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Young,
Aaron
Young,
Aaron
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ItemBiomechanics of locomotion during ground translation perturbations(Georgia Institute of Technology, 2023-02-23) Leestma, Jennifer K. ; Golyski, Pawel R. ; Smith, Courtney R. ; Sawicki, Gregory S. ; Young, AaronThe purpose of this data set is to enable the investigation of human balance and recovery strategies during perturbed walking. We performed a study where participants walked while being exposed to ground translation perturbations. We varied the magnitude, direction, and onset time of these perturbations while collecting various biomechanical outcome metrics.
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ItemMulti-Context, User-Independent, Real-Time Intent Recognition for Powered Lower-Limb Prostheses(Georgia Institute of Technology, 2023) Bhakta, Krishan ; Maldonado-Contreras, Jairo ; Camargo, Jonathan ; Zhou, Sixu ; Compton, William ; Herrin, Kinsey R. ; Young, AaronCommunity ambulation is a critical component in maintaining a healthy lifestyle but has numerous task demands that can be challenging for individuals with limb loss. In wearable robotics, specifically powered prostheses, a need exists to provide intuitive and seamless assistance to the user. We developed a user-independent and multi-context, intent recognition system that was deployed in real-time to an open-source knee and ankle powered prosthesis (OSL). The intent recognition system predicted user intent and environment attributes using embedded sensing and control. Eleven individuals with transfemoral amputation were recruited for this study, in which 7 individuals were used for real-time validation. Here, we proposed a hierarchical control framework in which the intelligent prosthesis would first predict locomotion mode and subsequently estimate an environmental variable (i.e., walking speed or slope). Two main conclusions were found: 1) the user-independent (IND) performance across mode, speed, and slope was not statistically different from user-dependent (DEP) models in real-time, even though the offline performance of the IND system was worse 2) IND walking speed estimates showed ~0.09 m/s average error and slope estimates showed ~0.95 deg average error, which provided acceptable performance for modulating ankle and knee assistance across multi-context scenarios. Our study suggests that intelligent controllers can generalize to individuals and can perform well in real-time. In addition, we made our training dataset and the developed machine learning models publicly available to an open-source repository. This approach provides novel prosthesis users with autonomous and task-dependent functionality across real-world walking tasks.
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ItemA Human Lower-Limb Biomechanics and Wearable Sensors Dataset During Cyclic and Non-Cyclic Activities(Georgia Institute of Technology, 2023) Scherpereel, Keaton ; Molinaro, Dean ; Inan, Omer ; Shepherd, Maxwell ; Young, AaronTasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task. To capture a more holistic picture of lower limb movements in everyday life, we collected data from 12 participants performing 20 non-cyclic activities (e.g. sit-to-stand, jumping, squatting, lunging, cutting) as well as 11 cyclic activities (e.g. walking, running) while kinematics (motion capture and IMUs), kinetics (force plates), and EMG were collected. This dataset provides normative biomechanics for a highly diverse range of activities and common tasks from a consistent set of participants and sensors.