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Abstract In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.2 total hours of movement data from a college student population with an approximately equal split of males to females. In addition, for one of the activities, we captured the egocentric field of view and gaze of the subjects using an eye tracker. Finally, we provide some examples of applications using the dataset and discuss how it might open possibilities for new studies in human gait analysis. 

Rich variations in gait are generated according to several attributes of the individual and environment, such as age, athleticism, terrain, speed, personal “style”, mood, etc. The effects of these attributes can be hard to quantify explicitly, but relatively straightforward to sample. We seek to generate gait that expresses these attributes, creating synthetic gait samples that exemplify a custom mix of attributes. This is difficult to perform manually, and generally restricted to simple, human-interpretable and handcrafted rules. In this manuscript, we present neural network architectures to learn representations of hard to quantify attributes from data, and generate gait trajectories by composing multiple desirable attributes. We demonstrate this method for the two most commonly desired attribute classes: individual style and walking speed. We show that two methods, cost function design and latent space regularization, can be used individually or combined. We also show two uses of machine learning classifiers that recognize individuals and speeds. Firstly, they can be used as quantitative measures of success; if a synthetic gait fools a classifier, then it is considered to be a good example of that class. Secondly, we show that classifiers can be used in the latent space regularizations and cost functions to improve training beyond a typical squared-error cost. 

Gait complexity is widely used to understand risk factors for injury, rehabilitation, the performance of assistive devices, and other matters of clinical interest. We analyze the complexity of out-of-the-lab locomotion activities via measures that have previously been used in gait analysis literature, as well as measures from other domains of data analysis. We categorize these broadly as quantifying either the intrinsic dimensionality, the variability, or the regularity, periodicity, or self-similarity of the data from a nonlinear dynamical systems perspective. We perform this analysis on a novel full-body motion capture dataset collected in out-of-the-lab conditions for a variety of indoor environments. This is a unique dataset with a large amount (over 24 h total) of data from participants behaving without low-level instructions in out-of-the-lab indoor environments. We show that reasonable complexity measures can yield surprising, and even profoundly contradictory, results. We suggest that future complexity analysis can use these guidelines to be more specific and intentional about what aspect of complexity a quantitative measure expresses. This will become more important as wearable motion capture technology increasingly allows for comparison of ecologically relevant behavior with lab-based measurements. 

Transitioning from one activity to another is oneof the key challenges of prosthetic control. Vision sensorsprovide a glance into the environment’s desired and futuremovements, unlike body sensors (EMG, mechanical). This couldbe employed to anticipate and trigger transitions in prosthesisto provide a smooth user experience.A significant bottleneck in using vision sensors has beenthe acquisition of large labeled training data. Labeling theterrain in thousands of images is labor-intensive; it would beideal to simply collect visual data for long periods withoutneeding to label each frame. Toward that goal, we apply anunsupervised learning method to generate mode labels forkinematic gait cycles in training data. We use these labels withimages from the same training data to train a vision classifier.The classifier predicts the target mode an average of 2.2 secondsbefore the kinematic changes. We report 96.6% overall and99.5% steady-state mode classification accuracy. These resultsare comparable to studies using manually labeled data. Thismethod, however, has the potential to dramatically scale withoutrequiring additional labeling. 

Data-driven gait prediction can provide a reference trajectory for a wide variety of simple and complex movements captured in the training data. Coordinated Movement (CM) is a data-driven approach that maps movements of the body to movements of target joints, such as the ankle and knee. We have previously shown that the performance of CM for complex activities can be improved by adding more training data. In this paper we demonstrate that performance can also be improved by 1) including a history of the target joint angles as inputs to the model and 2) dynamic reallocation of the importance of the inputs over time using a neural network technique called Attention. These modifications are applicable when additional training data is limited. We also observe that Attention can follow important events in gait over time, adding interpretability to the system.