Multi-resident activity recognition using multi-label classification in ambient sensing smart homes

Activity recognition in smart home environment using wireless ambient sensing is a well-known problem that is being researched very actively. Rapid development in the sensing technologies has made human activity recognition very important for various fields such as health care, home monitoring, surveillance, etc. In this paper, we describe the use of Classifier Chain method of the Multi-Label Classification approach to tackle the task of multi-resident activity recognition. We evaluate the developed model of Classifier Chain with K-Nearest Neighbor as base classifier on real world ARAS dataset which consists of two smart homes with evaluation metrics such as accuracy, precision and hamming loss. Through results, it can be inferred that Classifier Chain method successfully caters the problem of multi-resident activity recognition taking into consideration underlying label dependencies

Random k-labelsets method for human activity recognition with multi-sensor data in smart home

In today's world, we are surrounded by ambient sensors everywhere that record our data of activities of daily living. Moreover, the solutions to various applications such as health care, surveillance, home monitoring, and so on are possible by inferring this data. Thus, human activity recognition, especially in the smart home environment, has been a very actively researched problem. Multiple residents in a single home environment pose several challenges making multi-resident activity recognition a daunting task. Therefore, in this paper, we model the Random k-Labelsets method of the Multi-Label Classification to tackle this activity recognition problem. The proposed method not only takes label dependencies into account which is essential for multi-resident activity recognition but also overcomes the drawbacks of other problem transformation methods. Experiments are carried on a real smart home dataset and accuracy, precision and hamming loss are selected as metrics for evaluating the results of the proposed method

Towards event analysis in time-series data: Asynchronous probabilistic models and learning from partial labels

In this thesis, we contribute in two main directions: modeling asynchronous time-series data and learning from partial labelled data. We first propose novel probabilistic frameworks to improve flexibility and expressiveness of current approaches in modeling complex real-world asynchronous event sequence data. Second, we present a scalable approach to end-to-end learn a deep multi-label classifier with partial labels. To evaluate the effectiveness of our proposed frameworks, we focus on visual recognition application, however, our proposed frameworks are generic and can be used in modeling general settings of learning event sequences, and learning multi-label classifiers from partial labels. Visual recognition is a fundamental piece for achieving machine intelligence, and has a wide range of applications such as human activity analysis, autonomous driving, surveillance and security, health-care monitoring, etc. With a wide range of experiments, we show that our proposed approaches help to build more powerful and effective visual recognition frameworks

Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition

In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care. The combination of speech recognition and speech emotion recognition can improve the feedback efficiency and the quality of service. Thus, the speech emotion recognition has been attracted much attention in both industry and academic. Since emotions existing in an entire utterance may have varied probabilities, speech emotion is likely to be ambiguous, which poses great challenges to recognition tasks. However, previous studies commonly assigned a single-label or multi-label to each utterance in certain. Therefore, their algorithms result in low accuracies because of the inappropriate representation. Inspired by the optimally interacting theory, we address the ambiguous speech emotions by proposing a novel multi-classifier interactive learning (MCIL) method. In MCIL, multiple different classifiers first mimic several individuals, who have inconsistent cognitions of ambiguous emotions, and construct new ambiguous labels (the emotion probability distribution). Then, they are retrained with the new labels to interact with their cognitions. This procedure enables each classifier to learn better representations of ambiguous data from others, and further improves the recognition ability. The experiments on three benchmark corpora (MAS, IEMOCAP, and FAU-AIBO) demonstrate that MCIL does not only improve each classifier's performance, but also raises their recognition consistency from moderate to substantial.Comment: 10 pages, 4 figure

Optimization of Dengue immunoassay by label-free interferometric optical detection method

In this communication we report a direct immunoassay for detecting dengue virus by means of a label-free interferometric optical detection method. We also demonstrate how we can optimize this sensing response by adding a blocking step able to significantly enhance the optical sensing response. The blocking reagent used for this optimization is a dry milk diluted in phosphate buffered saline. The recognition curve of dengue virus over the proposed surface sensor demonstrates the capacity of this method to be applied in Point of Care technology

The Role of Pharmacologic Treatment in Autism Spectrum Disorder

Behavioral, educational, and family therapies combined with early recognition and intervention is the current standard for management of children with autism spectrum disorder (ASD). Despite these interventions, many children still retain behavioral disturbances that negatively impact their quality of life. Researchers have estimated more than 50% of adolescents with ASD are prescribed medications to help them manage behaviors or comorbid conditions, over half of whom experience psychotropic polypharmacy. Only risperidone and aripiprazole are FDA-approved for ASD patients, although numerous medications are used off-label and in combination to treat behavioral disturbances. There is limited research supporting greater therapeutic benefits than side effects of other atypical antipsychotics, SSRIs, SNRIs, stimulants, and TCAs for ASD management. The studies currently available have shown possible efficacy for off-label drugs, but the evidence is far from comprehensive or conclusive. Further clinical investigation is needed in order to develop standards of care for the use of psychotropic pharmaceuticals to treat maladaptive behaviors associated with ASD

Hearing voices: mental illness and cultural recognition

Popular misconceptions about the danger of madness have undermined mentally ill people’s struggle for social inclusion. Consequently, efforts to think through how we might belong together must take account of mental patients’ right to a public voice. However, the article takes issue with an excessively cognitive and rationalistic conception of the public citizen. It suggests, instead, that ‘alternative’ forums can be constructed which counters the dominant cultural image of the “mad, crazy, nutter”. It concludes by discussing one innovative TV series (Video Diaries) in which ‘mentally ill’ participants’ reveal their capacity to speak for themselves