The workflow to simulate motion with recorded data usually starts with selecting a generic musculoskeletal model and scaling it to represent subject-specific characteristics. Simulating muscle dynamics with muscle-tendon parameters computed from existing scaling methods in literature, however, yields some inconsistencies compared to measurable outcomes. For instance, simulating fiber lengths and muscle excitations during walking with linearly scaled parameters does not resemble established patterns in the literature. This study presents a tool that leverages reported in vivo experimental observations to tune muscle-tendon parameters and evaluates their influence in estimating muscle excitations and metabolic costs during walking. From a scaled generic musculoskeletal model, we tuned optimal fiber length, tendon slack length, and tendon stiffness to match reported fiber lengths from ultrasound imaging and muscle passive force-length relationships to match reported in vivo joint moment-angle relationships. With tuned parameters, muscle contracted more isometrically, and soleus\'s operating range was better estimated than with linearly scaled parameters. Also, with tuned parameters, on/off timing of nearly all muscles\' excitations in the model agreed with reported electromyographic signals, and metabolic rate trajectories varied significantly throughout the gait cycle compared to linearly scaled parameters. Our tool, freely available online, can customize muscle-tendon parameters easily and be adapted to incorporate more experimental data.

Walking on a split-belt treadmill is often compared to walking on tied belts at the average speed, but the relationship between the metabolic energy costs of split- and tied-belt walking remains largely unexplored. Recent simulation work has suggested that people could take advantage of a belt speed difference and lower their energy costs, but this effect has not yet been observed experimentally. To relate metabolic energy costs across a range of belt speed combinations, we had 10 participants each complete 14 tied-belt and 39 split-belt walking trials, with early split-belt trials incorporating additional time for training. The average speeds ranged from 0.6 to 1.8 m/s with belt speed differences up to 1.4 m/s. We used ANOVA to determine differences in energy cost due to average speed and belt speed difference. We fit a linear model to estimate the tied-belt speed with the same energy cost as a given pair of split belt speeds. The cost of split-belt walking was on average just 0.13 ± 0.32 W/kg more expensive than the cost of tied-belt walking at the average speed. Contrary to predictions from simple dynamical models, increased belt speed difference resulted in increased energy cost, and the energetically equivalent tied-belt speed could be estimated as veq=vavg+0.065⋅Δv. Clinicians designing rehabilitation protocols can balance the therapeutic benefits of higher belt speed difference with increased energetic demands. Open questions remain about why people cannot fully take advantage of mechanical work performed by a split-belt treadmill.

Powered ankle exoskeletons that apply assistive torques with optimized timing and magnitude can reduce metabolic cost by ∼10% compared to normal walking. However, finding individualized optimal control parameters is time consuming and must be done independently for different walking modes (e.g., speeds, slopes). Thus, there is a need for exoskeleton controllers that are capable of continuously adapting torque assistance in concert with changing locomotor demands. One option is to use a biologically inspired, model-based control scheme that can capture the adaptive behavior of the human plantarflexors during natural gait. Here, based on previously demonstrated success in a powered ankle-foot prosthesis, we developed an ankle exoskeleton controller that uses a neuromuscular model (NMM) comprised of a Hill type musculotendon driven by a simple positive force feedback reflex loop. To examine the effects of NMM reflex parameter settings on (i) ankle exoskeleton mechanical performance and (ii) users\' physiological response, we recruited nine healthy, young adults to walk on a treadmill at a fixed speed of 1.25 m/s while donning bilateral tethered robotic ankle exoskeletons. To quantify exoskeleton mechanics, we measured exoskeleton torque and power output across a range of NMM controller Gain (0.8-2.0) and Delay (10-40 ms) settings, as well as a High Gain/High Delay (2.0/40 ms) combination. To quantify users\' physiological response, we compared joint kinematics and kinetics, ankle muscle electromyography and metabolic rate between powered and unpowered/zero-torque conditions. Increasing NMM controller reflex Gain caused increases in average ankle exoskeleton torque and net power output, while increasing NMM controller reflex Delay caused a decrease in net ankle exoskeleton power output. Despite systematic reduction in users\' average biological ankle moment with exoskeleton mechanical assistance, we found no NMM controller Gain or Delay settings that yielded changes in metabolic rate. Post hoc analyses revealed weak association at best between exoskeleton and biological mechanics and changes in users\' metabolic rate. Instead, changes in users\' summed ankle joint muscle activity with powered assistance correlated with changes in their metabolic energy use, highlighting the potential to utilize muscle electromyography as a target for on-line optimization in next generation adaptive exoskeleton controllers.

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc.

When humans wish to move sideways, they almost never walk sideways, except for a step or two; they usually turn and walk facing forward. Here, we show that the experimental metabolic cost of walking sideways, per unit distance, is over three times that of forward walking. We explain this high metabolic cost with a simple mathematical model; sideways walking is expensive because it involves repeated starting and stopping. When walking sideways, our subjects preferred a low natural speed, averaging 0.575 m s(-1) (0.123 s.d.). Even with no prior practice, this preferred sideways walking speed is close to the metabolically optimal speed, averaging 0.610 m s(-1) (0.064 s.d.). Subjects were within 2.4% of their optimal metabolic cost per distance. Thus, we argue that sideways walking is avoided because it is expensive and slow, and it is slow because the optimal speed is low, not because humans cannot move sideways fast.

OBJECTIVE: To investigate the effect of rollover footwear on walking speed, metabolic cost of gait, lower limb kinematics, kinetics, EMG muscle activity and plantar pressure.
METHODS: Twenty subjects (mean age-33.1 years, height-1.71 m, body mass-68.9 kg, BMI 23.6, 12 male) walked in: a flat control footwear; a flat control footwear weighted to match the mass of a rollover shoe; a rollover shoe; MBT footwear. Data relating to metabolic energy and temporal aspects of gait were collected during 6 min of continuous walking, all other data in a gait laboratory.
RESULTS: The rollover footwear moved the contact point under the shoe anteriorly during early stance, increasing midfoot pressures. This changed internal ankle dorsiflexion moments to plantarflexion moments earlier, reducing ankle plantarflexion and tibialis anterior activity after initial contact, and increasing calf EMG activity. In mid stance the rollover footwear resulted in a more dorsiflexed ankle position but less ankle movement. During propulsion, the rollover footwear reduced peak ankle dorsiflexion, peak internal plantarflexor ankle moments and the range of ankle plantarflexion. Vertical ground reaction loading rates were increased by the rollover footwear. There were no effects on temporal or energy cost of gait and no effect of elevated shoe weight.
CONCLUSIONS: Investigating all proposed effects of this footwear concurrently has enabled a more valid investigation of how the footwear effects are interrelated. There were concurrent changes in several aspects of lower limb function, with greatest effects at the foot and ankle, but no change in the metabolic cost of walking.