45
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking.

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The aim of this study was to investigate if a machine learning algorithm utilizing triaxial accelerometer, gyroscope, and magnetometer data from an inertial motion unit (IMU) could detect surface- and age-related differences in walking. Seventeen older (71.5 ± 4.2 years) and eighteen young (27.0 ± 4.7 years) healthy adults walked over flat and uneven brick surfaces wearing an inertial measurement unit (IMU) over the L5 vertebra. IMU data were binned into smaller data segments using 4-s sliding windows with 1-s step lengths. Ninety percent of the data were used as training inputs and the remaining ten percent were saved for testing. A deep learning network with long short-term memory units was used for training (fully supervised), prediction, and implementation. Four models were trained using the following inputs: all nine channels from every sensor in the IMU (fully trained model), accelerometer signals alone, gyroscope signals alone, and magnetometer signals alone. The fully trained models for surface and age outperformed all other models (area under the receiver operator curve, AUC = 0.97 and 0.96, respectively; p ≤ .045). The fully trained models for surface and age had high accuracy (96.3, 94.7%), precision (96.4, 95.2%), recall (96.3, 94.7%), and f1-score (96.3, 94.6%). These results demonstrate that processing the signals of a single IMU device with machine-learning algorithms enables the detection of surface conditions and age-group status from an individual's walking behavior which, with further learning, may be utilized to facilitate identifying and intervening on fall risk.

          Related collections

          Author and article information

          Journal
          J Biomech
          Journal of biomechanics
          Elsevier BV
          1873-2380
          0021-9290
          Apr 11 2018
          : 71
          Affiliations
          [1 ] Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States; Liberty Mutual Research Institute for Safety, United States. Electronic address: boyihu@hsph.harvard.edu.
          [2 ] Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States; Liberty Mutual Research Institute for Safety, United States.
          [3 ] Liberty Mutual Research Institute for Safety, United States.
          [4 ] Department of Environmental Health, Harvard T.H. Chan School of Public Health, United States; Bouvé College of Health Sciences, Northeastern University, United States.
          Article
          S0021-9290(18)30019-8
          10.1016/j.jbiomech.2018.01.005
          29452755
          e2ab55d8-16bd-43e7-94c2-837a3e3cd3cd
          History

          Ageing,Neural networks,Irregular surface,Inertial measurement units,Gait,Falls

          Comments

          Comment on this article