Freezing-of-Gait Detection Using Wearable-Sensor Technology and Neural-Network Classifier

Tahafchi, Parisa; Judy, Jack W.

doi:10.1109/sensors43011.2019.8956548

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…However, CNN and transfer learning techniques were not limited to imaging data; they also learn complex features from voices and signal data [ 29 ]. Numerous studies used the biomedical voice ( n = 21) [ 4 , 6 , 22 , 23 , 29 , 33 , 44 , 48 , 50 , 52 , 53 , 55 , 60 , 61 , 73 , 74 , 84 , 93 , 100 , 104 , 105 ] and biometric signal ( n = 14) [ 26 , 31 , 34 , 36 , 45 , 46 , 57 , 62 , 64 , 65 , 68 , 89 , 96 , 98 ]; a few of the included studies used EEG and EMG signals ( n = 5) [ 32 , 39 , 51 , 83 , 85 ].…”

Section: Resultsmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

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Section: Resultsmentioning

confidence: 99%

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

et al. 2021

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“…There is evidence that individuals with Parkinson's disease who frequently present FoG follow a pattern of accelerated pacing prior to "freezing" [27]. Several studies show that, among a number of gait metrics, step duration, step length, double support time and swing time might be used to create models that predict episodes of FoG [28].…”

Section: A Analysis Of Gaitmentioning

confidence: 99%

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Skaramagkas

Pentari

Kefalopoulou

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Parkinson's Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, have been extensively embraced with promising outcomes. This study dispenses an exhaustive review between 2016 and January 2023 on deep learning techniques used in the prognosis and evolution of symptoms and characteristics of the disease based on gait, upper limb movement, speech and facial expression-related information as well as the fusion of more than one of the aforementioned modalities. The search resulted in the selection of 87 original research publications, of which we have summarized the relevant information regarding the utilized learning and development process, demographic information, primary outcomes, and sensory equipment related information. Various deep learning algorithms and frameworks have attained state-of-theart performance in many PD-related tasks by outperforming conventional machine learning approaches, according to the research reviewed. In the meanwhile, we identify significant drawbacks in the existing research, including a lack of data availability and interpretability of models. The fast advancements in deep learning and the rise in accessible data provide the opportunity to address these difficulties in the near future and for the broad application of this technology in clinical settings.

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“…Transient features are trends (increasing and decreasing), the magnitude of change, and so on [123]. Medical features, especially found in medical applications, are defined by physicians such as freezing index properties and exercise intensity [99], [124]. Body model-based features are features based on motion primitives defined in papers [54], [125], [126].…”

Section: E Feature Extractionmentioning

confidence: 99%

“…Ground truth, a concept related to the training phase that leads to the production of labeled data, is not discussed in all papers, and we ignore it and only get acquainted with this concept. Generally, papers that deal with hyperparameter tuning or optimization parameter tuning need validation [106], [124], and papers that do not need this part or use default hyperparameters will only run the training and testing part. Because validation is not an essential part, its methods will not be covered much, but validation methods are like evaluation methods.…”

Section: J Evaluationmentioning

confidence: 99%

Statistical Database of Human Motion Recognition Using Wearable IoT—A Review

et al. 2023

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Wearable sensors and the Internet of Things (IoT) will be two buzzwords that will be heard commonly in the coming decades. The combination of these two technologies soon will create a great revolution in applications that require motion recognition, such as health care, sports, and entertainment. The development of technology has made wearable sensors one of the most basic tools for human motion analysis. We believe that IoT is the most powerful complement to the use of wearable sensors in the analysis of human body motion. Using wearable IoT, all necessary human data will be collected and delivered via the Internet to the experts who can make accurate decisions about the type of activity, falling situations, freezing of gait (fog), and so on. In this article, the human motion analysis is presented in a chart and is divided into two parts: movement measurement and movement classification. However, this article focuses on movement classification that includes three subsections, gait analysis (GA), gesture recognition (GR), and human activity recognition (HAR), and is closely related to human motion recognition. In this article, our goal is to first acquaint the reader with the important steps required to classify the movement of the human body by wearable sensors and then by using tables to determine the most used algorithms and methods for each step. After briefly reviewing IoT concepts, directions for further research will be provided.

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Freezing-of-Gait Detection Using Wearable-Sensor Technology and Neural-Network Classifier

Cited by 9 publications

References 10 publications

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Statistical Database of Human Motion Recognition Using Wearable IoT—A Review

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