Spacecraft rendezvous and docking maneuvers are becoming highly automated in an attempt to decrease astronaut workload, but still require a human supervisor to continually monitor the system and manually take over control when systems are not performing as expected. This requirement shifts astronaut workload to a monitoring and failure-mitigation task, which must be characterized to assess the influence of this task shift for automated rendezvous and docking (ARD). The physical performance of manual takeover maneuvers are well studied in other fields, such as automated vehicles, but less is known about the factors that influence the decision leading to takeover. This study operationalizes the concept of automation legibility (i.e., intent-expression) to gain insight into when and where supervisors initiate manual takeover. We hypothesized that fundamental aspects of autonomous agent path planning of initial condition and path curvature influence path legibility and takeover decision-making. The study had$N=33$participants who performed an ARD monitoring task. Metrics for legibility were defined using the positions, where the human initiated a manual takeover along the ARD path. Results support that initial condition, path curvature, and autonomous agent heading were interacting predictors of path legibility and takeover decision timing and location. Increased legibility was correlated to supervisor perceptions of path-appropriateness. The most legible paths aligned the supervisor’s egocentric viewpoint to the path goal, while putting targets of avoidance in the supervisor’s periphery. Characterizations of cooperative performance in human-automation interaction systems from this study can inform future ARD system design that mitigates workload in ARD task performance.

Lower-limb active exoskeletons may experience errors in operational settings due to imperfect algorithms, which may impact users' trust in the system and the human-exoskeleton fluency (the coordination of actions between the human and exoskeleton). In this study, we introduced pseudorandom catch trials (errors) in 1.68% of all strides, where an expected exoskeleton torque was not applied for a single stride, to understand the immediate and time-dependent responses to missed actuations. Participants (N = 15) completed a targeted stepping task while walking with a bilateral powered ankle exoskeleton. Human-exoskeleton fluency and trust were inferred from task performance (step accuracy), step characteristics (step length and width), muscle activity, and lower limb joint kinematics. Reductions in ankle plantarflexion during catch trials suggest user adaptation to the exoskeleton. Hip flexion and muscle activity were modulated to mitigate effects of the loss of exoskeleton torque and reduced plantarflexion during catch trials to support task accuracy and maintain step characteristics. Trust was not impacted by this level of error, as there were no significant differences in task performance or gait characteristics over time. Understanding the interactions between human-exoskeleton fluency, task accuracy, and gait strategies will support exoskeleton controller development. Future work will investigate various levels of actuation reliability to understand the transition where performance and trust are affected.

Lower-limb exoskeletons may experience errors in operational settings, where an expected assistive torque is missing. These errors may affect user's gait strategies and perception of the exoskeleton's performance, leading to impacted human-exoskeleton fluency and user trust in the system. In this study, we introduced five different exoskeleton control algorithms with fixed error rates up to 10% error (90% accuracy). Two groups of participants (N=22, 11 per group) walked with a bilateral ankle exoskeleton while completing a targeted stepping task and experienced each controller twice, but in different orders. The impact of exoskeleton error rates was assessed on step characteristics (step length and width), task performance (absolute task error), and perception of exoskeleton performance (survey responses). Step character-istics were not impacted by exoskeleton errors, but multiple participants were not able to achieve acceptable task accuracy and increased task error over time across all error rates. Increasing error rates negatively impacted users' perception of algorithm predictability, exoskeleton supportiveness, and probability of future usage. Perceived predictability and future usage probability transitioned from positive to negative between 2% and 5% error. Understanding the effect of increasing exoskeleton error rates informs minimum algorithm accuracy to support human-exoskeleton fluency and performance for gait-assist exoskeletons.

The future of human space exploration will involve extra-vehicular activities (EVAs) on foreign planetary surfaces (i.e., Mars), an activity that will have significantly different characteristics than exploration scenarios on Earth. These activities become challenging due to restricted vision and limitations placed on sensory feedback from altered gravity and the space suit. The use of a bulky, pressurized EVA suit perceptually disconnects human explorers from the hostile environment, increasing navigation workload and the risk of collision associated with traversing through unfamiliar terrain. Multimodal displays are considered as a means to provide information to the astronaut as cues to multiple sensory modalities enhance cognitive processing through taking advantage of multiple sensory resources, and are believed to communicate the risk more efficiently than unimodal cues. This paper presents a wearable multimodal interface system to examine human performance when visual, vibratory, and visual-vibratory cues are provided to aid in ground obstacle avoidance. The wearable system applies vibrotactile cues to the feet and visual cues through augmented reality glasses to convey obstacle location and proximity. Participants (n = 16) performed an overground obstacle avoidance task with the multimodal device. Performance metrics included completion time, subjective workload, head-down time, collisions, as well as gait parameters. Results indicate that information displays enhance task performance, with the visual-only display promoting the least head-down time over tactile-only or visual-tactile displays. Head-down time was the highest for trials without a display. Results provide implications for presenting information during physically active tasks such as suited obstacle avoidance.

While the literature supports the potential to provide occupational therapy through telemedicine, there are several design challenges embedded in creating an at-home telemonitoring system that can visualize the complex biomechanical data required for clinical decision making. The challenge highlighted here is the development of measures that align with current decision making methods, which will support the integration and translation of the technology. A modified cognitive task analysis methodology is presented that is composed of three segments: characterizing the subject matter, defining relevant information, and formalizing requirements. This method was implemented through observations of eight therapists over 21 patient sessions. There were nine unique modes of interaction observed between the clinician and patient. Embedded within these interactions were 14 different features monitored. Relevant performance measures were defined from these interactions to provide perception and comprehension situation awareness (SA) to the clinician about the patients task performance. Using specific activities observed during therapy sessions, sample metrics and a visualization are presented that followed from the SA requirements. Finally, the technological challenges associated with making these measurements were identified.

Robotic assistive devices show potential to aid hand function using surface electromyography (sEMG) as a control signal. Current implementations of these robotic systems typically do not include interaction with the environment, which naturally occurs during functional tasks. Further, many applications have experts place the sEMG sensors on specific muscles, which benefits precision alignment that may not be possible by non-experts. This study informs algorithm development for controlling assistive devices for grasping and releasing objects using kinematics and non-specifically placed sEMG sensors. Significant effects of object type were found in the grip aperture and joint kinematics. Muscle activity was significantly affected by small alignment changes in the sensor placement, yet the features analyzed showed anticipatory mechanisms prior to grasp and release. The appropriate inclusion of placement variability within a control architecture can be coupled with the kinematics and sEMG features to inform object type and anticipate grasp and release.

Accurate estimations of variability in multiple donnings of sensor suites may aid algorithm development for wearable motion capture systems that make use of Inertial Measurement Units (IMUs). The accuracy of any algorithm incorporating these sensors is limited by the accuracy of the sensor to segment calibration. When either sensor placement (use by a non-expert) or limb motion during calibration (natural human variation) vary, the estimations are affected. In this study, 22 participants self-placed IMUs on three locations and performed six prescribed motions during each of these five donnings. For absolute placement of the sensors, the chest location mean was less than the forearm, which was less than the bicep. For sensor orientation, the opposite ordering of location was found. No difference in sensor rotation was found between the bicep and forearm, but both locations differed from the chest location. Results were analyzed at the beginning of prescribed motions.

Mobile, interactive robots that operate in human-centric environments need the capability to safely and efficiently navigate around humans. This requires the ability to sense and predict human motion trajectories and to plan around them. In this paper, we present a study that supports the existence of statistically significant biomechanical turn indicators of human walking motions. Further, we demonstrate the effectiveness of these turn indicators as features in the prediction of human motion trajectories. Human motion capture data is collected with predefined goals to train and test a prediction algorithm. Use of anticipatory features results in improved performance of the prediction algorithm. Lastly, we demonstrate the closed-loop performance of the prediction algorithm using an existing algorithm for motion planning within dynamic environments. The anticipatory indicators of human walking motion can be used with different prediction and/or planning algorithms for robotics; the chosen planning and prediction algorithm demonstrates one such implementation for human-robot co-navigation.

Motion sensing has played an important role in the study of human biomechanics as well as the entertainment industry. Although existing technologies, such as optical or inertial based motion capture systems, have relatively high accuracy in detecting body motions, they still have inherent limitations with regards to mobility and wearability. In this paper, we present a soft motion sensing suit for measuring lower extremity joint motion. The sensing suit prototype includes a pair of elastic tights and three hyperelastic strain sensors. The strain sensors are made of silicone elastomer with embedded microchannels filled with conductive liquid. To form a sensing suit, these sensors are attached at the hip, knee, and ankle areas to measure the joint angles in the sagittal plane. The prototype motion sensing suit has significant potential as an autonomous system that can be worn by individuals during many activities outside the laboratory, from running to rock climbing. In this study we characterize the hyperelastic sensors in isolation to determine their mechanical and electrical responses to strain, and then demonstrate the sensing capability of the integrated suit in comparison with a ground truth optical motion capture system. Using simple calibration techniques, we can accurately track joint angles and gait phase. Our efforts result in a calculated trade off: with a maximum error less than 8%, the sensing suit does not track joints as accurately as optical motion capture, but its wearability means that it is not constrained to use only in a lab.

In this paper we present a soft lower-extremity robotic exosuit intended to augment normal muscle function in healthy individuals. Compared to previous exoskeletons, the device is ultra-lightweight, resulting in low mechanical impedance and inertia. The exosuit has custom McKibben style pneumatic actuators that can assist the hip, knee and ankle. The actuators attach to the exosuit through a network of soft, inextensible webbing triangulated to attachment points utilizing a novel approach we call the virtual anchor technique. This approach is designed to transfer forces to locations on the body that can best accept load. Pneumatic actuation was chosen for this initial prototype because the McKibben actuators are soft and can be easily driven by an off-board compressor. The exosuit itself (human interface and actuators) had a mass of 3500 g and with peripherals (excluding air supply) is 7144 g. In order to examine the exosuit's performance, a pilot study with one subject was performed which investigated the effect of the ankle plantar-flexion timing on the wearer's hip, knee and ankle joint kinematics and metabolic power when walking. Wearing the suit in a passive unpowered mode had little effect on hip, knee and ankle joint kinematics as compared to baseline walking when not wearing the suit. Engaging the actuators at the ankles at 30% of the gait cycle for 250 ms altered joint kinematics the least and also minimized metabolic power. The subject's average metabolic power was 386.7 W, almost identical to the average power when wearing no suit (381.8 W), and substantially less than walking with the unpowered suit (430.6 W). This preliminary work demonstrates that the exosuit can comfortably transmit joint torques to the user while not restricting mobility and that with further optimization, has the potential to reduce the wearer's metabolic cost during walking.

We describe the design of an active soft ankle-foot orthotic device powered by pneumatic artificial muscles for treating gait pathologies associated with neuromuscular disorders. The design is inspired by the biological musculoskeletal system of a human foot and a lower leg, and mimics the muscle-tendon-ligament structure. A key feature of the device is that it is fabricated with flexible and soft materials that provide assistance without restricting degrees of freedom at the ankle joint. Three pneumatic artificial muscles assist dorsiflexion as well as inversion and eversion. The prototype is also equipped with various embedded sensors for gait training and gait pattern analysis. The prototype is capable of 12° dorsiflexion from a resting position of an ankle joint and a 20° dorsiflexion from plantarflexion. Results of early feedback control experiments show controllability of ankle joint angles. Ultimately, we envision a system that not only can provide physical support to improve mobility but also can increase safety and stability during walking, while enhancing muscle usage and encouraging rehabilitation.