Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm

Varatharajan, R.; Manogaran, Gunasekaran; Kumar, Priyan Malarvizhi; Sundarasekar, Revathi

doi:10.1007/s10586-017-0977-2

Cited by 219 publications

(88 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These sensors have been used to detect abnormal fall events for people with disabilities or the elderly (Mubashir, Shao, and Seed 2013), identify behaviors in sports activities, such as snowboarding and football (Kautz, Groh, and Eskofier 2015;Groh, Fleckenstein, and Eskofier 2016), detect whether a person is in a dangerous situation subject to crime or physical violence (López-Cuevas et al 2017), or reveal mental health issues for citizens (Rodrigues et al 2015). These wearable sensors leverage the advancement of information and communications technology (ICT) to collect healthrelated information directly but remotely for doctors and health facilities (Gao et al 2016;Varatharajan et al 2018).…”

Section: Health Sensingmentioning

confidence: 99%

Spatiotemporal event detection: a review

Bambacus

Cervone

et al. 2020

International Journal of Digital Earth

View full text Add to dashboard Cite

The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.

show abstract

Section: Health Sensingmentioning

confidence: 99%

Spatiotemporal event detection: a review

Bambacus

Cervone

et al. 2020

International Journal of Digital Earth

View full text Add to dashboard Cite

show abstract

“…Existing works often focus on assisting people who experience difficulties in maintaining independent living, for example, elderly or people with certain chronic diseases [11], such as heart disease, diabetes, and Alzheimer's. For instance, Varatharajan et al [12] present a dynamic time warping algorithm-based early detection of Alzheimer disease using wearable sensors, whilst Romero et al [13] describe a system that diagnose and monitor Parkinson's disease. Nonetheless, it is impossible to develop a one-size-fits-all application that can address all the needs of various individuals; hence, several directions have been explored as demonstrated in the following sections.…”

Section: Related Workmentioning

confidence: 99%

Wearable IoT enabled real-time health monitoring system

Wan

Al-awlaqi

Li³

et al. 2018

J Wireless Com Network

217

View full text Add to dashboard Cite

As the age profile of many societies continues to increase, in addition to the increasing population of people affected by chronic diseases, including diabetes, cardiovascular disease, obesity, and so on, supporting health, both mentally and physically, is of increasing importance if independent living is to be maintained. Sensing, remote health monitoring, and, ultimately, recognising activities of daily living have been an promising solution. From a technical perspective, the Internet of Things (IoT) is gaining a rapidly growing attention in many disciplines, especially in personalised healthcare. Meanwhile, body area sensor network (BASN) under the IoT framework has been widely applied for ubiquitous health monitoring, for example. ECG monitoring has been commonly adopted as vital approach for diagnosing heart disease. The main contribution of this paper include the following: firstly, this paper presents a novel system, the WISE (Wearable IoT-cloud-baSed hEalth monitoring system), for real-time personal health monitoring. WISE adopts the BASN (body area sensor network) framework in the support of real-time health monitoring. Several wearable sensors have been embedded, including the heartbeat, body temperature, and the blood pressure sensors. Secondly, the majority of existing wearable health monitoring systems requisite a smart phone as data processing, visualisation, and transmission gateway, which will indeed impact the normal daily use of the smart phone. Whilst in WISE, data gathered from the BASN are directly transmitted to the cloud, and a lightweight wearable LCD can be embedded as an alternative solution for quick view of the real-time data.

show abstract

“…Afterwards, we extend it to subsequences, while we finally combine it with a k-nearest neighbor procedure. Dynamic Time Warping has proven to be an exceptionally powerful distance measuring device for time series [12,25]. It is an algorithm for measuring similarity between two temporal sequences where events occur at different speeds.…”

Section: Wasserstein-based Dynamic Time Warpingmentioning

confidence: 99%

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Kraus

Feuerriegel

2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i. e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-ofthe-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers.In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns.(2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on stateof-the-art decision rules from the literature is outperformed by a factor of 4.0.

show abstract

Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm

Cited by 219 publications

References 51 publications

Spatiotemporal event detection: a review

Spatiotemporal event detection: a review

Wearable IoT enabled real-time health monitoring system

Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Contact Info

Product

Resources

About