses are linked to back pain and injury. Unfortunately, clinical assessments of spine function do not objectively evaluate this important attribute, which reflects speed of trunk control. Speed of trunk control can be parsed into two components: (1) delay, the time it takes to initiate a movement, and (2) lag, the time it takes to execute a movement once initiated. The goal of this study is to demonstrate a new approach to assess delay and lag in trunk control using a simple tracking task. Ten healthy subjects performed four blocks of six trials of trunk tracking in the sagittal plane. Delay and lag were estimated by modeling trunk control for predictable and unpredictable (control mode) trunk movements in flexion and extension (control direction) at movement amplitudes of 2°, 4°, and 6° (control amplitude). The main effect of control mode, direction, and amplitude of movement were compared between trial blocks to assess secondary influencers (e.g., fatigue). Only control mode was consistent across trial blocks with predictable movements being faster than unpredictable for both delay and lag. Control direction and amplitude effects on delay and lag were consistent across the first two trial blocks and less consistent in later blocks. Given the heterogeneity in the presentation of back pain, clinical assessment of trunk control should include different control modes, directions, and amplitudes. To reduce testing time and the influence of fatigue, we recommend six trials to assess trunk control.

Control at stability's edge minimizes energetic costs: expert stick balancing

Stick balancing on the fingertip is a complex voluntary motor task that requires the stabilization of an unstable system. For seated expert stick balancers, the time delay is 0.23 s, the shortest stick that can be balanced for 240 s is 0.32 m and there is a [Formula: see text]° dead zone for the estimation of the vertical displacement angle in the saggital plane. These observations motivate a switching-type, pendulum-cart model for balance control which uses an internal model to compensate for the time delay by predicting the sensory consequences of the stick's movements. Numerical simulations using the semi-discretization method suggest that the feedback gains are tuned near the edge of stability. For these choices of the feedback gains, the cost function which takes into account the position of the fingertip and the corrective forces is minimized. Thus, expert stick balancers optimize control with a combination of quick manoeuvrability and minimum energy expenditures.

Lumbar extensor muscle force control is associated with disability in people with chronic low back pain

BACKGROUND

The ability to control lumbar extensor force output is necessary for daily activities. However, it is unknown whether this ability is impaired in chronic low back pain patients. Similarly, it is unknown whether lumbar extensor force control is related to the disability levels of chronic low back pain patients.

METHODS

Thirty-three chronic low back pain and 20 healthy people performed lumbar extension force-matching task where they increased and decreased their force output to match a variable target force within 20%-50% maximal voluntary isometric contraction. Force control was quantified as the root-mean-square-error between participants' force output and target force across the entire, during the increasing and decreasing portions of the force curve. Within- and between-group differences in force-matching error and the relationship between back pain group's force-matching results and their Oswestry Disability Index scores were assessed using ANCOVA and linear regression respectively.

FINDINGS

Back pain group demonstrated more overall force-matching error (mean difference=1.60 [0.78, 2.43], P<0.01) and more force-matching error while increasing force output (mean difference=2.19 [1.01, 3.37], P<0.01) than control group. The back pain group demonstrated more force-matching error while increasing than decreasing force output (mean difference=1.74, P<0.001, 95%CI [0.87, 2.61]). A unit increase in force-matching error while decreasing force output is associated with a 47% increase in Oswestry score in back pain group (R²=0.19, P=0.006).

INTERPRETATION

Lumbar extensor muscle force control is compromised in chronic low back pain patients. Force-matching error predicts disability, confirming the validity of our force control protocol for chronic low back pain patients.

Less precise motor control leads to increased agonist-antagonist muscle activation during stick balancing

Human motor control has constraints in terms of its responsiveness, which limit its ability to successfully perform tasks. In a previous study, it was shown that the ability to balance an upright stick became progressively more challenging as the natural frequency (angular velocity without control) of the stick increased. Furthermore, forearm and trunk agonist and antagonist muscle activation increased as the natural frequency of the stick increased, providing evidence that the central nervous system produces agonist-antagonist muscle activation to match task dynamics. In the present study, visual feedback of the stick position was influenced by changing where subject focused on the stick during stick balancing. It was hypothesized that a lower focal height would degrade motor control (more uncertainty in tracking stick position), thus making balancing more challenging. The probability of successfully balancing the stick at four different focal heights was determined along with the average angular velocity of the stick. Electromyographic signals from forearm and trunk muscles were also recorded. As expected, the probability of successfully balancing the stick decreased and the average angular velocity of the stick increased as subjects focused lower on the stick. In addition, changes in the level of agonist and antagonist muscle activation in the forearm and trunk was linearly related to changes in the angular velocity of the stick during balancing. One possible explanation for this is that the central nervous system increases muscle activation to account for less precise motor control, possibly to improve the responsiveness of human motor control.

Perceptual and motor learning underlies human stick-balancing skill

We investigated the acquisition of skill in balancing a stick (52 cm, 34 g) on the fingertip in nine participants using three-dimensional motion analysis. After 3.5 h of practice over 6 wk, the participants could more consistently balance the stick for longer durations with greatly reduced magnitude and speed of stick and finger movements. Irrespective of level of skill, the balanced stick behaved like a normal noninverted pendulum oscillating under greater-than-gravity torque with simple harmonic motion about a virtual pivot located at the radius of gyration above the center of mass. The control input parameter was the magnitude ratio between the torque applied on the stick by the participant and the torque due to gravity. The participants utilized only a narrow range of this parameter, which did not change with practice, to rotate the stick like a linear mass-spring system. With increased skill, the stick therefore maintained the same period of oscillation but showed marked reductions in magnitude of both oscillation and horizontal translation. Better balancing was associated with 1) more accurate visual localization of the stick and proprioceptive localization of the finger and 2) reduced cross-coupling errors between finger and stick movements in orthogonal directions; i.e., finger movements in the anteroposterior plane became less coupled with stick tip movements in the mediolateral plane, and vice versa. Development of this fine motor skill therefore depended on perceptual and motor learning to provide improved estimation of sensorimotor state and precision of motor commands to an unchanging internal model of the rotational dynamics.