A Wearable Device Of Gait Tracking For Parkinson’S Disease Patients

Lee, Chien‐Nan; Fong, Vai Hong; Chu, Yiu-Tong; Cheng, Lin; Chuang, Han-Wei; Lo, C. Y.

doi:10.1109/icmlc.2018.8527011

Cited by 10 publications

(5 citation statements)

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“…Some articles examined the incorporation of EMG and other biopotential sensors into wearable devices to collect data about PD patients’ muscle activity [ 72 , 76 , 102 , 103 , 104 , 105 ]. Several other articles evaluated sensors, including insole force (i.e., pressure) sensors, which generate data on the vertical ground reaction force generated when a subject is walking: using these data, clinicians can assess the subject’s gait, balance, or posture, all of which are critical factors for PD patients [ 106 , 107 , 108 , 109 , 110 , 111 , 112 ]. Figure 11 b shows that the number of articles using wearable devices increased significantly during the period under examination.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease

Deb

Bhat

et al. 2022

Sensors

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Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist’s subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018–2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community’s growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.

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Section: Resultsmentioning

confidence: 99%

A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease

Deb

Bhat

et al. 2022

Sensors

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“…Lee et al proposed a wearable device to evaluate Parkinsonian gait, and the patients' gait is analyzed [54]. Arshad et al developed a new approach for recognizing Human Gait for feature selection [55].…”

Section: Related Workmentioning

confidence: 99%

Multi-objective Harris Hawk Algorithms for the Diagnosis of Parkinson's Disease

Dökeroğlu

2023

Preprint

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This study proposes new binary Harris Hawk Optimization algorithms for diagnosing Parkinson's disease. New exploration and exploitation operators are developed, and a K-Nearest Neighbour classifier that adapts to the given dataset is employed. A parallel version of the algorithm implemented using Message Passing Interfaces is proposed for large problem instances where the fitness evaluation is time-consuming. Comparisons with state-of-the-art genetic, particle swarm, binary bat, cuckoo search, and grey wolf algorithms verified that our proposed algorithms are the best methods in the literature on average in terms of prediction accuracy values. In total, a 32.5% reduction is achieved in the number of features of all datasets. We report new best solutions for the first time in literature. For three datasets out of four, we outperformed state-of-the-art algorithms.

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“…Furthermore, wearable devices are common for the remote management and monitoring of patients after leaving hospital. 18,19 In addition, the devices could improve the quality of treatment and encourage patients to adhere to treatment, helping them to recover earlier as a result. 20 For these reasons, wearable devices are becoming an innovative solution for medical observation, monitoring, and the provision of early medical interventions.…”

Section: Introductionmentioning

confidence: 99%

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

Nguyen

Chan

et al. 2022

Health Informatics J

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In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients’ families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.

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A Wearable Device Of Gait Tracking For Parkinson’S Disease Patients

Cited by 10 publications

References 0 publications

A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease

A Systematic Survey of Research Trends in Technology Usage for Parkinson’s Disease

Multi-objective Harris Hawk Algorithms for the Diagnosis of Parkinson's Disease

Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data

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