On Leveraging Statistical and Relational Information for the Representation and Recognition of Complex Human Activities

Machine activity recognition aims to automatically predict human activities from a series of sensor signals. It is a key aspect to several emerging applications, especially in the pervasive computing field. However, this problem faces several challenges due to the complex, relational and ambiguous nature of human activities. These challenges still defy the majority of traditional pattern recognition approaches, whether they are knowledge-based or data-driven. Concretely, the current approaches to activity recognition in sensor environments fall short to represent, reason or learn under uncertainty, complex relational structure, rich temporal context and abundant common-sense knowledge. Motivated by these shortcomings, our work focuses on the combination of both data-driven and knowledge-based paradigms in order to address this problem. In particular, we propose two logic-based statistical relational activity recognition frameworks which we describe in two different parts. The first part presents a Markov logic-based framework addressing the recognition of complex human activities under realistic settings. Markov logic is a highly flexible statistical relational formalism combining the power of first-order logic with Markov networks by attaching real-valued weights to formulas in first-order logic. Thus, it unites both symbolic and probabilistic reasoning and allows to model the complex relational structure as well as the inherent uncertainty underlying human activities and sensor data. We focus on addressing the challenge of recognizing interleaved and concurrent activities while preserving the intuitiveness and flexibility of the modelling task. Using three different models we evaluate and prove the viability of using Markov logic networks for that problem statement. We also demonstrate the crucial impact of domain knowledge on the recognition outcome. Implementing an exhaustive model including heterogeneous information sources comes, however, at considerable knowledge engineering efforts. Hence, employing a standard, widely used formalism can alleviate that by enhancing the portability, the re-usability and the extension of the model. In the second part of this document, we apply a hybrid approach that goes one step further than Markov logic network towards a formal, yet intuitive conceptualization of the domain of discourse. Concretely, we propose an activity recognition framework based on log-linear description logic, a probabilistic variant of description logics. Log-linear description logic leverages the principles of Markov logic while allowing for a formal conceptualization of the domain of discourse, backed up with powerful reasoning and consistency check tools. Based on principles from the activity theory, we focus on addressing the challenge of representing and recognizing human activities at three levels of granularity: operations, actions and activities. Complying with real-life scenarios, we assess and discuss the viability of the proposed framework. In particular, we show the positive impact of augmenting the proposed multi-level activity ontology with weights compared to using its conventional weight-free variant

SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment

Objective: In an ageing world population more citizens are at risk of cognitive impairment, with negative consequences on their ability of independent living, quality of life and sustainability of healthcare systems. Cognitive neuroscience researchers have identified behavioral anomalies that are significant indicators of cognitive decline. A general goal is the design of innovative methods and tools for continuously monitoring the functional abilities of the seniors at risk and reporting the behavioral anomalies to the clinicians. SmartFABER is a pervasive system targeting this objective. Methods: A non-intrusive sensor network continuously acquires data about the interaction of the senior with the home environment during daily activities. A novel hybrid statistical and knowledge-based technique is used to analyses this data and detect the behavioral anomalies, whose history is presented through a dashboard to the clinicians. Differently from related works, SmartFABER can detect abnormal behaviors at a fine-grained level. Results: We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates

Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes

The past decade has seen an explosion of the amount of digital information generated within the healthcare domain. Digital data exist in the form of images, video, speech, transcripts, electronic health records, clinical records, and free-text. Analysis and interpretation of healthcare data is a daunting task, and it demands a great deal of time, resources, and human effort. In this paper, we focus on the problem of co-morbidity recognition from patient’s clinical records. To this aim, we employ both classical machine learning and deep learning approaches.We use word embeddings and bag-of-words representations, coupled with feature selection techniques. The goal of our work is to develop a classification system to identify whether a certain health condition occurs for a patient by studying his/her past clinical records. In more detail, we have used pre-trained word2vec, domain-trained, GloVe, fastText, and universal sentence encoder embeddings to tackle the classification of sixteen morbidity conditions within clinical records. We have compared the outcomes of classical machine learning and deep learning approaches with the employed feature representation methods and feature selection methods. We present a comprehensive discussion of the performances and behaviour of the employed classical machine learning and deep learning approaches. Finally, we have also used ensemble learning techniques over a large number of combinations of classifiers to improve the single model performance. For our experiments, we used the n2c2 natural language processing research dataset, released by Harvard Medical School. The dataset is in the form of clinical notes that contain patient discharge summaries. Given the unbalancedness of the data and their small size, the experimental results indicate the advantage of the ensemble learning technique with respect to single classifier models. In particular, the ensemble learning technique has slightly improved the performances of single classification models but has greatly reduced the variance of predictions stabilizing the accuracies (i.e., the lower standard deviation in comparison with single classifiers). In real-life scenarios, our work can be employed to identify with high accuracy morbidity conditions of patients by feeding our tool with their current clinical notes. Moreover, other domains where classification is a common problem might benefit from our approach as well

TF-IDF vs word embeddings for morbidity identification in clinical notes: An initial study

Today, we are seeing an ever-increasing number of clinical notes that contain clinical results, images, and textual descriptions of patient's health state. All these data can be analyzed and employed to cater novel services that can help people and domain experts with their common healthcare tasks. However, many technologies such as Deep Learning and tools like Word Embeddings have started to be investigated only recently, and many challenges remain open when it comes to healthcare domain applications. To address these challenges, we propose the use of Deep Learning and Word Embeddings for identifying sixteen morbidity types within textual descriptions of clinical records. For this purpose, we have used a Deep Learning model based on Bidirectional Long-Short Term Memory (LSTM) layers which can exploit state-of-the-art vector representations of data such as Word Embeddings. We have employed pre-trained Word Embeddings namely GloVe and Word2Vec, and our own Word Embeddings trained on the target domain. Furthermore, we have compared the performances of the deep learning approaches against the traditional tf-idf using Support Vector Machine and Multilayer perceptron (our baselines). From the obtained results it seems that the latter outperform the combination of Deep Learning approaches using any word embeddings. Our preliminary results indicate that there are specific features that make the dataset biased in favour of traditional machine learning approaches

TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial Study

Today, we are seeing an ever-increasing number of clinical notes that contain clinical results, images, and textual descriptions of patient's health state. All these data can be analyzed and employed to cater novel services that can help people and domain experts with their common healthcare tasks. However, many technologies such as Deep Learning and tools like Word Embeddings have started to be investigated only recently, and many challenges remain open when it comes to healthcare domain applications. To address these challenges, we propose the use of Deep Learning and Word Embeddings for identifying sixteen morbidity types within textual descriptions of clinical records. For this purpose, we have used a Deep Learning model based on Bidirectional Long-Short Term Memory (LSTM) layers which can exploit state-of-the-art vector representations of data such as Word Embeddings. We have employed pre-trained Word Embeddings namely GloVe and Word2Vec, and our own Word Embeddings trained on the target domain. Furthermore, we have compared the performances of the deep learning approaches against the traditional tf-idf using Support Vector Machine and Multilayer perceptron (our baselines). From the obtained results it seems that the latter outperforms the combination of Deep Learning approaches using any word embeddings. Our preliminary results indicate that there are specific features that make the dataset biased in favour of traditional machine learning approaches.Comment: 12 pages, 2 figures, 2 tables, SmartPhil 2020-First Workshop on Smart Personal Health Interfaces, Associated to ACM IUI 202

Are Experts Needed? : On Human Evaluation of Counselling Reflection Generation

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A Probabilistic Logic Programming Event Calculus

We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of a LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.Comment: Accepted for publication in the Theory and Practice of Logic Programming (TPLP) journa

Analysis of distortion in pulse modulation converters for switching radio frequency power amplifiers

High-efficiency linear radio frequency (RF) power amplifiers are needed for today’s wireless communication systems. Switch mode techniques have the potential for high efficiency but require a pulse drive signal. The generation of pulse width modulated signals and pulse position modulated signals by sigma delta modulators can introduce unwanted spectral components. Third order and image components are the dominant distortions generated in the pulse position modulation circuit. The authors identify the cause of distortion and mathematically quantify its amplitude and frequency. In a single carrier environment, an increase in offset frequency increases the unwanted spectral components. Calculations, simulations and measurements show that offsets less than 1% of the carrier frequency are required to keep unwanted components 40 dB below the signal level. Simulations and measurements show that the effect on a multichannel orthogonal frequency division multiplexing (OFDM) system is less detrimental. Nonetheless, unacceptable noise increases of up to 20 dB are observed in odd harmonic channels when the transmission is not centred on the nominal carrier frequency