Mobile Stride Length Estimation With Deep Convolutional Neural Networks
- 1 March 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Biomedical and Health Informatics
- Vol. 22 (2), 354-362
- https://doi.org/10.1109/JBHI.2017.2679486
Abstract
Objective: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. Methods: We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions. Results: Even though best results are achieved with strides defined frommidstance to midstance with average accuracy and precision of 0.01 +/- 5.37 cm, performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by 3.0 cm (36%). Conclusion: Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. Significance: With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.Other Versions
Funding Information
- University of Erlangen-Nürnberg
- Emerging Fields Initiative
This publication has 33 references indexed in Scilit:
- Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's DiseasePLOS ONE, 2013
- Quantitative Normative Gait Data in a Large Cohort of Ambulatory Persons with Parkinson’s DiseasePLOS ONE, 2012
- Which measures of physical function and motor impairment best predict quality of life in Parkinson’s disease?Parkinsonism & Related Disorders, 2011
- Estimation of stride length in level walking using an inertial measurement unit attached to the foot: A validation of the zero velocity assumption during stanceJournal of Biomechanics, 2011
- Normative spatiotemporal gait parameters in older adultsGait & Posture, 2011
- Gait analysis in multiple sclerosis: Characterization of temporal–spatial parameters using GAITRite functional ambulation systemGait & Posture, 2009
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- Three dimensional inertial sensing of foot movements for automatic tuning of a two-channel implantable drop-foot stimulatorMedical Engineering & Physics, 2003
- Estimation of speed and incline of walking using neural networkIEEE Transactions on Instrumentation and Measurement, 1995
- ParkinsonismNeurology, 1967