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Traditional gait event detection methods for heel strike and toe-off utilize thresholding with ground reaction force (GRF) or kinematic data, while recent methods tend to use neural networks. However, when subjects’ walking behaviors are significantly altered by an assistive walking device, these detection methods tend to fail. Therefore, this paper introduces a new long short-term memory (LSTM)-based model for detecting gait events in subjects walking with a pair of custom ankle exoskeletons. This new model was developed by multiplying the weighted output of two LSTM models, one with GRF data as the input and one with heel marker height as input. The gait events were found using peak detection on the final model output. Compared to other machine learning algorithms, which use roughly 8:1 training-to-testing data ratio, this new model required only a 1:79 training-to-testing data ratio. The algorithm successfully detected over 98% of events within 16ms of manually identified events, which is greater than the 65% to 98% detection rate of previous LSTM algorithms. The high robustness and low training requirements of the model makes it an excellent tool for automated gait event detection for both exoskeleton-assisted and unassisted walking of healthy human subjects. 

Active exoskeletons are emerging as ergonomic solutions in the construction sector to reduce work-related musculoskeletal injuries. While the benefits of active exoskeletons are promising, they can also cause increased muscle activity, leading to local muscular fatigue. This study aimed to examine the impact of the active exoskeleton system on the muscular activity of construction workers during common construction activities. Ten subjects completed material handling tasks under two weight conditions (10 and 30 lbs) in a lab-controlled environment, with and without using an active exoskeleton. Portable electromyography (EMG) sensors were used to measure lumbar erector spinae (LES) muscle activity in each condition. Four descriptive statistics features in the time and frequency domains were extracted from the collected signals. Results of the t-test showed a significant difference in the physiological metrics extracted from the subjects’ EMG signals of the LES muscle. Findings demonstrated that using active exoskeletons reduces the internal muscle force in the lower back regions of construction workers. 

User perceived exoskeleton comfort is likely important for device acceptance, but there is currently no validated instrument to measure it. The Visual Analogue Scale (VAS) is an existing tool to measure subjective human feedback by asking the user to mark a point on a line with each end of the line representing an opposing anchor statement. It can be modified to show the previous response, allowing the subject to directly indicate if the current condition is better or worse than the previous one. The goal of this study was to determine how well the modified VAS could measure user-perceived comfort as the exoskeleton control parameters were varied. To validate the survey, 14 healthy subjects walked in a pair of ankle exoskeletons with approximately ten distinct sets of control parameters tested in a prescribed order. Each set of control parameters was tested twice. After each trial, user-perceived comfort was measured using a two-question VAS survey. The repeatability coefficient was approximately 40 mm, similar to the total range of responses. The results were also inconsistent, with relative rankings between consecutive pairs of conditions matching for approximately 50% of comparisons. Thus, as tested, the VAS was not repeatable or consistent. It is possible that subject adaptation within the trial and over the course of the experiment may have impacted the results. Additional work is needed to develop a repeatable method to measure comfort and to determine how perceived comfort varies as subjects’ gain exoskeleton experience. 

Abstract For many planar bipedal models, each step is divided into a finite time single support period and an instantaneous double support period. During single support, the biped is typically underactuated and thus has limited ability to reject disturbances. The instantaneous nature of the double support period prevents nonimpulsive control during this period. However, if the double support period is expanded to finite time, it becomes overactuated. While it has been hypothesized that this overactuation during a finite-time double support period may improve disturbance rejection capabilities, this has not yet been tested. This paper presents a refined biped model by developing a finite-time, adaptive double support controller capable of handling the overactuation and limiting slip. Using simulations, we quantify the disturbance rejection capabilities of this controller and directly compare them to a typical, instantaneous double support model for a range of gait speeds and perturbations. We find that the finite-time double support controller increased the walking stability of the biped in approximately half of the cases, indicating that a finite-time double support period does not automatically increase disturbance rejection capabilities. We also find that the timing and magnitude of the perturbation can affect if a finite-time double support period enhances stability. Finally, we demonstrate that the adaptive controller reduces slipping. 

For many planar bipedal models, each step is divided into a finite time single support period and an instantaneous double support period. During single support, the biped is typically underactuated and thus has limited ability to reject disturbances. The instantaneous nature of the double support period prevents control during this period. However, if the double support period is expanded to finite time, this introduces an overactuated period into the model which may improve disturbance rejection capabilities. This paper derives and compares the performance of two finite-time double support controllers. The first controller uses time to drive the progression of the double support period and controls the joint angles. The second controller uses a time-invariant phase variable to drive the progression of the double support period and controls the joint velocities since it is not possible to control the joint positions. The disturbance rejection capabilities of both controllers are then quantified using simulations. The instantaneous double support model is also simulated for comparison. The instantaneous double support model can recover from the largest disturbances but it requires the greatest number of steps to do. The time-based double support controller can recover from the smallest range of disturbances but requires the fewest number of steps for a given perturbation size.