Input representations and classification strategies for automated human gait analysis

Slijepčević, Djordje; Zeppelzauer, Matthias; Schwab, Caterine; Raberger, Anna-Maria; Breiteneder, Christian; Horsak, Brian

doi:10.1016/j.gaitpost.2019.10.021

Cited by 16 publications

(16 citation statements)

References 20 publications

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“…Based on the presented results, using linear SVMs for the classification of gait data can be recommended. Furthermore, in line with recent research (Slijepcevic et al, 2019), a majority vote could possibly provide an even better classification. However, it should be noted that only a small selection of possible classifiers and corresponding architectures as well as a grid search procedures were examined in this analysis.…”

Section: Machine-learning Classifiersupporting

confidence: 74%

“…Different methods have been used for each stage and there is no clear consensus on how to proceed in each of these stages. This is particularly the case for the preprocessing stages of the measured raw data before the classification stage, where there are hardly any recommendations, standard procedures or systematic comparisons of these different preprocessing stages and their impact on the classification accuracy (Slijepcevic et al, 2019). The following six steps, for example, can be derived from the preprocessing stage: (1) Ground reaction force (GRF) filtering, (2) time derivative, (3) time normalization, (4) data reduction, (5) weight normalization, and (6) data scaling.…”

Section: Introductionmentioning

confidence: 99%

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Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning

Burdack

Horst

Giesselbach

et al. 2020

Front. Bioeng. Biotechnol.

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Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniques seems to be particularly suitable to generate classification models. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution e.g. in increasing the classification accuracy. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification accuracy. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification accuracy of gait patterns.A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy subjects performed 6 sessions of 15 gait trials for one day. For each trial, two force plates recorded the three-dimensional ground reaction forces (GRF). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each individual preprocessing step were analyzed and compared with respect to their prediction accuracy in a six-session classification using Support Vector Machines, Random Forest Classifiers and Multi-Layer Perceptrons. 1 arXiv:1911.04335v1 [cs.LG] 11 Nov 2019 Burdack et al. Data Preprocessing in Gait ClassificationThe results indicate that filtering GRF data and a supervised data reduction (e.g., using Principal Components Analysis) lead to increased prediction accuracies of the machine-learning classifiers. Interestingly, the weight normalization and the number of data points (above a certain minimum) in the time normalization does not have a substantial effect. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.

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Section: Machine-learning Classifiersupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning

Burdack

Horst

Giesselbach

et al. 2020

Front. Bioeng. Biotechnol.

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“…Our main research goal within this collaboration was to develop automatic classification algorithms which support clinicians during data inspection and interpretation. To this end, we have developed a machine learning framework for gait classification and have performed comprehensive experiments [13][14][15][16] . One conclusion of our experiments is that the performance of automatic classification methods strongly depends on the amount of available training data.…”

Section: Background and Summarymentioning

confidence: 99%

GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait

et al. 2020

Self Cite

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The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging. Machine learning approaches seem to be promising tools to support clinicians in identifying and categorizing specific gait patterns. However, the quality of such approaches strongly depends on the amount of available annotated data to train the underlying models. therefore, we present GaitRec, a comprehensive and completely annotated large-scale dataset containing bilateral GRF walking trials of 2,084 patients with various musculoskeletal impairments and data from 211 healthy controls. The dataset comprises data of patients after joint replacement, fractures, ligament ruptures, and related disorders at the hip, knee, ankle or calcaneus during their entire stay(s) at a rehabilitation center. The data sum up to a total of 75,732 bilateral walking trials and enable researchers to classify gait patterns at a large-scale as well as to analyze the entire recovery process of patients.

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Section: Introductionmentioning

confidence: 92%

“…The input representation plays an important role in classification accuracy [ 12 ], as well as in interpretability, clinical relevancy, and comparability with previous research. Different feature extraction approaches can be found in the literature: Many studies have used simple descriptive statistics of the gait waveforms such as peak values, range of motion, or respective side differences [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty

Dindorf

Teufl

Taetz

et al. 2020

Sensors

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Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown promising results. The current study examined the influence of different input representations on a trained model’s accuracy, interpretability, as well as clinical relevancy using XAI methods. The gait of 27 healthy subjects and 20 subjects after total hip arthroplasty (THA) was recorded with an inertial measurement unit (IMU)-based system. Three different input representations were used for classification. Local Interpretable Model-Agnostic Explanations (LIME) was used for model interpretation. The best accuracy was achieved with automatically extracted features (mean accuracy Macc = 100%), followed by features based on simple descriptive statistics (Macc = 97.38%) and waveform data (Macc = 95.88%). Globally seen, sagittal movement of the hip, knee, and pelvis as well as transversal movement of the ankle were especially important for this specific classification task. The current work shows that the type of input representation crucially determines interpretability as well as clinical relevance. A combined approach using different forms of representations seems advantageous. The results might assist physicians and therapists finding and addressing individual pathologic gait patterns.

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Input representations and classification strategies for automated human gait analysis

Cited by 16 publications

References 20 publications

Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning

Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning

GaitRec, a large-scale ground reaction force dataset of healthy and impaired gait

Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty

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