Wearable computing of Freezing of Gait in Parkinson's disease: A survey

Sun, Minglong; Watson, Amanda; Zhou, Gang

doi:10.1016/j.smhl.2020.100143

Cited by 4 publications

(3 citation statements)

References 78 publications

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“…For example, there are some review studies in the current literature that summarize different types of sensors and commercially available devices, which are deployed to monitor PD patients with respect to various aspects and motor symptoms, without however emphasizing predictive ML models [20][21][22][28][29][30][31]. There are also others that focus only on one problem, such as rigidity quantification [32], impaired gait analysis [33][34][35], freezing of gait episodes and potential falls detection [36][37][38], as well as computer-aided PD diagnosis [39,40]. The current systematic review intends to fill all these gaps, by presenting a more holistic approach, examining both the sensorial and the algorithmic side of strictly IoT-and ML-based approaches.…”

Section: Aim Of the Current Systematic Reviewmentioning

confidence: 99%

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Giannakopoulou

Roussaki

Demestichas

2022

Sensors

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Parkinson’s disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients’ quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson’s disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson’s disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson’s disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.

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Section: Aim Of the Current Systematic Reviewmentioning

confidence: 99%

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Giannakopoulou

Roussaki

Demestichas

2022

Sensors

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“…FOG refers to the sudden and brief episode of inability to produce effective forward stepping (Sun et al, 2020 ), which is one of the most disabling symptoms of PD patients at advanced stage (Mirelman et al, 2019 ). Therefore, PD patients with FOG are easily suffered from falling and fall-related injuries (Creaby and Cole, 2018 ).…”

Section: Toward Automatic Recognition In Pd Based On Gait Datamentioning

confidence: 99%

“…In addition, non-movement symptoms (e.g., cognition impairment, depression, anxiety) also contribute to abnormal gait patterns (Deligianni et al, 2019 ). Supported by advanced sensing technologies, gait analysis can be performed from the clinical lab studies to daily living environments (Chen et al, 2016 ; Kour and Arora, 2019 ; Sun et al, 2020 ), providing opportunities for gait-based PD detection, monitoring, and intervention.…”

Section: Introductionmentioning

confidence: 99%

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Guo

Yang

Liu

et al. 2022

Front. Aging Neurosci.

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Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.

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Toward a Multi-Modal Brain-Body Assessment in Parkinson's Disease: A Systematic Review in fNIRS

Sousani,

Rojas,

Preston

et al. 2023

IEEE J. Biomed. Health Inform.

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Parkinson's disease (PD) causes impairments in cor-1 tical structures leading to motor and cognitive symptoms. While 2 common disease management and treatment strategies mainly 3 depend on the subjective assessment of clinical scales and pa-4 tients' diaries, research in recent years has focused on advances 5 in automatic and objective tools to help with diagnosing PD and 6 determining its severity. Due to the link between brain structure 7 deficits and physical symptoms in PD, objective brain activity and 8 body motion assessment of patients have been studied in the liter-9 ature. This study aimed to explore the relationship between brain 10 activity and body motion measures of people with PD to look at the 11 feasibility of diagnosis or assessment of PD using these measures. 12 In this study, we summarised the findings of 24 selected papers 13 from the complete literature review using the Scopus database. Se-14 lected studies used both brain activity recording using functional 15 near-infrared spectroscopy (fNIRS) and motion assessment using 16 sensors for people with PD in their experiments. Results include: 17 1) The most common study protocol is a combination of single 18 tasks. 2) Prefrontal cortex is mostly studied region of interest in the 19 literature. 3) Oxygenated haemoglobin (HbO 2 ) concentration is the 20 predominant metric utilised in fNIRS, compared to deoxygenated 21 haemoglobin (HHb). 4) Motion assessment in people with PD is 22 mostly done with inertial measurement units (IMUs) and electronic 23 walkway. 5) The relationship between brain activity and body mo-24 tion measures is an important factor that has been neglected in the 25 literature.26

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Wearable computing of Freezing of Gait in Parkinson's disease: A survey

Cited by 4 publications

References 78 publications

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Toward a Multi-Modal Brain-Body Assessment in Parkinson's Disease: A Systematic Review in fNIRS

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