WEARABLE SENSOR-BASED GAIT CLASSIFICATION IN IDIOPATHIC TOE WALKING ADOLESCENTS
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

WEARABLE SENSOR-BASED GAIT CLASSIFICATION IN IDIOPATHIC TOE WALKING ADOLESCENTS

Filetype[PDF-855.81 KB]


  • English
    • Details Supporting Files You May Also Like

    Details:

    • Alternative Title:
      Biomed Sci Instrum
    • Personal Author:
    • Description:
      Idiopathic toe walking on the balls of the feet is commonly found in children. Many toddlers who are just beginning to walk show signs of toe walking, but when toe walking persists after two years of age, the child's risk of falling increases as well as the risk of other developmental delays. Idiopathic toe-walking is estimated to occur in 7 to 24% of children. In order to address the problem of toe walking and assess improvements due to intervention, one needs to identify heel-toe gait versus toe-toe gait in natural environments of idiopathic toe walkers. The aim of this study was to investigate if learning algorithms utilizing triaxial accelerometers and gyroscopes from wearable sensors could detect and differentiate heel-toe gait versus toe-toe gait. In this study, 5 adolescents (13± 5 years) patients with idiopathic toe walking characteristics wore inertial sensor at L5 - S1 joint. New interventions can be designed for idiopathic toe walking population, but currently, it is a challenge to quantify the efficiency of toe-walking intervention. In recent times, with the advancement of machine learning classification methods and powerful computing, longitudinal data from wearable sensors can be used to accurately classify gait abnormalities. The aim of this study was to investigate machine learning methods to classify toe-toe walking versus heel-toe walking using data from a single inertial sensor. We found that k-means clustering was successful in differentiating toe walking with that of typical walking signals. We found that some of the linear variability based features such as standard deviation, Root Mean Square (RMS), and kurtosis contained most of the variability among the data and could therefore distinguish toe-toe gait versus heeltoe gait through clustering. The k-means cluster provided an 82% accuracy score with a specificity of 83% and sensitivity of 86%. We further utilized Recurrent Convolution Neural Network (RNN) such as Long Short-Term Memory (LSTM). The LSTM model was another classification method that was used to distinguish between toe-toe gait and heel-toe gait. Wearable sensors integrated with machine and deep learning algorithms have the capability to transform current on-going therapy methods and monitor patients longitudinally for their improvement in gait. These novel learning-based techniques could successfully classify toe walking gait and help in estimating the efficacy of the treatment in idiopathic toe walking adolescents.
    • Subjects:
    • Source:
      Biomed Sci Instrum. 55(2):178-185
    • Pubmed ID:
      32214530
    • Pubmed Central ID:
      PMC7094809
    • Document Type:
      Journal Article
    • Funding:
      R01 OH009222/OH/NIOSH CDC HHSUnited States/
    • Collection(s):
    • Main Document Checksum:
    • Download URL:
    • File Type:

    You May Also Like

    Checkout today's featured content at stacks.cdc.gov