Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors

Lee, Sang‐Woo; Lee, Chung-Yeon; Kwak, Donghyun; Ha, Jung-Woo; Kim, Jeong-Hee; Zhang, Byoung-Tak

doi:10.1016/j.neunet.2017.02.008

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Thanks to the technological improvements, monitoring of stress level could be performed with various wearable devices, such as smart watches and smart glasses, e.g., Google Glass or JINS MEME ES_R (JINS Holdings, Inc., Tokyo, Japan) [53][54][55][56][57][58][59][60][61], or smartphones [62]. These devices may be used to estimate the level of one's concentration; for instance, Ishimaru et al, conducted a study [63] which involved the use of JINS MEME ES_R smart glasses (JINS Holdings, Inc., Tokyo, Japan), further described in [61], to estimate the user's concentration level by processing the EOG signal and head position.…”

Section: Related Workmentioning

confidence: 99%

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

et al. 2021

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Stress is a physical, mental, or emotional response to a change and is a significant problem in modern society. In addition to questionnaires, levels of stress may be assessed by monitoring physiological signals, such as via photoplethysmogram (PPG), electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), facial expressions, and head and body movements. In our study, we attempted to find the relationship between the perceived stress level and physiological signals, such as heart rate (HR), head movements, and electrooculographic (EOG) signals. The perceived stress level was acquired by self-assessment questionnaires in which the participants marked their stress level before, during, and after performing a task. The heart rate was acquired with a finger pulse oximeter and the head movements (linear acceleration and angular velocity) and electrooculographic signals were recorded with JINS MEME ES_R smart glasses (JINS Holdings, Inc., Tokyo, Japan). We observed significant differences between the perceived stress level, heart rate, the power of linear acceleration, angular velocity, and EOG signals before performing the task and during the task. However, except for HR, these signals were poorly correlated with the perceived stress level acquired during the task.

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Section: Related Workmentioning

confidence: 99%

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

et al. 2021

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“…Cognitive decline can manifest itself in many ways, including the inability to recognize people, locations, and objects, loss of short- and long-term memory, and changes in behavior and appearance. Assistive systems include [ 47 ], and those with Google Glass devices to guide the user through a complex (cognitive) task, capturing the view of the wearer and combining with remote processing to perform real-time scene interpretation [ 48 ]. WCAs are anticipated to become a widely-used application class, in conjunction with emerging network infrastructures such as 5G that incorporate edge computing capabilities [ 49 ].…”

Section: Background Studymentioning

confidence: 99%

Smart Textiles for Improved Quality of Life and Cognitive Assessment

Oatley¹,

Choudhury²,

Buckman³

2021

Sensors

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Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). This concept paper presents a smart textile prototype to both entertain and monitor/assess the behavior of the relevant clients. The prototype includes physical computing components for music playing and simple interaction, but additionally games and data logging systems, to determine baselines of activity and interaction. Using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors woven into a fabric, the study demonstrates the kinds of augmentations possible over the normal manipulation of the traditional non-smart activity apron by incorporating light and sound effects as feedback when patients interact with different regions of the textile. A data logging system will record the patient’s behavioral patterns. This would include the location, frequency, and time of the patient’s activities within the different textile areas. The textile will be placed across the laps of the resident, which they then play with, permitting the development of a behavioral profile through the gamification of cognitive tests. This concept paper outlines the development of a prototype sensor system and highlights the challenges related to its use in a care home setting. The research implements a wide range of functionality through a novel architecture involving loosely coupling and concentrating artifacts on the top layer and technology on the bottom layer. Components in a loosely coupled system can be replaced with alternative implementations that provide the same services, and so this gives the solution the best flexibility. The literature shows that existing architectures that are strongly coupled result in difficulties modeling different individuals without incurring significant costs.

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“…There are several other examples of research that are applying the inspiration by brain structures into the field of artificial intelligence, such as grey wolf optimization [4], genetic feature selector [5], associative memory [6] or fuzzy neural network [7]. In many applications, the biological motivation for the paradigm of neural networks is an advantage because the modeled processes are also of biological origin, such as automated analysis of medical or physiological data [8,9] or mental processes modeling [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Homeostatic Learning Rule for Artificial Neural Networks

Ruzek¹

2018

NNW

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This article presents an improvement of learning algorithm for an artificial neural network that makes the learning process more similar to a biological neuron, but still simple enough to be easily programmed. This idea is based on autonomous artificial neurons that are working together and at same time competing for resources; every neuron is trying to be better than the others, but also needs the feed back from other neurons. The proposed artificial neuron has similar forward signal processing as the standard perceptron; the main difference is the learning phase. The learning process is based on observing the weights of other neurons, but only in biologically plausible way, no back propagation of error or 'teacher' is allowed. The neuron is sending the signal in a forward direction into the higher layer, while the information about its function is being propagated in the opposite direction. This information does not have the form of energy, it is the observation of how the neuron's output is accepted by the others. The neurons are trying to find such setting of their internal parameters that are optimal for the whole network. For this algorithm, it is necessary that the neurons are organized in layers. The tests proved the viability of this concept-the learning process is slower; but has other advantages, such as resistance against catastrophic interference or higher generalization.

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Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors

Cited by 8 publications

References 29 publications

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

Smart Textiles for Improved Quality of Life and Cognitive Assessment

Homeostatic Learning Rule for Artificial Neural Networks

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