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Instrumented insoles could benefit locomotion research on healthy and clinical populations by providing data in natural settings outside of the laboratory. We designed a low-cost, instrumented insole with 8 pneumatic bladders to measure localized plantar pressure information. We collected gait data during treadmill walking at 1.0 m/s and 1.5 m/s and for sit-to-stand and stand-tosit tasks for 10 subjects. We estimated a common representation of ground kinetics (3-component force vector, 2-component center of pressure position vector, and a single-component torque vector) from the insole data. We trained an intertask neural network for each component of the kinetic data. For the walking tasks at 1.0 m/s and 1.5 m/s, the normalized root mean square error was between 3.1% and 12.9% and for the sit-to-stand and stand-to-sit tasks, the normalized root mean square error was between 3.3% and 21.3% Our findings suggest that the proposed low-cost, instrumented insoles could provide useful data about movement kinetics during real-world activities.

Robotic exoskeletons and bionic prostheses have moved from science fiction to science reality in the last decade. These robotic devices for assisting human movement are now technically feasible given recent advancements in robotic actuators, sensors, and computer processors. However, despite the ability to build robotic hardware that is wearable by humans, we still do not have optimal controllers to allow humans to move with coordination and grace in synergy with the robotic devices. We consider the history of robotic exoskeletons and bionic limb prostheses to provide a better assessment of the roadblocks that have been overcome and to gauge the roadblocks that still remain. There is a strong need for kinesiologists to work with engineers to better assess the performance of robotic movement assistance devices. In addition, the identification of new performance metrics that can objectively assess multiple dimensions of human performance with robotic exoskeletons and bionic prostheses would aid in moving the field forward. We discuss potential control approaches for these robotic devices, with a preference for incorporating feedforward neural signals from human users to provide a wider repertoire of discrete and adaptive rhythmic movements.

Studying human and animal locomotion on an uneven terrain can be beneficial to basic science and applied studies for clinical and robotic applications. Traditional biomechanical analysis of human locomotion has often been limited to laboratory environments with flat, smooth runways and treadmills. The authors modified a regular exercise treadmill by attaching wooden blocks to the treadmill belt to yield an uneven locomotion surface. To ensure that these treadmill modifications facilitated biomechanical measurements, the authors compared ground reaction force data collected while a subject ran on the modified instrumented treadmill with a smooth surface with data collected using a conventional instrumented treadmill. Comparisons showed only minor differences. These results suggest that adding an uneven surface to a modified treadmill is a viable option for studying human or animal locomotion on an uneven terrain. Other types of surfaces (eg, compliant blocks) could be affixed in a similar manner for studies on other types of locomotion surfaces.

During passive lower limb movement, active use of the upper limbs increases unintentional lower limb muscle activation. We hypothesized that faster movement frequencies would amplify lower limb muscle activation during upper limb exertion but would not affect lower limb muscle activation when the upper limbs were relaxed. We studied 10 healthy participants exercising on a recumbent stepping machine that mechanically coupled the four limbs via handles and pedals. Participants exercised at four frequencies (30, 60, 90, 120 steps/min) under four conditions of active and passive movement. Self-driven lower limb motion resulted in greater muscle activation compared to externally driven lower limb motion. Muscle activation amplitude increased with frequency for all conditions except for externally driven stepping. These results indicate that fast upper limb movement facilitates neuromuscular recruitment of lower limb muscles during stepping tasks. If a similar effect occurs in neurologically impaired individuals during active stepping, self-assisted exercise might enhance neuromuscular recruitment during rehabilitation.

We developed a pneumatically powered orthosis for the human ankle joint. The orthosis consisted of a carbon fiber shell, hinge joint, and two artificial pneumatic muscles. One artificial pneumatic muscle provided plantar flexion torque and the second one provided dorsiflexion torque. Computer software adjusted air pressure in each artificial muscle independently so that artificial muscle force was proportional to rectified low-pass-filtered electromyography (EMG) amplitude (i.e., proportional myoelectric control). Tibialis anterior EMG activated the artificial dorsiflexor and soleus EMG activated the artificial plantar flexor. We collected joint kinematic and artificial muscle force data as one healthy participant walked on a treadmill with the orthosis. Peak plantar flexor torque provided by the orthosis was 70 Nm, and peak dorsiflexor torque provided by the orthosis was 38 Nm. The orthosis could be useful for basic science studies on human locomotion or possibly for gait rehabilitation after neurological injury.

Although there have been many lower limb robotic exoskeletons that have been tested for human walking, few devices have been tested for assisting running. It is possible that a pseudo-passive elastic exoskeleton could benefit human running without the addition of electrical motors due to the spring-like behavior of the human leg. We developed an elastic lower limb exoskeleton that added stiffness in parallel with the entire lower limb. Six healthy, young subjects ran on a treadmill at 2.3 m/s with and without the exoskeleton. Although the exoskeleton was designed to provide ~50% of normal leg stiffness during running, it only provided 24% of leg stiffness during testing. The difference in added leg stiffness was primarily due to soft tissue compression and harness compliance decreasing exoskeleton displacement during stance. As a result, the exoskeleton only supported about 7% of the peak vertical ground reaction force. There was a significant increase in metabolic cost when running with the exoskeleton compared with running without the exoskeleton (ANOVA, P < .01). We conclude that 2 major roadblocks to designing successful lower limb robotic exoskeletons for human running are human-machine interface compliance and the extra lower limb inertia from the exoskeleton.