Increased stride-to-stride variability during walking characterizes gait instability and predicts falling in older adults. Walking while performing cognitive tasks (dual task walking) is also associated with increased risk of falling. The purpose of the study was to examine whether gait velocity and stride-to-stride variability in gait velocity differ in older adults compared with middle-aged and younger adults during normal and dual task walking conditions. Sixty older ( n =20, mean age=81 years), middle-aged ( n =20, mean age=48 years), and young adults ( n =20, mean age=25 years) participated in the study. Gait parameters were quantified with GAITRite ® instrumentation. In the dual task condition, participants spelled five-letter words in reverse while walking across the walkway. Across groups, gait velocity was slower ( p p =0.001) in dual task walking. Older subjects walked more slowly than did middle-aged and younger subjects and the difference in gait velocity was greatest in the dual task condition ( p p

Age-related differences in spatiotemporal markers of gait stability during dual task walking.

A faculty of medicine.

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

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Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.

Cooperative machine learning has many applications, such as data annotation, where an initial model trained with partially labeled data is used to predict labels for unseen data continuously. Predicted labels with a low confidence value are manually revised to allow the model to be retrained with the predicted and revised data. In this paper, we propose an alternative to this approach: an initial training process called Deep Unsupervised Active Learning. Using the proposed training scheme, a classification model can incrementally acquire new knowledge during the testing phase without manual guidance or correction of decision making. The training process consists of two stages: the first stage of supervised training using a classification model, and an unsupervised active learning stage during the test phase. The labels predicted during the test phase, with high confidence, are continuously used to extend the knowledge base of the model. To optimize the proposed method, the model must have a high initial recognition rate. To this end, we exploited the Visual Geometric Group (VGG16) pre-trained model applied to three datasets: Mathematical Image Analysis (AMI), University of Science and Technology Beijing (USTB2), and Annotated Web Ears (AWE). This approach achieved impressive performance that shows a significant improvement in the recognition rate of the USTB2 dataset by coloring its images using a Generative Adversarial Network (GAN). The obtained performances are interesting compared to the current methods: the recognition rates are 100.00%, 98.33%, and 51.25% for the USTB2, AMI, and AWE datasets, respectively.

Ear Recognition Based on Deep Unsupervised Active Learning

Deep ensemble learning approach for lower extremity activities recognition using wearable sensors

The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

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Deep Learning for Monitoring of Human Gait: A Review

Deep learning models are used to process and fuse raw data of gait-induced ground reaction force (GRF) for Parkinson’s disease (PD) patients and healthy subjects with the aim to categorize PD severity. This is achieved by learning automatically, end-to-end, the spatiotemporal GRF signals, resulting in an effective PD severity classification with mean performance F1-score of 95.5% and F1-score standard errors of 0.28%. Layer-wise relevance propagation (LRP) is used to interpret the models’ output and provide insight into which features in the spatiotemporal gait GRF signals are most significant for the models’ predictions. This allows their assignment to gait events, implying that while for the classification of healthy gait the heel strike and body balance are the most indicative gait elements, foot landing and body balance are those most affected in advanced stages of PD. The proposed models are resilient to noise and are computationally efficient for processing and classification of large longitudinal GRF signal datasets, therefore they can be useful for detecting deterioration in the postural balance and rating PD severity.

Gait Spatiotemporal Signal Analysis for Parkinson’s Disease Detection and Severity Rating

Deep learning methods are proposed to process and fuse raw spatiotemporal ground reaction forces (GRF) to accurately categorize gait pattern. These methods are based on convolutional neural network and long short-term memory networks architectures to learn spatiotemporal features, automatically end-to-end from raw GRF sensor signals. In a case study on Parkinson's disease (PD) data, spatiotemporal signals of gait for PD patient and healthy subjects are processed and classified, resulting an effective gait pattern classification with a precision performance of 96%. Deep learning considerably achieved better classification results, compared to the shallow learning methods with the handcrafted features. This implies that for the purpose of automatic decision-making, it is beneficial to utilize deep learning methods to analyse GRF. This insight is portable across a range of industrial tasks that involve complex spatiotemporal GRF signals classification. The proposed models are computationally efficient and able to achieve high classification precision from a large set of GRF signals.

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Deep Learning for Ground Reaction Force Data Analysis: Application to Wide-Area Floor Sensing

Human motion is an important spatio-temporal pattern as it can be a powerful indicator of human well-being and identity. In particular, human gait offers a unique motion pattern of an individual. Gait refers to the study of locomotion in both humans and animals. It involves the coordination of several parts of the human body: the brain, the spinal cord, the nerves, muscles, bones, and also joints. Gait analysis has been studied for a variety of applications including healthcare, biometrics, sports, and many others. Until recently, the analysis has been done mainly by human observation, using parameters and features established in existing practice and therefore limited by the nature of measurements captured by the gait sensing modalities. In this chapter, we reviewed key conceptual and algorithmic facets of deep learning applied to gait analysis in two important contexts: security and healthcare.

Deep learning in gait analysis for security and healthcare

In a case study of gait classification from floor and ambulatory sensors, we compare results with data from each modality. The automatic extraction of features is achieved by Principle Component Analysis and Canonical Correlation Analysis, the latter performing better even with a reduced number of components used. Non-linear classifiers are most efficient for fused features. With a Kernel Support Vector Machine around 94% accuracy is demonstrated, improving over the 87% and 79% accuracies obtained with separate floor and ambulatory sensor data, respectively.

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Abdullah S. Alharthi

Papers

Deep Learning for Monitoring of Human Gait: A Review

Gait Spatiotemporal Signal Analysis for Parkinson’s Disease Detection and Severity Rating

Deep Learning for Ground Reaction Force Data Analysis: Application to Wide-Area Floor Sensing

Deep learning in gait analysis for security and healthcare

Multi-modality fusion of floor and ambulatory sensors for gait classification