Application of data fusion techniques and technologies for wearable health monitoring

King, Rachel; Villeneuve, Emma; White, Ruth; Sherratt, R. Simon; Holderbaum, William; Harwin, William

doi:10.1016/j.medengphy.2016.12.011

Cited by 151 publications

(86 citation statements)

References 70 publications

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“…young fit to old frail) may only be attainable by multiple wearables [56] or by fusing different data streams [57] detecting slight changes in movement. While the use of complex/multiple wearables is not feasible during free-living, it highlights ongoing developments.…”

Section: Micro Gaitmentioning

confidence: 99%

Wearables for independent living in older adults: Gait and falls

Godfrey

2017

Maturitas

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Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) are one approach to offer a viable means for ubiquitous, sustainable and scalable monitoring in habitual free-living environments. Gait has been presented as a relevant (bio) marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of recent wearable gait assessment literature, focusing on the need for an algorithm fusion approach to measurement cumulating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Opportunities for how this domain needs to progress are also summarised. Highlights• Wearables can meet older adults' needs for independent living.• Gait assessment is a (bio)marker within ageing and different pathologies.• Measuring gait with wearables has been innovative but fraught with inconsistencies.

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Section: Micro Gaitmentioning

confidence: 99%

Wearables for independent living in older adults: Gait and falls

Godfrey

2017

Maturitas

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“…Information fusion can be used to overcome limitations of individual sensors by pooling either information or decisions from the singular sources. Fusion can be achieved at signal, feature, and decision level [36,[38][39]. Signal level fusion can take place between sensors that record the same quantities (for example accelerometers placed on different body parts of the monitored subject), or commensurate data (e.g.…”

Section: Information Fusion Methodsmentioning

confidence: 99%

“…Compared to filter-based methods, wrapper methods can be resource intensive, requiring more iterations and exhaustive search, to run the classification algorithm.  Embedded methods, which integrate the classification and feature selection together with feedback (SVM-RFE) [36].…”

Section: Truepositive Sensitivitymentioning

confidence: 99%

“…The second method utilizes Fuzzy logic, where the binary loss is used as a Fuzzy set as described in equation (5) [36]. and are two sets of negated binary loss values including 10 elements related to the 10 activities, as generated from the two SVM classifiers, whereas is the new binary loss set which represents the least optimal outcome (i.e.…”

Section: B Decision Level Fusionmentioning

confidence: 99%

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A Multisensory Approach for Remote Health Monitoring of Older People

Shrestha

Heidari

et al. 2018

IEEE J. Electromagn. RF Microw. Med. Biol.

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Growing life expectancy and increasing incidence of multiple chronic health conditions are significant societal challenges. Different technologies have been proposed to address these issues, to detect critical events such as stroke or falls, and to monitor automatically human activities for health condition inference and anomalies detection. This paper aims to investigate two types of sensing technologies proposed for assisted living: wearable and radar sensors. First, different feature selection methods are validated and compared in terms of accuracy and computational loads. Then, information fusion is applied to enhance activity classification accuracy combining the two sensors. Improvements in classification accuracy of approximately 12% using feature level fusion is achieved with both Support Vector Machine and K Nearest Neighbor classifiers. Decision-level fusion schemes are also investigated, yielding classification accuracy in the order of 97-98%.

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“…Akan veri kümeleme yaklaşımı söz konusu bu veriyi değerli bilgiye çevirme konusunda pek çok açıdan ihtiyacı karşılamaktadır. Akan veri kümeleme tıklama verisi [6], saldırı tespit sistemleri [7][8][9], finansal uygulamalar [10], bilimsel araştırmalar [11], sağlık araştırmaları [12][13][14], nesnelerin interneti (IoT) [15] ve mobil uygulamalar [16] gibi pek çok alanda kullanılmaktadır [17][18][19].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

Şenol

Karacan

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Highlights:Graphical/Tabular Abstract  Fully online data stream clustering  Evolutionary based clustering  Adaptive radius  Time based summarization  Memory for past status of clusters Figure A. Comparision of clustering quality and run-time complexity of algorithms on KDD datasetPurpose: The aim of this article to propose a new data stream clustering algorithm, which has an adaptive radius, can adapt itself to the evolutionary structure of streaming data and works in a fully online manner. Theory and Methods:In this study, kd-tree is used to forming and splitting clusters, adaptive radius approach is used to support increasing and decreasing the size of clusters, active/inactive status of clusters is used to adapt to the evolutionary structure of streaming data and all the operations are done online. In order to create a new cluster, the data that does not belong to any cluster are placed in a kd-tree, and the rangesearch operation is performed on those data according to predefined variables r (the radius of candidate cluster) and N (the number of data must be in the area). After forming the clusters, the radius of each cluster could be increased or decreased over time if necessary. Some clusters may be split and some may be merged over time because of dynamically changing structure of streaming data. Inactivation and reactivation of the status of clusters is used to allow for the identification of clusters formed in the same region at a different time interval with same cluster labels in accordance with the nature of the streaming data contrary to literature. This feature increases clustering quality of the proposed method. A summarization method that consist of time window and sliding window is used to support time based summarization without reduce performance. Results:To verify the effectiveness of KD-AR Stream algorithm, it is compared with SE-Stream, DPStream, and CEDAS on a variety of well-known datasets in terms of clustering quality and run-time complexity. The results show that KD-AR Stream outperforms other algorithms with a higher clustering success in a reasonable time as shown in Fig. A. Conclusion:The aim of this study is to propose a novel data stream clustering algorithm that adapts to the dynamic structure of the streaming data. The aim achieved by using the five evolutionary process which are appearance, activation/inactivation, self-evolution, merge, and split. According to the results, the proposed method is very successful in terms of clustering quality and run-time complexity.

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Application of data fusion techniques and technologies for wearable health monitoring

Cited by 151 publications

References 70 publications

Wearables for independent living in older adults: Gait and falls

Wearables for independent living in older adults: Gait and falls

A Multisensory Approach for Remote Health Monitoring of Older People

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

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