Comparing neural control and mechanically intrinsic control of powered ankle exoskeletons.

There are an infinite number of ways to control an assistive robotic device; however, there is little consensus on which ways are better than others and why. We designed this study to compare the control of powered ankle exoskeletons using neural measurements to drive control versus that using mechanically intrinsic measurements. The controller driven by neural measurements was a dynamic gain proportional myoelectric controller using user's soleus muscle activity for an actuation signal. The controller driven by mechanically intrinsic measurements was a timing-based controller using detected heel-strikes of the user to appropriately time actuation. We designed these two controllers in such a way that both had the same average actuation signal and tested them with 8 healthy subjects. Results show no significant difference in metabolic work rate between the two controllers. Both controllers resulted in reductions in metabolic work rate of 19% below walking in the devices unpowered. We found that subjects using the timing-based mechanically intrinsic controller exhibited less positive and negative total ankle power than when using the dynamic gain proportional myoelectric controller. This finding was coupled with a reduction of 11.8% in soleus muscle activity. We believe these finding can have large implications for applications in rehabilitation and lend insight to when one controller is more appropriate to use than another.

Using wearable physiological sensors to predict energy expenditure.

Lower-limb assistive robotic devices are often evaluated by measuring a reduction in the user's energy cost. Using indirect calorimetry to estimate energy cost is poorly suited for real-time estimation and long-term collection. The goal of this study was to use data from wearable sensors to predict energy cost with better temporal resolution and less variability than breath measurements. We collected physiological data (heart rate, electrodermal activity, skin temperature) and mechanical data (EMG, accelerometry) from three healthy subjects walking on a treadmill at various speeds on level ground, inclined, and backwards. Ground truth energy cost was established by averaging steady-state breath measurements. Raw physiological signals correlated well with ground truth energy cost, but raw mechanical signals did not. Correlation of mechanical signals was improved by calculating accelerometer magnitudes and linear envelope EMG signals, and further improved by averaging the signals over several seconds. A multiple linear regression including physiological and mechanical data accurately predicted ground truth energy cost across all subjects and activities tested, with less variability and better temporal resolution than breath measurements. The sensors used in this study were fully portable, and such algorithms could be used to estimate energy cost of users in the real world. This could greatly improve the design, control, and evaluation of lower-limb assistive robotic devices.

Passive Dynamics Explain Quadrupedal Walking, Trotting, and Tölting.

This paper presents a simplistic passive dynamic model that is able to create realistic quadrupedal walking, tölting, and trotting motions. The model is inspired by the bipedal spring loaded inverted pendulum (SLIP) model and consists of a distributed mass on four massless legs. Each of the legs is either in ground contact, retracted for swing, or is ready for touch down with a predefined angle of attack. Different gaits, that is, periodic motions differing in interlimb coordination patterns, are generated by choosing different initial model states. Contact patterns and ground reaction forces (GRFs) evolve solely from these initial conditions. By identifying appropriate system parameters in an optimization framework, the model is able to closely match experimentally recorded vertical GRFs of walking and trotting of Warmblood horses, and of tölting of Icelandic horses. In a detailed study, we investigated the sensitivity of the obtained solutions with respect to all states and parameters and quantified the improvement in fitting GRF by including an additional head and neck segment. Our work suggests that quadrupedal gaits are merely different dynamic modes of the same structural system and that we can interpret different gaits as different nonlinear elastic oscillations that propel an animal forward.