The self-medication hypothesis of addictive disorders derives primarily from clinical observations of patients with substance use disorders. Individuals discover that the specific actions or effects of each class of drugs relieve or change a range of painful affect states. Self-medication factors occur in a context of self-regulation vulnerabilities--primarily difficulties in regulating affects, self-esteem, relationships, and self-care. Persons with substance use disorders suffer in the extreme with their feelings, either being overwhelmed with painful affects or seeming not to feel their emotions at all. Substances of abuse help such individuals to relieve painful affects or to experience or control emotions when they are absent or confusing. Diagnostic studies provide evidence that variously supports and fails to support a self-medication hypothesis of addictive disorders. The cause-consequence controversy involving psychopathology and substance use/abuse is reviewed and critiqued. In contrast, clinical observations and empirical studies that focus on painful affects and subjective states of distress more consistently suggest that such states of suffering are important psychological determinants in using, becoming dependent upon, and relapsing to addictive substances. Subjective states of distress and suffering involved in motives to self-medicate with substances of abuse are considered with respect to nicotine dependence and to schizophrenia and posttraumatic stress disorder comorbid with a substance use disorder.

The Self-medication Hypothesis of Substance Use Disorders: A Reconsideration and Recent Applications

Human Activity Recognition: A Survey

The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results.

Sensor-Based Datasets for Human Activity Recognition – A Systematic Review of Literature

The hidden Markov models are statistical models used in many real-world applications and communities. The use of hidden Markov models has become predominant in the last decades, as evidenced by a large number of published papers. In this survey, 146 papers (101 from Journals and 45 from Conferences/Workshops) from 93 Journals and 44 Conferences/Workshops are considered. The authors evaluate the literature based on hidden Markov model variants that have been applied to various application fields. The paper represents a short but comprehensive description of research on hidden Markov model and its variants for various applications. The paper shows the significant trends in the research on hidden Markov model variants and their applications.

A Systematic Review of Hidden Markov Models and Their Applications

With the advent of digital approaches to mental health, modern artificial intelligence (AI), and machine learning in particular, is being used in the development of prediction, detection and treatment solutions for mental health care. In terms of treatment, AI is being incorporated into digital interventions, particularly web and smartphone apps, to enhance user experience and optimise personalised mental health care. In terms of prediction and detection, modern streams of abundant data mean that data-driven AI methods can be employed to develop prediction/detection models for mental health conditions. In particular, an individual’s ‘digital exhaust’, the data gathered from their numerous personal digital device and social media interactions, can be mined for behavioural or mental health insights. Language, long considered a window into the human mind, can now be quantitatively harnessed as data with powerful computer-based natural language processing to also provide a method of inferring mental health. Furthermore, natural language processing can also be used to develop conversational agents used for therapeutic intervention.

AI in mental health.

Activities of Daily Livings (ADLs) refer to the activities that are carried out by an individual for everyday living. Recognition of ADLs is key element for building intelligent and pervasive environments. We propose a two-layer HMM to build a ADLs recognition model that can represent the mapping between low-level sensor data and high-level activity based on the binary sensor data. We used embedded sensor with appliances or object to get object used sequence data as well as object name, type, interaction time, and location. In the first layer, we use location data of object used sensor to predict the activity class and in the second layer object used sequence data to determine the exact activity. We perform comparison with other activity recognition models using three real datasets to validate the proposed model. The results show that the proposed model achieves significantly better recognition performance than other models.

https://journals.sagepub.com/doi/pdf/10.1155/2016/4560365

Two-layer hidden Markov model for human activity recognition in home environments

Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone

Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are typical for now, but recognizing complex activities such as concurrent and interleaving activity is still a major challenging issue. In this paper, we propose a two-phase hybrid deep machine learning approach using bi-directional Long-Short Term Memory (BiLSTM) and Skip-Chain Conditional random field (SCCRF) to recognize the complex activity. BiLSTM is a sequential generative deep learning inherited from Recurrent Neural Network (RNN). SCCRFs is a distinctive feature of conditional random field (CRF) that can represent long term dependencies. In the first phase of the proposed approach, we recognized the concurrent activities using the BiLSTM technique, and in the second phase, SCCRF identifies the interleaved activity. Accuracy of the proposed framework against the counterpart state-of-art methods using the publicly available datasets in a smart home environment is analyzed. Our experiment's result surpasses the previously proposed approaches with an average accuracy of more than 93%.

A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition.

State-Space based Linear Modeling for Human Activity Recognition in Smart Space

Epilepsy is a complex neurological condition that affects a large number of people worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and is widely used in epilepsy diagnosis, but it usually requires manual inspection, which can be hours long, by a neurologist. Several automatic systems have been proposed to detect epilepsy but still have some unsolved issues. In this study, we proposed a dynamic method using a deep learning model (Epileptic-Net) to detect an epileptic seizure. The proposed method is largely heterogeneous and comprised of the dense convolutional blocks (DCB), feature attention modules (FAM), residual blocks (RB), and hypercolumn technique (HT). Firstly, DCB is used to get the discriminative features from the EEG samples. Then, FAM extracts the essential features from the samples. After that, RB learns more vital parts as it entirely uses information in the convolutional layer. Finally, HT retains the efficient local features extracted from the layers situated at the different levels of the model. Its performance has been evaluated on the University of Bonn EEG dataset, divided into five distinct classes. The proposed Epileptic-Net achieves the average accuracy of 99.95% in the two-class classification, 99.98% in the three-class classification, 99.96% in the four-class classification, and 99.96% in classifying the complicated five-class problem. Thus the proposed approach shows more competitive results than the existing model to detect epileptic seizures. We also hope that this method can support experts in achieving objective and reliable results, lowering the misdiagnosis rate, and assisting in decision-making.

https://www.mdpi.com/1424-8220/22/3/728/pdf

Keshav Thapa

Papers

Two-layer hidden Markov model for human activity recognition in home environments

Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone

A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition.

State-Space based Linear Modeling for Human Activity Recognition in Smart Space

Epileptic-Net: An Improved Epileptic Seizure Detection System Using Dense Convolutional Block with Attention Network from EEG