Lynx: Automatic Elderly Behavior Prediction in Home Telecare

López-Guede, José Manuel; Moreno-Fernandez-de-Leceta, Aitor; Martinez-Garcia, Alexeiw; Graña, Manuel

doi:10.1155/2015/201939

Cited by 14 publications

(6 citation statements)

References 28 publications

(22 reference statements)

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“…The experts extracted paper information in seven categories, including author, year, task, accuracy, and data mining techniques. Table 1 shows relevant papers and their characteristics based on study-specific aims [ 9 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 ].…”

Section: Methodsmentioning

confidence: 99%

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems

Fallah

Kalhori

2017

Healthc Inform Res

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ObjectivesSmartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems.MethodsWe systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed.ResultsData mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients’ self-management.ConclusionsEmbedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

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Section: Methodsmentioning

confidence: 99%

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems

Fallah

Kalhori

2017

Healthc Inform Res

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“…49 In this regard, future work will be devoted to the recognition of neuroethogram patterns, and characterization of deviations related with neural disorders or diseases. We are interested in the development of tools for the study of how some treatments affect subjects with some form or another of neural diseases, 79 elderly, 80 or children with special needs. 81 Many of these conditions show typical external behavioral traits that can be objectively measured by means of motion capture devices, allowing to measure changes in behavior due to treatment with great accuracy.…”

Section: T0-mentioning

confidence: 99%

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

Graña

Aguilar-Moreno

Asiaín

et al. 2020

Int. J. Neur. Syst.

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Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house ([Formula: see text]) and out-house ([Formula: see text]) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognition.

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“…The Necessity intelligent system [10] includes an adaptive mechanism that considers the monitored person's routines and detects abnormal situations. Lynx [11] uses a wireless network of heterogeneous sensors to collect data about the monitored person's environment and daily activities. The data are used to infer activities and identify behaviour patterns, which are subsequently employed by an expert knowledge system to detect dangerous situations and send real-time alerts to caregivers.…”

Section: Ambient Assisted Livingmentioning

confidence: 99%

i-Light—Intelligent Luminaire Based Platform for Home Monitoring and Assisted Living

et al. 2018

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We present i-Light, a cyber-physical platform that aims to help older adults to live safely within their own homes. The system is the result of an international research project funded by the European Union and is comprised of a custom developed wireless sensor network together with software services that provide continuous monitoring, reporting and real-time alerting capabilities. The principal innovation proposed within the project regards implementation of the hardware components in the form of intelligent luminaires with inbuilt sensing and communication capabilities. Custom luminaires provide indoor localisation and environment sensing, are cost-effective and are designed to replace the lighting infrastructure of the deployment location without prior mapping or fingerprinting. We evaluate the system within a home and show that it achieves localisation accuracy sufficient for room-level detection. We present the communication infrastructure, and detail how the software services can be configured and used for visualisation, reporting and real-time alerting.

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Lynx: Automatic Elderly Behavior Prediction in Home Telecare

Cited by 14 publications

References 28 publications

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

i-Light—Intelligent Luminaire Based Platform for Home Monitoring and Assisted Living

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