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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.

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Many upper-limb prostheses lack proper wrist rotation functionality, leading to users performing poor compensatory strategies, leading to overuse or abandonment. In this study, we investigate the validity of creating and implementing a data-driven predictive control strategy in object grasping tasks performed in virtual reality. We propose the idea of using gaze-centered vision to predict the wrist rotations of a user and implement a user study to investigate the impact of using this predictive control. We demonstrate that using this vision-based predictive system leads to a decrease in compensatory movement in the shoulder, as well as task completion time. We discuss the cases in which the virtual prosthesis with the predictive model implemented did and did not make a physical improvement in various arm movements. We also discuss the cognitive value in implementing such predictive control strategies into prosthetic controllers. We find that gaze-centered vision provides information about the intent of the user when performing object reaching and that the performance of prosthetic hands improves greatly when wrist prediction is implemented. Lastly, we address the limitations of this study in the context of both the study itself as well as any future physical implementations.

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We anticipate wide adoption of wrist and forearm electomyographic (EMG) interface devices worn daily by the same user. This presents unique challenges that are not yet well addressed in the EMG literature, such as adapting for session-specific differences while learning a longer-term model of the specific user. In this manuscript we present two contributions toward this goal. First, we present the MiSDIREKt (Multi-Session Dynamic Interaction Recordings of EMG and Kinematics) dataset acquired using a novel hardware design. A single participant performed four kinds of hand interaction tasks in virtual reality for 43 distinct sessions over 12 days, totaling 814 min. Second, we analyze this data using a non-linear encoder-decoder for dimensionality reduction in gesture classification. We find that an architecture which recalibrates with a small amount of single session data performs at an accuracy of 79.5% on that session, as opposed to architectures which learn solely from the single session (49.6%) or learn only from the training data (55.2%).

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BACKGROUND: Stair descent analysis has been typically limited to laboratory staircases of 4 or 5 steps. To date there has been no report of gait parameters during unconstrained stair descent outside of the laboratory, and few motion capture datasets are publicly available. RESEARCH QUESTION: We aim to collect a dataset and perform gait analysis for stair descent outside of the laboratory. We aim to measure basic kinematic and kinetic gait parameters and foot placement behavior. METHODS: We present a public stair descent dataset from 101 unimpaired participants aged 18-35 on an unconstrained 13-step staircase collected using wearable sensors. The dataset consists of kinematics (full-body joint angle and position), kinetics (plantar normal forces, acceleration), and foot placement for 30,609 steps. RESULTS: We report the lower limb joint angle ranges (30° and 8° for hip flexion and extension, 85° and -11° for knee flexion and extension, and 31° and 28° for ankle dorsi- and plantar-flexion). The self-selected speed was 0.79 ± 0.16 m/s, with cycle duration of 0.97 ± 0.18 s. Mean foot overhang as a percentage of foot length was 17.07 ± 6.66 %, and we calculate that foot size explains only 6% of heel placement variation, but 79% of toe placement variation. We also find a minor but significant asymmetry between left and right maximum hip flexion angle, though all other measured parameters were symmetrical. SIGNIFICANCE: This is the first quantitative observation of gait data from a large number (n = 101) of participants descending an unconstrained staircase outside of a laboratory. This study enables analysis of gait characteristics including self-selected walking speed and foot placement to better understand typical stair gait behavior. The dataset is a public resource for understanding typical stair descent.

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We seek to use dimensionality reduction to simplify the difficult task of controlling a lower limb prosthesis. Though many techniques for dimensionality reduction have been described, it is not clear which is the most appropriate for human gait data. In this study, we first compare how Principal Component Analysis (PCA) and an autoencoder on poses (Pose-AE) transform human kinematics data during flat ground and stair walking. Second, we compare the performance of PCA, Pose-AE and a new autoencoder trained on full human movement trajectories (Move-AE) in order to capture the time varying properties of gait. We compare these methods for both movement classification and identifying the individual. These are key capabilities for identifying useful data representations for prosthetic control. We first find that Pose-AE outperforms PCA on dimensionality reduction by achieving a higher Variance Accounted For (VAF) across flat ground walking data, stairs data, and undirected natural movements. We then find in our second task that Move-AE significantly outperforms both PCA and Pose-AE on movement classification and individual identification tasks. This suggests the autoencoder is more suitable than PCA for dimensionality reduction of human gait, and can be used to encode useful representations of entire movements to facilitate prosthetic control tasks.

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INTRODUCTION: Affordable virtual reality (VR) technology is now widely available. Billions of dollars are currently being invested into improving and mass producing VR and augmented reality products. PURPOSE OF THE STUDY: The purpose of the present study is to explore the potential of immersive VR to make physical therapy/occupational therapy less painful, more fun, and to help motivate patients to cooperate with their hand therapist. DISCUSSION: The following topics are covered: a) psychological influences on pain perception, b) the logic of how VR analgesia works, c) evidence for reduction of acute procedural pain during hand therapy, d) recent major advances in VR technology, and e) future directions-immersive VR embodiment therapy for phantom limb (chronic) pain. CONCLUSION: VR hand therapy has potential for a wide range of patient populations needing hand therapy, including acute pain and potentially chronic pain patients. Being in VR helps reduce the patients' pain, making it less painful for patients to move their hand/fingers during hand therapy, and gamified VR can help motivate the patient to perform therapeutic hand exercises, and make hand therapy more fun. In addition, VR camera-based hand tracking technology may be used to help therapists monitor how well patients are doing their hand therapy exercises, and to quantify whether adherence to treatment increases long-term functionality. Additional research and development into using VR as a tool for hand therapist is recommended for both acute pain and persistent pain patient populations.

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