Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease

Aghanavesi, Somayeh; Fleyeh, Hasan; Dougherty, Mark

doi:10.1155/2020/3265795

Cited by 3 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Normalization techniques are also used to decrease the influence of the morphology of each individual [55]. This approach is used, for example, in [56], where DTW is used to compare how patients performed a movement before, during, and after treatment.…”

mentioning

confidence: 99%

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept

et al. 2023

View full text Add to dashboard Cite

The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson’s disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.

show abstract

mentioning

confidence: 99%

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept

et al. 2023

View full text Add to dashboard Cite

show abstract

High-altitude tree growth responses to climate change across the Hindu Kush Himalaya

Zheng

Gaire

Shi

2021

Journal of Plant Ecology

View full text Add to dashboard Cite

Aims Rapid warming at high altitudes may lead to a higher sensitivity in tree growth to temperature. The key factors constraining tree radial growth and to what extent regional tree growth has suffered from climatic changes are unclear. Methods Tree-ring width data were collected from 73 sites across the Hindu Kush Himalaya (HKH), including three dominant genera (Abies, Juniperus, and Picea) at high altitudes over 3000 m. Dynamic Time Warping (DTW) was introduced to develop subregional chronologies by considering the synchrony of annual tree growth among different sites. We quantified the contribution of the climate variables, and analyzed the spatio-temporal variation of the growth-climate relationship. Important Findings The site chronologies were grouped into three clusters, corresponding to the three distinct bioclimatic zones, i.e., the western HKH, central-eastern HKH, and southeastern Tibetan Plateau (TP). Tree growth was positively correlated to winter and spring precipitation in the drier western HKH, and to winter temperature and spring precipitation in the humid southeastern TP. Tree growth was markedly constrained by the minimum temperature, especially in winter, with its importance increasing from the west towards the east. As shown by moving correlation analysis, the signal of winter temperature in tree growth was weakened in the western and central-eastern HKH, while it was enhanced in the southeastern TP following rapid warming since the 1980s. Our results highlight that continuous warming may cause forest recession due to warming-induced moisture deficit in the western HKH, but forest expansion in the southeastern TP.

show abstract

Towards Sustainable Management of Parkinson’s Disease Healthcare: Evaluating the Role and Impact of Mobile Health Devices (Preprint)

Memedi,

Emruli,

Cao

et al. 2024

Preprint

View full text Add to dashboard Cite

BACKGROUND Mobile health devices (MHDs), such as wearables and smartphones, have the potential to improve the monitoring of people with Parkinson’s disease (PD) and inform timely and individualized disease management decisions. OBJECTIVE This paper provides an overview of the technologies behind mobile health devices MHDs and investigate their potential for sustainable management of PD. METHODS A literature search using PubMed, Scopus, and IEEE Xplore was performed between October 30, 2023, and November 16, 2023, for studies investigating the use of MHDs to measure PD cardinal motor features. RESULTS A total of 138 relevant papers drawn from 2364 papers were analyzed. MHDs have been mainly used to assess gait and bradykinesia (slowness of movements). Most studies focused on the patient outcomes, while overlooking aspects such as the integration of MHDs in healthcare workflows and sustainable viability factors (user adoption and cost-effectiveness). CONCLUSIONS When designing MHD-based interventions it is essential to balance the focus on immediate health benefits for PD patients with equal consideration for technology integration, cost-effectiveness, and user adoption to ensure comprehensive, impactful, and sustainable contributions of such interventions.

show abstract

Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease

Cited by 3 publications

References 26 publications

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept

High-altitude tree growth responses to climate change across the Hindu Kush Himalaya

Towards Sustainable Management of Parkinson’s Disease Healthcare: Evaluating the Role and Impact of Mobile Health Devices (Preprint)

Contact Info

Product

Resources

About