Given two rewarding stimuli, animals tend to choose the more rewarding (or less effortful) option. However, they also move faster toward that stimulus [1-5]. This suggests that reward and effort not only affect decision-making, they also influence motor control [6, 7]. How does the brain compute the effort requirements of a task? Here, we considered data acquired during walking, reaching, flying, or isometric force production. In analyzing the decision-making and motor-control behaviors of various animals, we considered the possibility that the brain may estimate effort objectively, via the metabolic energy consumed to produce the action. We measured the energetic cost of reaching and found that, like walking, it was convex in time, with a global minimum, implying that there existed a movement speed that minimized effort. However, reward made it worthwhile to be energetically inefficient. Using a framework in which utility of an action depended on reward and energetic cost, both discounted in time, we found that it was possible to account for a body of data in which animals were free to choose how to move (reach slow or fast), as well as what to do (walk or fly, produce force F1 or F2). We suggest that some forms of decision-making and motor control may share a common utility in which the brain represents the effort associated with performing an action objectively via its metabolic energy cost and then, like reward, temporally discounts it as a function of movement duration.

Objective High-density electroencephelography (EEG) can provide insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer. Approach We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4–1.6 m/s. We then tested artifact removal methods including moving average and wavelet-based techniques. Main Results Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking. Significance Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removing of EEG movement artifact to advance the field.

It is often assumed that the CNS controls movements in a manner that minimizes energetic cost. While empirical evidence for actual metabolic minimization exists in locomotion, actual metabolic cost has yet to be measured during motor learning and/or arm reaching. Here, we measured metabolic power consumption using expired gas analysis, as humans learned novel arm reaching dynamics. We hypothesized that (1) metabolic power would decrease with motor learning and (2) muscle activity and coactivation would parallel changes in metabolic power. Seated subjects made horizontal planar reaching movements toward a target using a robotic arm. The novel dynamics involved compensating for a viscous curl force field that perturbed reaching movements. Metabolic power was measured continuously throughout the protocol. Subjects decreased movement error and learned the novel dynamics. By the end of learning, net metabolic power decreased by ϳ20% (ϳ0.1 W/kg) from initial learning. Muscle activity and coactivation also decreased with motor learning. Interestingly, distinct and significant reductions in metabolic power occurred even after muscle activity and coactivation had stabilized and movement changes were small. These results provide the first evidence of actual metabolic reduction during motor learning and for a reaching task. Further, they suggest that muscle activity may not explain changes in metabolic cost as completely as previously thought. Additional mechanisms such as more subtle features of arm muscle activity, changes in activity of other muscles, and/or more efficient neural processes may also underlie the reduction in metabolic cost during motor learning.

Strain sensors that are capable of monitoring complex human motions with high accuracy are highly desirable for developing wearable electronics. This paper reports the fabrication of highly stretchable and sensitive multidirectional strain sensors with tunable strain gauge factors by employing a digitally controlled printer to incorporate carbon nanotube (CNT) layers into polydimethylsiloxane (PDMS) substrates. The fabricated sensors exhibit a high stretchability (up to 45%) and sensitivity with a gauge factor of 35.75. The gauge factors could be easily modulated by tuning the number of CNT printing cycles to accommodate diverse requirements. The cyclic loading-unloading test results revealed that the composite strain sensors exhibited excellent long-term durability. Particularly, in this work, for the first time, human-motion-induced strain was measured by a motion capture system and compared with the strain data obtained from the fabricated strain sensors. The deviation of strains measured by composite sensors is less than 20%, indicating the great accuracy of CNT/PDMS sensors to quantify the amount of motion-induced strain. Of significant importance is that due to the flexibility of the printing technique used, rosette-type sensors were fabricated to simultaneously measure strains along multiple axes. These superior sensing capabilities of the fabricated CNT/PDMS strain sensors give them great application potential in motion-detecting systems.

Recent studies on neurologically intact individuals and individuals with spinal cord injury indicate that rhythmic upper limb muscle activation has an excitatory effect on lower limb muscle activation during locomotor-like tasks. This finding suggests that gait rehabilitation therapy after neurological injury should incorporate simultaneous upper limb and lower limb rhythmic exercise to take advantage of neural coupling.