Automatic gait analysis during steady and unsteady walking using a smartphone

Sher, Arshad; Langford, David; Dogger, Einar; Monaghan, Dan; Lunn, Luke Ian; Schroeder, Maresa; Hamidinekoo, Azam; Arkesteijn, Marco; Shen, Qing; Zwiggelaar, Reyer; Tench, Helen; Villagra, Federico; Akanyeti, Otar

doi:10.36227/techrxiv.16586780.v1

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…If the distribution of the estimated step times does not match the expected distribution, the algorithm changes the peak detection threshold and reanalyses the data (repeats Phase 2, 3 and 4). More details about the gait characterization module can be found in (Sher et al, 2021).…”

Section: Part 2: Gait Characterizationmentioning

confidence: 99%

Towards personalized environment‐aware outdoor gait analysis using a smartphone

2022

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Automatic gait analysis in free‐living environments using inertial sensors requires individualized approach as local acceleration and velocity profiles vary with the walker and the topological properties of the environment (e.g., walking in the forest vs. walking on sand). Here, we propose a smartphone‐based gait assessment architecture which consists of two data processing modules. The first module employs a set of personalized classifiers for automatic recognition of the walking environment. The second module provides accurate step time estimates by selecting the optimal filtering frequency tailored to the predicted environment. The performance of the architecture was evaluated using experimental data collected from 10 participants walking in 10 different conditions typically encountered during daily living. Compared with ground truth data, the architecture successfully recognized the walking environments; the percentage of correctly classified instances was above 92%. It also estimated step time with high accuracy; the mean absolute error was less than 10 ms, outperforming or at the very least matching the performance levels achieved in controlled laboratory trials (indoor flat surface walking). Compared with using one filtering frequency for all environments, using optimal frequency tailored to each environment reduced step time estimation error by more than 39%. To the best of our knowledge, this is the first study which successfully demonstrates that parameter tuning can improve gait characterization in outdoor environments. However, further research using a larger data set (including more participants with varying demographics and degree of impairment) is needed to confirm this result. Our findings highlight the importance of environment‐aware gait analysis, and lay the groundwork for a smartphone‐based technology that can be used in the community.

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Section: Part 2: Gait Characterizationmentioning

confidence: 99%

Towards personalized environment‐aware outdoor gait analysis using a smartphone

2022

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Towards Fuzzy Context-Aware Automatic Gait Assessments in Free-Living Environments

Bunker

Sher

Akpokodje

et al. 2021

Advances in Intelligent Systems and Computing

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How people walk in natural conditions often reveals key insights into health, quality of life and independence. However, topological properties of the environment in which walking takes place should be taken into account for accurate and reliable gait assessments. Here, to begin to tackle this problem a novel smartphone-based gait assessment framework is introduced, and the implementation and performance analysis of two of its data processing modules are described. The first module employs a fuzzy rough nearest neighbour classifier for automatic labelling of walking environments. Tested on a publicly available dataset, the fuzzy classifier reached up to 82% classification accuracy outperforming other state-of-the-art classical machine learning methods and matching deep neural networks reported in the literature. The second module employs a novel algorithm to detect steps and extract temporal gait parameters (such as stride time, double support time and step variability) from heel strike and toe off time points. Tested on a new dataset collected for this study, the algorithm successfully detected more than 97% of the steps and estimated gait parameters with high accuracy and precision matching with the performance levels achieved in controlled laboratory trials.

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