Transfemoral Amputee Stumble Detection through Machine-Learning Classification: Initial Exploration with Three Subjects.
Objective: To train a machine-learning (ML) algorithm to classify stumbling in transfemoral amputee gait. Methods: Three subjects completed gait trials in which they were induced to stumble via three different means. Several iterations of ML algorithms were developed to ultimately classify whether i...
| Publicado en: | Prosthesis (2673-1592) Vol. 6; no. 2; pp. 235 - 251 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176904294&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176904294 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26731592 MVQF jtl: Prosthesis (2673-1592) issn: 26731592 maglogo: N pubinfo: dt: Apr2024 vid: 6 iid: 2 pid: 97109 pub: MDPI artinfo: ui: 176904294 176904294 176904294 10.3390/prosthesis6020018 176904294 ppf: 235 ppct: 16 formats: tig: atl: Transfemoral Amputee Stumble Detection through Machine-Learning Classification: Initial Exploration with Three Subjects. aug: au: Galey, Lucas Fuentes, Olac Gonzalez, Roger V. affil: Engineering Education and Leadership, The University of Texas at El Paso, El Paso, TX 79968, USA sug: subj: Lower Extremity Amputees Above-Knee Amputation Accidental Falls Machine Learning Classification Human Funding Source Male Female Recovery Prostheses and Implants Gait Training Data Collection Neural Networks (Computer) Male Female ab: Objective: To train a machine-learning (ML) algorithm to classify stumbling in transfemoral amputee gait. Methods: Three subjects completed gait trials in which they were induced to stumble via three different means. Several iterations of ML algorithms were developed to ultimately classify whether individual steps were stumbles or normal gait using leave-one-out methodology. Data cleaning and hyperparameter tuning were applied. Results: One hundred thirty individual stumbles were marked and collected during the trials. Single-layer networks including Long-Short Term Memory (LSTM), Simple Recurrent Neural Network (SimpleRNN), and Gradient Recurrent Unit (GRU) were evaluated at 76% accuracy (LSTM and GRU). A four-layer LSTM achieved an 88.7% classic accuracy, with 66.9% step-specific accuracy. Conclusion: This initial trial demonstrated the ML capabilities of the gathered dataset. Though further data collection and exploration would likely improve results, the initial findings demonstrate that three forms of induced stumble can be learned with some accuracy. Significance: Other datasets and studies, such as that of Chereshnev et al. with HuGaDB, demonstrate the cataloging of human gait activities and classifying them for activity prediction. This study suggests that the integration of stumble data with such datasets would allow a knee prosthesis to detect stumbles and adapt to gait activities with some accuracy without depending on state-based recognition. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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