A Survey of Healthcare Monitoring Systems for Chronically Ill Patients and Elderly

Mardini, Mamoun; Iraqi, Youssef; Agoulmine, Nazim

doi:10.1007/s10916-019-1165-0

Cited by 36 publications

(26 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this model, higher order terms (n > 1) of (2) are neglected, hence measured acceleration is expressed by (3), and the vector form is given by (4), where Z = diag(z). The joint PDF of a is given by (5), where σ 2 i is the error variance at sample i expressed by (6). The log likelyhood (LL) function is given by (7).…”

Section: M1mentioning

confidence: 99%

“…Monitoring these activities can effectively prevent and manage chronic diseases. Moreover, in [5], [6] an approach for the monitoring of elders and people with neurological pathologies (e.g., Alzheimer) has been proposed. Multi-sensor approaches are used to recognize critical events, such as falls or prolonged inactivity, that can be used to alert caregivers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accuracy Limits of Embedded Smart Device Accelerometer Sensors

Ibrahim

El‐Tawil

2020

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Smartphones are an indispensable tool in modern day-today life. Their widespread use has spawned numerous applications targeting diverse domains such as bio-medical, environment sensing and infrastructure monitoring. In such applications, the accuracy of the sensors at the core of the system is still questionable, since these devices are not originally designed for high accuracy sensing purposes. In this work, we investigate the accuracy limits of one of the commonly used sensors, namely, smartphone accelerometer. We focus on the efficacy of a smartphone as an acceleration measuring device, rather than focusing only on the accuracy of its internal accelerometer chip. This holistic approach includes additional errors that arise from the device operating system such as sampling time uncertainty. Hence, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors represented by a white Gaussian process. The model is validated experimentally using shake table experiments, and maximum likelyhood estimation (MLE) is used to estimate the sampling time uncertainty standard deviation. Moreover, we derive the Cramer-Rao lower bound (CRLB) of acceleration estimation based on the proposed model.

show abstract

Section: M1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accuracy Limits of Embedded Smart Device Accelerometer Sensors

Ibrahim

El‐Tawil

2020

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

show abstract

“…In 2015, a survey collected five vision-based public datasets on falls [15], while in 2017, twelve wearable-based public datasets on falls were surveyed [16]. Additionally, only in 2019 have surveys about techniques for abnormal human activity recognition [17], healthcare monitoring systems for elderly people [18], and fall prediction with sensors in smart homes appeared [7].…”

Section: Related Workmentioning

confidence: 99%

eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research

Riquelme

Espinoza²,

Rodenas

et al. 2019

Sensors

View full text Add to dashboard Cite

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.

show abstract

“…Finding the optimal parameters update is very complex and time consuming, hence, in this section, we propose three heuristics trying to find a common beacon interval for all the WPANs while satisfying (2). Indeed, if all the WPANs have the same beacon interval we have ∀i, j b i ↑ b j = b i = b j , and conditions (2) can be simplified as follows:…”

Section: Same Beacon Interval Approachesmentioning

confidence: 99%

“…Therefore, in the case where all WPANs have the same beacon interval, (2) reduces to (5). (2) and 3are satisfied:…”

Section: Same Beacon Interval Approachesmentioning

confidence: 99%

Dynamic Adaptation for WPANs Collision Prevention in eHealth Environments

Iraqi

Mekouar

2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper presents several adaptive mechanisms to dynamically update the wireless personal area networks (WPANs) parameters that are essential in wearable devices, especially in healthcare environments. Instead of collision detection and recovery, collision prevention is achieved using the proposed algorithms to guarantee a collision-free environment. We present a binary integer programming model for the optimal solution. To avoid the increased complexity, we introduce six suboptimal algorithms. The presented algorithms try to minimize network disruption by updating the parameters of a minimal number of the WPANs. The simulation results show how the algorithms trade off minimal disruption and time complexity.

show abstract

A Survey of Healthcare Monitoring Systems for Chronically Ill Patients and Elderly

Cited by 36 publications

References 67 publications

Accuracy Limits of Embedded Smart Device Accelerometer Sensors

Accuracy Limits of Embedded Smart Device Accelerometer Sensors

eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research

Dynamic Adaptation for WPANs Collision Prevention in eHealth Environments

Contact Info

Product

Resources

About