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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With the dramatic proliferation in recent years, the social networks have become a ubiquitous medium of marketing and the influence maximization (IM) technique, being such a viral marketing tool, has gained significant research interest in recent years. The IM determines the influential users who maximize the profit defined by the maximum number of nodes that can be activated by a given seed set. However, most of the existing IM studies do not focus on estimating the seeding cost which is identified by the minimum number of nodes that must be activated in order to influence the given seed set. They either assume the seed nodes are initially activated, or some free products or services are offered to activate the seed nodes. However, seed users might also be activated by some other influential users, and thus, the reverse influence maximization (RIM) models have been proposed to find the seeding cost of target marketing. However, the existing RIM models are incapable of resolving the challenging issues and providing better seeding cost simultaneously. Therefore, in this paper, we propose a Knapsack-based solution (KRIM) under linear threshold (LT) model which not only resolves the RIM challenges efficiently, but also yields optimized seeding cost. The experimental results on both the synthesized and real datasets show that our model performs better than existing RIM models concerning estimated seeding cost, running time, and handling RIM-challenges.