Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People

Wickramasinghe, Asanga; Ranasinghe, Damith C.; Visvanathan, Renuka

doi:10.1109/jbhi.2016.2576285

Cited by 58 publications

(62 citation statements)

References 36 publications

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“…Getting out of bed is one of the main activities reported as causing falls in older populations [74]. Considering different degrees of mobility impairments, this can be significant for the pre-evacuation phase of occupants.…”

Section: Resultsmentioning

confidence: 99%

A review on the role of functional limitations on evacuation performance using the International Classification of Functioning, Disability and Health

et al. 2020

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This scoping review addresses the role of functional limitations on evacuation performance of adults in public buildings. Although this topic has been addressed in evacuation research, no linkage is currently available between functional limitations, the predominant activities affected by them and evacuation performance. This review strives to open a debate on the need to classify the impact of disability in terms of functional limitations on evacuation performance according to methods adopted in health science. This paper reviews literature concerning evacuation from public buildings with adults aged ≥ 60 years and/or adults aged ≥ 18 years with functional limitations. The International Classification of Functioning, Disability and Health has been used to identify predominant activities during an evacuation and to perform a structured classification at different levels of resolution to address self-evacuation possibilities. Results of the review are presented in a tabular form linking predominant activities in terms of the International Classification of Functioning, Disability and Health and six categories of functional limitations with the engineering evacuation time-line. The suggested classification can facilitate the assessment of the evacuation-related issues in buildings in relation to the population under consideration. The main research gaps identified include the lack of studies concerning the impact of cognitive limitations on egress, and the need to add the temporal dimension to the methods adopted in accessibility research to allow for their use in the egress field.

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Section: Resultsmentioning

confidence: 99%

A review on the role of functional limitations on evacuation performance using the International Classification of Functioning, Disability and Health

et al. 2020

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“…It has been deployed in numerous applications (e.g. body temperature monitoring [41][42][43], sometimes with on-chip temperature sensors [44,45], vital signs monitoring systems like ECG [46] or EEG [47], relative plantar pressure [48], bed-regress tracking in elderly people [49], breath monitoring [50], pH skin sensor [51], etc. )…”

Section: Introductionmentioning

confidence: 99%

Wireless In Vivo Biofuel Cell Monitoring

Trocchio

Carucci

Sindhu

et al. 2021

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

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Enzymatic reactions involving glucose hold the potential for building implantable biosensors and embedded power generators for various medical applications. While Biofuel cells (BFCs) such as enzymatic glucose/O2 are ensured to benefit from abundant chemical resources that can be harvested in the immediate environment of the human body, the highly critical in vivo kinetics of biofuel cell is not yet fully understood. Unfortunately, existing solutions for real-time monitoring of the reaction on rodents are not possible today, or too bulky, which has a biasing impact on the animal behavior. This work presents a light, battery-less, and wireless strategy to continuously monitor a BFC implanted in a laboratory rat using a Frequency Identification (RFID) link. An extremely lightweight and flexible tag antenna of footprint lower than 10 cm² is presented with communication capability above 60 cm in field environment. The operational capabilities are demonstrated with a 24-hour continuous monitoring of an enzymatic glucose/O2 reaction, both in vitro and in vivo. Keywords-RFID tags, biofuel cell, wearable sensors, in vitro, in vivo. for the future" Programme IdEx Bordeaux (ANR-10-IDEX-03-02), the ANR (Bio3, ANR-16-CE19-0001-01, animal experimentation and salary of CM). KRS and LDT received a doctoral fellowship from LabEx AMADEus

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“…A wearable device is widely used for an elderly activity monitoring system [6,7]. However, in most cases, the elderly can feel uncomfortable with their daily living activities, which leads to discontinuous monitoring [8,9].…”

Section: Introductionmentioning

confidence: 99%

Bed Position Classification by a Neural Network and Bayesian Network Using Noninvasive Sensors for Fall Prevention

Viriyavit

Sornlertlamvanich

2020

Journal of Sensors

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Falls from a bed often occur when an elderly patient attempts to get out of bed or comes close to the edge of a bed. These mishaps have a high possibility of serious injuries, such as bruises, soreness, and bone fractures. Moreover, a lack of repositioning the body of a bedridden elderly person may cause bedsores. To avoid such a risk, a continuous activity monitoring system is needed for taking care of the elderly. In this study, we propose a bed position classification method based on the sensor signals collected from only four sensors that are embedded in a panel (composed of two piezoelectric sensors and two pressure sensors). It is installed under the mattress on the bed. The bed positions considered are classified into five different classes, i.e., off-bed, sitting, lying center, lying left, and lying right. To collect the training dataset, three elderly patients were asked for consent to participate in the experiment. In our approach, a neural network combined with a Bayesian network is adopted to classify the bed positions and put a constraint on the possible sequences of the bed positions. The results from both the neural network and Bayesian network are combined by the weighted arithmetic mean. The experimental results have a maximum accuracy of position classification of 97.06% when the proportion of coefficients for the neural network and the Bayesian network is 0.3 and 0.7, respectively.

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Sequence Learning with Passive RFID Sensors for Real-Time Bed-Egress Recognition in Older People

Cited by 58 publications

References 36 publications

A review on the role of functional limitations on evacuation performance using the International Classification of Functioning, Disability and Health

A review on the role of functional limitations on evacuation performance using the International Classification of Functioning, Disability and Health

Wireless In Vivo Biofuel Cell Monitoring

Bed Position Classification by a Neural Network and Bayesian Network Using Noninvasive Sensors for Fall Prevention

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