Descending 13 real world steps: A dataset and analysis of stair descent

Sie, Astrini; Karrenbach, Maxim; Fisher, Charlie; Fisher, Shawn C.; Wieck, Nathaniel; Caraballo, Callysta; Case, Elisabeth; Boe, David; Muir, Brittney C.

doi:10.1016/j.gaitpost.2021.10.039

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…One of the main findings that arise from forensic incident investigations is that many “slips” are not true slips, but rather missteps. The available studies during stair descent focus on hip, knee, and ankle joint kinematics (Riener et al, 2002; Sie et al, 2022). However, very few studies focusing on descriptive kinematics of foot posture and foot placement on the tread surface are available, yet they are essential to better understand and support the causative factors of falls during stair descent.…”

Section: Discussionmentioning

confidence: 99%

“…General stair descent kinematics described in the literature focus on the trunk, hip, and legs (Jeon et al, 2020; McFadyen & Winter, 1988; Muhaidat et al, 2011). Few studies explore specific foot kinematics that can aid investigations of falls on stairs (Sie et al, 2022; Tsukagoshi et al, 2021). Specifically, it is relatively unknown how the feet are positioned over treads at key instances during the gait cycle and how it relates to the position of the center of mass.…”

Section: Introductionmentioning

confidence: 99%

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The Importance of Understanding Foot Posture Strategies during Stair Descent for Forensic Incident Investigations

Kefala,

Mantilla,

Levitan

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Falls on stairs are commonly claimed to be caused by slipping on the tread nosing, but few studies have explored specific foot kinematics at initial contact during stair descent. Further understanding of such kinematics can aid forensic incident investigations. We investigated foot posture at initial contact with the tread surface of seven participants while descending stairs to determine: a) foot posture variability across participants, and b) the effects of foot posture with respect to the nosing on foot placement. We calculated foot posture as the angle at initial contact relative to the horizontal plane using two consistent landmark points on the subjects’ shoes. Our preliminary results demonstrated inter-subject and intra-subject significant differences in foot posture (P<0.05) for both legs. These results demonstrate that further exploring this at specific instances of the gait cycle during stair descent is crucial to better understand fall events during forensic investigations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Importance of Understanding Foot Posture Strategies during Stair Descent for Forensic Incident Investigations

Kefala,

Mantilla,

Levitan

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Most gait datasets currently available fall under the following categories: (1) Full-body kinematics or kinetics from level-ground walking or running in a lab environment, comprised of straight walking (~10 m) at different self-selected speeds [7][8][9][10][11] , (2) full-body kinematics or kinetics from treadmill walking or running at pre-defined speeds 7,[11][12][13] , (3) standing data collected on perturbation platforms 14,15 , and (4) staircase walking on in-lab staircases with 4-7 steps 11,16 . For a more comprehensive review of stair ambulation, please refer to 17 . These datasets were collected using in-lab motion capture systems and collectively comprise most of the publicly available gait datasets.…”

Section: Background and Summarymentioning

confidence: 99%

A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision

et al. 2023

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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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“…However, the methods are application agnostic. With the recent rise in multi-modal gait datasets [8], [15], [16], [17], [18], [19], [20], we can also learn representations of hard to define factors in a similar way the representations of style were learned in this manuscript. Similarly, we could encode terrain in an appropriate latent space and then compose different representations using the decoder.…”

Section: Beyond Personalizationmentioning

confidence: 99%

Optimizing Representations of Multiple Simultaneous Attributes for Gait Generation Using Deep Learning

Sharma

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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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.

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Descending 13 real world steps: A dataset and analysis of stair descent

Cited by 5 publications

References 32 publications

The Importance of Understanding Foot Posture Strategies during Stair Descent for Forensic Incident Investigations

The Importance of Understanding Foot Posture Strategies during Stair Descent for Forensic Incident Investigations

A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision

Optimizing Representations of Multiple Simultaneous Attributes for Gait Generation Using Deep Learning

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