EM-Psychiatry: An Ambient Intelligent System for Psychiatric Emergency

Alam, Md. Golam Rabiul; Haw, Rim; Kim, Sung Soo; Azad, Md. Abul Kalam; Abedin, Sarder Fakhrul; Hong, Choong Seon

doi:10.1109/tii.2016.2610191

Cited by 22 publications

(10 citation statements)

References 17 publications

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“…The constraint in (12) indicates that each Fog device r can be associated with a limited number of IoT devices d ∈ D and q r is the quota value, which is equal to the number of subchannels of Fog device r. In ( 13), the constraint indicates each of IoT device d ∈ D can be associated with only one Fog device r ∈ R. The first two constraints ( 7) and ( 6) address the network resource allocation for the application running in the IoT devices. The constraint in ( 6) is the bandwidth capacity b k d,r of the associated Fog device r ∈ R when the assigned subchannel is k ∈ K and b r max is the maximum bandwidth of the fog devices r ∈ R. In (7), the capacity (throughput) β k d,r is an allocation vector with feasible allocations based on the subchannel bandwidth b k d,r via Fog device r ∈ R while assigning subchannel k ∈ K, and β d sla is the minimum QoS requirement imposed by IoT device d ∈ A r .…”

Section: B Problem Formulationmentioning

confidence: 99%

“…In that case, the resource allocation has to be mapped to a particular IoT application, depending on the application type, resource demand, and service priority [5]. For example, the Ultra-Reliable Low Latency Communications (URLLC) service type [6] applications have high requirement of a tolerable bit error rate (BER) followed by ensuring an acceptable data delay [7]. In contrast, the enhanced Mobile Broadband (eMBB) service type [8] applications in Fog network generating real time IoT traffics [9] may have more stringent requirement on the bandwidth requirement than that of timeliness and error free communication [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resource Allocation for Ultra-Reliable and Enhanced Mobile Broadband IoT Applications in Fog Network

Abedin

Alam

Kazmi

et al. 2019

IEEE Trans. Commun.

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118

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In recent years, in order to provide a better quality of service (QoS) to Internet of Things (IoT) devices, the cloud computing paradigm has shifted toward the edge. However, the resource capacity (e.g., bandwidth) in fog network technology is limited and it is essential to efficiently bind the IoT applications with stringent QoS requirements with the available network infrastructure. In this paper, we formulate a joint user association and resource allocation problem in the downlink of the fog network, considering the evergrowing demand of QoS requirements imposed by the ultra-reliable low latency communications and enhanced mobile broadband services. First, we determine the priority of different QoS requirements of heterogeneous IoT applications at the fog network by enforcing the analytical framework using an analytic hierarchy process (AHP). Using the AHP, we then formulate a two-sided matching game to initiate stable association between the fog network infrastructure (i.e., fog devices) and IoT devices. Subsequently, we consider the externalities in the matching game that occurs due to job delay and solve the network resource allocation problem by applying the "best-fit" resource allocation strategy during matching. The simulation results illustrate the stability of the user association and efficiency of resource allocation with higher utility gain.

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Section: B Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resource Allocation for Ultra-Reliable and Enhanced Mobile Broadband IoT Applications in Fog Network

Abedin

Alam

Kazmi

et al. 2019

IEEE Trans. Commun.

Self Cite

118

View full text Add to dashboard Cite

show abstract

“…In addition to psychological emotion assessment scales, several scientific studies have been carried out to detect human emotions automatically. Facial expression-based emotion recognition is studied in [22], where the authors' used a maximum entropy Markov model (MEMM) [15] to recognize basic emotions. A decision tree-based typical facial expression recognition (FER) system is proposed in [32].…”

Section: Related Workmentioning

confidence: 99%

“…As emotion is characterized by functional mental activity, determining the true emotional state is very challenging. However, with advances in technology, scientific methodologies and tools are successfully being applied in emotion recognition [6], [7], activity recognition and monitoring [8], [9], stress measurement [10], depression assessment [11], [12], and mental healthcare [13]- [15]. This research applies signal processing [5], big-data management [16], Internet of Medical Things (IoMT) [17] and advanced machine learning [18] methodologies for mining the affective states of individuals.…”

Section: Introductionmentioning

confidence: 99%

Healthcare IoT-Based Affective State Mining Using a Deep Convolutional Neural Network

et al. 2019

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Human effects are complex phenomena, which are studied for pervasive healthcare and wellbeing. The legacy pen and paper-based affective state determination methods are limited in their scientific explanation of causes and effects. Therefore, due to advances in intelligence technology, researchers are trying to apply some advanced artificial intelligence (AI) methods to realize individuals' affective states. To recognize, realize, and predict a human's affective state, domain experts have studied facial expressions, speeches, social posts, neuroimages, and physiological signals. However, with the advancement of the Internet of Medical Things (IoMT) and wearable computing technology, on-body non-invasive medical sensor observations are an effective source for studying users' effects or emotions. Therefore, this paper proposes an IoMT-based emotion recognition system for affective state mining. Human psychophysiological observations are collected through electromyography (EMG), electro-dermal activity (EDA), and electrocardiogram (ECG) medical sensors and analyzed through a deep convolutional neural network (CNN) to determine the covert affective state. According to Russell's circumplex model of effects, the five basic emotional states, i.e., happy, relaxed, disgust, sad, and neutral, are considered for affective state mining. An experimental study is performed, and a benchmark dataset is used to analyze the performance of the proposed method. The higher classification accuracy of the primary affective states has justified the performance of the proposed method. INDEX TERMS Affective computing, convolutional neural network, emotion recognition, healthcare IoT, Internet of Medical Things. The associate editor coordinating the review of this manuscript and approving it for publication was Giancarlo Fortino. Therefore, product designs, shopping mall decorations, model selection for advertisements, and black-Friday sales offer are all the outcomes of consumer affect or emotion studies. After all, science applied to emotion is needed for mental healthcare, business studies, recommender systems, affective computing, social networks, emotion communication [2] and emotion-aware robot design. Affective computing deals with the constructs of psychophysiology and generalizes human emotions as affective states [3]. In psychology, emotion is a conscious experience that can be characterized by functional mental activity and by the degree of contentment and discontentment.

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“…This integrated model increases the capacity of problem solving and improves suggestion accuracy. In work [33], an ambient intelligent system of in-home psychiatric care service for emergency psychiatry (EM-psychiatry) is proposed for the remote monitoring of psychiatric emergency patients. The emergency psychiatric states of patients are modeled as the states of the maximum-entropie Markov model (MEMM), in which sensor observations, psychiatric screening scores, and patients' histories are considered as the observations of MEMM.…”

Section: VImentioning

confidence: 99%