Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking
Introduction
Falling during walking is a significant risk to people’s health and safety across the age span (Verma et al., 2016). In the United States, falls are the leading cause of death due to unintentional injury for older adults (56.0% in 2014) (Centers for Disease Control and Prevention, 2017). Falls in community-dwelling older adults can occur both inside and outside the home. Some studies report a greater prevalence of falls in outdoor settings (Bergland et al., 1998, Li et al., 2006, Oloughlin et al., 1994), which are predominantly associated with hazards such as uneven surfaces that are present in the built-environment (Li et al., 2006), either by design or degradation of walking infrastructure.
Previous work has shown that gait adaptations occur on uneven, compared to flat, surfaces and that older adults may not be as able as young adults to produce the required adaptive strategies to improve stability and decrease fall risk (Marigold and Patla, 2008, Menz et al., 2003, Thies et al., 2005, Zurales et al., 2016). Therefore, investigating gait biomechanics in more challenging situations like uneven as compared to flat surfaces appears to be warranted to better understand fall risk in outdoor environments.
Moreover, with the increased attention on the surveillance of physical activity for enhanced health promotion, wearable sensors play a growing role in supporting identified goals (Fulton et al., 2016). The feasibility and reliability of using wearable IMU sensors for human motion research have been confirmed from previous literature (Moe-Nilssen, 1998, Patel et al., 2012). Research and development of wearables that can detect surface conditions associated with incidents of falling are needed in order to better monitor, classify, and interpret human movement. Analytical tools are also needed to better understand circumstances of falls and the efficacy of fall prevention interventions (Pannurat et al., 2014). Previous work has shown that metrics of movement quality derived from the acceleration signal of a trunk-mounted IMU, such as step and stride regularity, can accurately detect surface differences during walking (Dixon et al., 2017) and running (Schutte et al., 2015). One primary limitation of such methods is that a significant amount of expertise and human input are needed to process the raw data and extract meaningful features for analyses. Therefore, it may be advantageous to develop advanced machine learning approaches that do not require a priori derivation on outcome measures.
Machine learning methods have been applied to a range of populations and technologies in the context of gait research to classify adults into fall risk categories, individuals with and without health conditions, and to improve estimation of minimum toe clearance. Deep learning networks (neural networks with multiple layers) are a category of machine-learning algorithms that possess multiple, non-linear, processing layers. This method has outperformed other machine-learning algorithms in many areas such as computer vision (Krizhevsky et al., 2012), audio classification (Lee et al., 2008) and even achievement of human expert level of performance (Silver et al., 2016) if given enough data and trained properly. Therefore, application of the deep learning networks warrants investigation in other contexts such as biomechanics and human motion analysis. Previous literature has used multiple machine learning algorithms for on-body sensor-based activity recognition such as Support Vector Machine (SVM) and Decision Trees (Chavarriaga et al., 2013). In that paper, machine learning algorithms showed reasonably good prediction performance (up to 0.89 f-score and 0.90 area under the curve). However, it remains unknown if deep learning networks can contribute to the human motion recognition task. In addition, it remains unclear whether such deep learning methods, coupled with simple IMU setups, can detect relatively subtle gait differences such as those induced by walking surface changes in different age groups.
Therefore, the primary aim of this study was to investigate if a deep learning network with long short term memory (LSTM) units relying on IMU input data could be used to detect surface- and age-related effects on walking. The secondary aim of this study was to determine which combination of sensors (accelerometer, gyroscope, or magnetic) provides the best classification performance. We hypothesized that (1) our deep learning network with LSTM algorithms could correctly classify trials according to surface and age and (2) the fusion of all sensor sources would result in the greatest classification performance.
Section snippets
Participants
Forty-six healthy community-dwelling young (18–35 years) and older (65+ years) adults volunteered for this study. Eleven (two young and nine old) participants were excluded after a medical screen identified neurological disorders, vertigo, dizziness, lower-limb musculoskeletal abnormalities (joint replacement, chronic or acute pain, fractures within the last two years), diabetes, obesity, or poor age-normative balance scores (O'Hoski et al., 2014). Thus, seventeen older (71.5 ± 4.2 years,
Walking surface model
The model trained with all nine channels as inputs elicited the best performance in predicting surface type with an accuracy of 96.3% (158/164 trials correctly classified), precision and recall of 96.4% and 96.3%, respectively and an f1-score of 96.3%. ROC analysis indicated that the fully trained model achieved an AUC of 0.97.
Models using only 3 accelerometer, 3 gyroscope, or 3 magnetic channels achieved AUCs of 0.92, 0.80, and 0.71, respectively (Fig. 3a). The confusion matrix of the
Discussion
This study investigated the use of machine learning algorithms to detect surface- and age-related differences in walking using signals from a single trunk-mounted IMU. In agreement with our first hypothesis, the results show that the machine learning algorithms discriminated between flat and uneven surfaces as well as between young and older adults at high levels of accuracy, precision, recall, and f1-score. Moreover, in agreement with our second hypothesis, utilizing all IMU sensors
Acknowledgements
The second author of this work was supported by the Fonds de Recherche Québec – Santé post-doctoral training award (Dixon # 33358). The authors would like to thank Amanda Rivard, Niall O’Brian, and Jacob Banks for help with the collection and initial processing of the data.
Conflict of interest
This is to declare that we had no financial or personal relationships with other people or organizations that could inappropriately influence (bias) our work submitted herein.
References (42)
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Pattern Recogn.
(1997)- et al.
The opportunity challenge: a benchmark database for on-body sensor-based activity recognition
Pattern Recogn. Lett.
(2013) - et al.
Age-related changes in gait for multi-surface terrain
Gait Posture
(2008) Test-retest reliability of trunk accelerometry during standing and walking
Arch. Phys. Med. Rehabil.
(1998)- et al.
Effects of surface irregularity and lighting on step variability during gait: a study in healthy young and older women
Gait Post.
(2005) - Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M.,...
- et al.
Investigating the effect of training–testing data stratification on the performance of soft computing techniques: an experimental study
J. Exp. Theor. Artif. Intell
(2016) - et al.
Falls reported among elderly Norwegians living at home
Physiother Res Int
(1998) - et al.
Age-associated changes in head jerk while walking reveal altered dynamic stability in older people
Exp. Brain Res.
(2014) - et al.
Deep learning for detecting freezing of gait episodes in Parkinson’s disease based on accelerometers. IWANN 2017, Part II
LNCS
(2017)
A tutorial on the cross-entropy method
Ann. Oper. Res
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
Biometrics
Gait adaptations of older adults on an uneven brick surface can be predicted by age-related physiological changes in strength
Gait Post
Strategic priorities for physical activity surveillance in the United States
Med. Sci. Sports Exerc.
Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit
Nature
Long short-term memory
Neural Comput.
Plus One
ImageNet Classification with Deep Convolutional Neural Networks
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