A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson's Disease Gait Dysfunctions—A Deep Learning Approach

Kaur, Rachneet; Motl, Robert W.; Sowers, Richard B.; Hernandez, Manuel E.

doi:10.1109/jbhi.2022.3208077

Cited by 33 publications

(20 citation statements)

References 37 publications

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“…Similar features could be shown for team behavior in volleyball [32], [33], soccer [34], [35], or basketball [36], [37]. Typical parameters investigated are biomechanical data from video recordings [38], force plates [39]- [41], pressure insoles [42], [43], EMG [44], [45], and brain signals [46], [47]. Besides these, wearable sensors are becoming increasingly popular [48].…”

Section: Introductionmentioning

confidence: 80%

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

Burdack¹,

Giesselbach²,

Simak³

et al. 2023

Preprint

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<p>In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation–dependent fine structures within it are already identified. Methodologically, however, only signals of same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. 17 healthy adults (35.8 ± 11.1 years, 8 females, 9 males) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional CycleGAN, conditioned on the participant’s class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, it was tested whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement. The classification F1–score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross–movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross–movement analysis and the artificial generation of larger amounts of data.</p>

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Section: Introductionmentioning

confidence: 80%

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

Burdack¹,

Giesselbach²,

Simak³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These authors validated a DL-based approach in a cohort of ND that was useful to extract data about stride-specific gait parameters from IMUs. Additionally, Kaur et al [76] developed a DL approach to classify PD and MS patients based on gait dynamics, extracting 3D body frameworks from the recorded gait videos. This approach could be useful in the field of telemedicine as a potential in-home gait monitoring tool since alterations in gait function can be a biomarker of pathology progression.…”

Section: Future Directions Of Gait Analysismentioning

confidence: 99%

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

Bonanno

Nunzio²,

Quartarone

et al. 2023

Bioengineering

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When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients’ quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.

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“…Next, we compared the following 4 convolutional DL models (see Sections V-A1 to V-A4) and 3 recurrent models (Sections V-A5 to V-A7); for these algorithms, a sequence of 5 consecutive strides was used directly as the model's input. These algorithms have been previously used for vision-based gait analysis in our past work [35].…”

Section: A Classification Modelsmentioning

confidence: 99%

Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait

Kaur

Levy

Motl

et al. 2023

IEEE Trans. Biomed. Eng.

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Objective: Multiple sclerosis (MS) is a chronic neurological condition of the central nervous system leading to various physical, mental and psychiatric complexities. Mobility limitations are amongst the most frequent and early markers of MS. We evaluated the effectiveness of a DeepMS2G (deep learning (DL) for MS differentiation using multi-stride dynamics in gait) framework, which is a DL-based methodology to classify multi-stride sequences of persons with MS (PwMS) from healthy controls (HC), in order to generalize over newer walking tasks and subjects. Methods: We collected single-task Walking and dualtask Walking-while-Talking gait data using an instrumented treadmill from a balanced collection of 20 HC and 20 PwMS. We utilized domain knowledge-based spatiotemporal and kinetic gait features along with two normalization schemes, namely standard size-based and multiple regression normalization strategies. To differentiate between multi-stride sequences of HC and PwMS, we compared 16 traditional machine learning and DL algorithms. Further, we studied the interpretability of our highest-performing models; and discussed the association between the lower extremity function of participants and our model predictions. Results: We observed that residual neural network (ResNet) based models with regression-based normalization were the top performers across both task and subject generalization classification designs. Considering regression-based normalization, a multi-scale ResNet attained a subject classification accuracy and F 1 -score of 1.0 when generalizing from single-task Walking to dual-task Walking-while-Talking; and a ResNet resulted in the top subject-wise accuracy and F 1 of 0.83 and 0.81 (resp.), when generalizing over Manuscript

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A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson's Disease Gait Dysfunctions—A Deep Learning Approach

Cited by 33 publications

References 37 publications

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait

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