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We introduce a novel dataset containing 3-dimensional biomechanical and wearable sensor data from 22 able-bodied adults for multiple locomotion modes (level-ground/treadmill walking, stair ascent/descent, and ramp ascent/descent) and multiple terrain conditions of each mode (walking speed, stair height, and ramp inclination). In this paper, we present the data collection methods, explain the structure of the open dataset, and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics as a function of the gait phase. This dataset offers a comprehensive source of locomotion information for the same set of subjects to motivate applications in locomotion recognition, developments in robotic assistive devices, and improvement of biomimetic controllers that better adapt to terrain conditions. With such a dataset, models for these applications can be either subject-dependent or subject-independent, allowing greater flexibility for researchers to advance the field.

Walking and running have fundamentally different biomechanics, which makes developing devices that assist both gaits challenging. We show that a portable exosuit that assists hip extension can reduce the metabolic rate of treadmill walking at 1.5 meters per second by 9.3% and that of running at 2.5 meters per second by 4.0% compared with locomotion without the exosuit. These reduction magnitudes are comparable to the effects of taking off 7.4 and 5.7 kilograms during walking and running, respectively, and are in a range that has shown meaningful athletic performance changes. The exosuit automatically switches between actuation profiles for both gaits, on the basis of estimated potential energy fluctuations of the wearer’s center of mass. Single-participant experiments show that it is possible to reduce metabolic rates of different running speeds and uphill walking, further demonstrating the exosuit’s versatility.

Split-belt treadmills have become popular tools for investigating stability during walking by using belt accelerations to induce slip-like perturbations. While the onset timing of destabilizing perturbations is a critical determinant of an individual’s stabilizing response, previous studies have predominantly delivered belt acceleration perturbations at heel strike or have not explicitly controlled onset as a percentage of the gait cycle. To address this gap, we 1) developed an algorithm to target transient increases in unilateral belt speed to begin at specific percentages of the walking gait cycle, 2) validated the algorithm’s accuracy and precision, and 3) investigated the influence of different onset timings on spatial stability measures. We evaluated desired onset timings of 10, 15, 20, and 30% of the gait cycle during walking at 1.25 m/s and measured step lengths and widths, as well as anteroposterior and mediolateral margins of stability during the perturbed and four recovery steps in 10 able-bodied participants. From 800 perturbations, we found a mean (standard deviation) delay in onset timing of 5.2% (0.9%) of the gait cycle, or 56 (9) ms. We hypothesized later onset timings would elicit more stabilizing responses due to the less stable configuration of the body during late vs. early single stance. Our data generally supported this hypothesis – in comparison to earlier onset timings, later onset timings precipitated greater stabilizing responses, including larger step lengths, step widths, and anteroposterior/mediolateral margins of stability on the perturbed step, in addition to shorter step lengths and wider step widths on the first step post-perturbation.

Kinematic analysis is a useful and widespread tool used in research and clinical biomechanics for the quantification of human movement. Common marker-based optical motion capture systems are time intensive and require highly trained operators to obtain kinematic data. Markerless motion capture systems offer an alternative method for the measurement of kinematic data with several practical benefits. This work compared the kinematics of human gait measured using a deep learning algorithm-based markerless motion capture system to those from a standard marker-based motion capture system. Thirty healthy adult participants walked on a treadmill while data were simultaneously recorded using eight video cameras and seven infrared optical motion capture cameras, providing synchronized markerless and marker-based data for comparison. The average root mean square distance (RMSD) between corresponding joint centers was less than 2.5 cm for all joints except the hip, which was 3.6 cm. Lower limb segment angles relative to the global coordinate system indicated the global segment pose estimates from both systems were very similar, with RMSD of less than 5.5° for all segment angles except those that represent rotations about the long axis of the segment. Lower limb joint angles captured similar patterns for flexion/extension at all joints, ab/adduction at the knee and hip, and toe-in/toe-out at the ankle. These findings indicate that the markerless system would be a suitable alternative technology in cases where the practical benefits of markerless data collection are preferred.

The variety of poststroke impairments and compensatory mechanisms necessitate adaptive and subject-specific approaches to locomotor rehabilitation. To implement subject-specific, adaptive training to treadmill-based gait training, we developed a user-driven treadmill (UDTM) control algorithm that adjusts the user’s speed in real-time. This study examines the response of individuals poststroke to the combination of UDTM control and electrical stimulation of the paretic ankle musculature to augment forward propulsion during walking. Sixteen individuals poststroke performed a randomized series of walking tasks on an instrumented split-belt treadmill at their self-selected speeds 1) with fixed speed treadmill (FSTM) control only, 2) FSTM control and paretic limb functional electrical stimulation (FES), 3) UDTM control only, and 4) UDTM control and FES. With UDTM control and FES, participants selected speeds that were 0.13 m/s faster than their speeds with fixed speed control only. This instantaneous increase is comparable to the gains in SS speed seen after 12 weeks of training with FES and fast walking with fixed speed treadmill control by Kesar and colleagues (Δ = 0.18 m/s). However, we saw no significant differences in the corresponding push-off forces or trailing limb position. Since individuals can use a variety of strategies to change their walking speeds, it is likely that the differences among individual responses obscured trends in the group average changes in mechanics. Ultimately, the combination of UDTM control and functional electrical stimulation (FES) allows individuals to increase speeds after a short exposure and may be a beneficial addition to poststroke gait training programs.

Implementing user-driven treadmill control in gait training programs for rehabilitation may be an effective means of enhancing motor learning and improving functional performance. This study aimed to determine the effect of a user-driven treadmill control scheme on walking speeds, anterior ground reaction forces (AGRF), and trailing limb angles (TLA) of healthy adults. Twenty-three participants completed a 10-m overground walking task to measure their overground self-selected (SS) walking speeds. Then, they walked at their SS and fastest comfortable walking speeds on an instrumented split-belt treadmill in its fixed speed and user-driven control modes. The user-driven treadmill controller combined inertial-force, gait parameter, and position based control to adjust the treadmill belt speed in real time. Walking speeds, peak AGRF, and TLA were compared among test conditions using paired t-tests (α = 0.05). Participants chose significantly faster SS and fast walking speeds in the user-driven mode than the fixed speed mode (p > 0.05). There was no significant difference between the overground SS walking speed and the SS speed from the user-driven trials (p < 0.05). Changes in AGRF and TLA were caused primarily by changes in walking speed, not the treadmill controller. Our findings show the user-driven treadmill controller allowed participants to select walking speeds faster than their chosen speeds on the fixed speed treadmill and similar to their overground speeds. Since user-driven treadmill walking increases cognitive activity and natural mobility, these results suggest user-driven treadmill control would be a beneficial addition to current gait training programs for rehabilitation.