A Wireless Under-Mattress Sensor System for Sleep Monitoring in People with Major Depression

Mahdavi, Hadiseh; Ramos‐Castro, J.; Giovinazzo, Giuseppe; González, Miguel Ángel García; Rosell, J.

doi:10.2316/p.2012.764-119

Cited by 5 publications

(2 citation statements)

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“…The second datatype used to test the proposed methods consists of a real dataset acquired during the Help4Mood project, including 5 days of single user activity monitoring using accelerometers embedded in five different devices including wristwatch, belt, keyring, smartphone [53], and undermattress sensor [54]. This second dataset allowed us to test the fusion models in a real scenario.…”

Section: Evaluation Methodologymentioning

confidence: 99%

Fusing actigraphy signals for outpatient monitoring

Fuster–Garcia¹,

Bresó

Martı́nez-Miranda

et al. 2015

Information Fusion

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ElsevierFuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015) AbstractActigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a * good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.

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Section: Evaluation Methodologymentioning

confidence: 99%

Fusing actigraphy signals for outpatient monitoring

Fuster–Garcia¹,

Bresó

Martı́nez-Miranda

et al. 2015

Information Fusion

Self Cite

View full text Add to dashboard Cite

show abstract

“…A general term for these methods is ballistocardiography (BCG). Hydraulic under mattress sensors, piezoelectric and piezoresistive transducers, Load cells, strain gauges and fiber-optic pressure sensors are some of the sensors which have been used extensively (Motoi et al 2009, Adami et al 2010, Dziuda et al 2012, Mahdavi et al 2012.…”

Section: Introductionmentioning

confidence: 99%