Ubiquitous emotion-aware computing

Emotions are a crucial element for personal and ubiquitous computing. What to sense and how to sense it, however, remain a challenge. This study explores the rare combination of speech, electrocardiogram, and a revised Self-Assessment Mannequin to assess people’s emotions. 40 people watched 30 International Affective Picture System pictures in either an office or a living-room environment. Additionally, their personality traits neuroticism and extroversion and demographic information (i.e., gender, nationality, and level of education) were recorded. The resulting data were analyzed using both basic emotion categories and the valence--arousal model, which enabled a comparison between both representations. The combination of heart rate variability and three speech measures (i.e., variability of the fundamental frequency of pitch (F0), intensity, and energy) explained 90% (p < .001) of the participants’ experienced valence--arousal, with 88% for valence and 99% for arousal (ps < .001). The six basic emotions could also be discriminated (p < .001), although the explained variance was much lower: 18–20%. Environment (or context), the personality trait neuroticism, and gender proved to be useful when a nuanced assessment of people’s emotions was needed. Taken together, this study provides a significant leap toward robust, generic, and ubiquitous emotion-aware computing

Advances in automated surgery skills evaluation

Training a surgeon to be skilled and competent to perform a given surgical procedure, is an important step in providing a high quality of care and reducing the risk of complications. Traditional surgical training is carried out by expert surgeons who observe and assess the trainees directly during a given procedure. However, these traditional training methods are time-consuming, subjective, costly, and do not offer an overall surgical expertise evaluation criterion. The solution for these subjective evaluation methods is a sensor-based methodology able to objectively assess the surgeon's skill level. The development and advances in sensor technologies enable capturing and studying the information obtained from complex surgery procedures. If the surgical activities that occur during a procedure are captured using a set of sensors, then the skill evaluation methodology can be defined as a motion and time series analysis problem. This work aims at developing machine learning approaches for automated surgical skill assessment based on hand motion analysis. Specifically, this work presents several contributions to the field of objective surgical techniques using multi-dimensional time series, such as 1) introduce a new distance measure for the surgical activities based on the alignment of two multi-dimensional time series, 2) develop an automated classification framework to identify the surgeon proficiency level using wrist worn sensors, 3) develop a classification technique to identify elementary surgical tasks: suturing, needle passing, and knot tying , 4) introduce a new surgemes mean feature reduction technique which help improve the machine learning algorithms, 5) develop a framework for surgical gesture classification by employing the mean feature reduction method, 6) design an unsupervised method to identify the surgemes in a given procedure.Includes bibliographical references

Gesture recognition with application in music arrangement

This thesis studies the interaction with music synthesis systems using hand gestures. Traditionally users of such systems were limited to input devices such as buttons, pedals, faders, and joysticks. The use of gestures allows the user to interact with the system in a more intuitive way. Without the constraint of input devices, the user can simultaneously control more elements within the music composition, thus increasing the level of the system's responsiveness to the musician's creative thoughts. A working system of this concept is implemented, employing computer vision and machine intelligence techniques to recognise the user's gestures.Dissertation (MSc)--University of Pretoria, 2006.Computer ScienceMScunrestricte

Deep learning for time series classification

Time series analysis is a field of data science which is interested in analyzing sequences of numerical values ordered in time. Time series are particularly interesting because they allow us to visualize and understand the evolution of a process over time. Their analysis can reveal trends, relationships and similarities across the data. There exists numerous fields containing data in the form of time series: health care (electrocardiogram, blood sugar, etc.), activity recognition, remote sensing, finance (stock market price), industry (sensors), etc. Time series classification consists of constructing algorithms dedicated to automatically label time series data. The sequential aspect of time series data requires the development of algorithms that are able to harness this temporal property, thus making the existing off-the-shelf machine learning models for traditional tabular data suboptimal for solving the underlying task. In this context, deep learning has emerged in recent years as one of the most effective methods for tackling the supervised classification task, particularly in the field of computer vision. The main objective of this thesis was to study and develop deep neural networks specifically constructed for the classification of time series data. We thus carried out the first large scale experimental study allowing us to compare the existing deep methods and to position them compared other non-deep learning based state-of-the-art methods. Subsequently, we made numerous contributions in this area, notably in the context of transfer learning, data augmentation, ensembling and adversarial attacks. Finally, we have also proposed a novel architecture, based on the famous Inception network (Google), which ranks among the most efficient to date.Comment: PhD thesi

A Deep Learning Approach to Classify Surgical Skill in Microsurgery Using Force Data from a Novel Sensorised Surgical Glove

Microsurgery serves as the foundation for numerous operative procedures. Given its highly technical nature, the assessment of surgical skill becomes an essential component of clinical practice and microsurgery education. The interaction forces between surgical tools and tissues play a pivotal role in surgical success, making them a valuable indicator of surgical skill. In this study, we employ six distinct deep learning architectures (LSTM, GRU, Bi-LSTM, CLDNN, TCN, Transformer) specifically designed for the classification of surgical skill levels. We use force data obtained from a novel sensorized surgical glove utilized during a microsurgical task. To enhance the performance of our models, we propose six data augmentation techniques. The proposed frameworks are accompanied by a comprehensive analysis, both quantitative and qualitative, including experiments conducted with two cross-validation schemes and interpretable visualizations of the network’s decision-making process. Our experimental results show that CLDNN and TCN are the top-performing models, achieving impressive accuracy rates of 96.16% and 97.45%, respectively. This not only underscores the effectiveness of our proposed architectures, but also serves as compelling evidence that the force data obtained through the sensorzsed surgical glove contains valuable information regarding surgical skill

Efficient tuning in supervised machine learning

The tuning of learning algorithm parameters has become more and more important during the last years. With the fast growth of computational power and available memory databases have grown dramatically. This is very challenging for the tuning of parameters arising in machine learning, since the training can become very time-consuming for large datasets. For this reason efficient tuning methods are required, which are able to improve the predictions of the learning algorithms. In this thesis we incorporate model-assisted optimization techniques, for performing efficient optimization on noisy datasets with very limited budgets. Under this umbrella we also combine learning algorithms with methods for feature construction and selection. We propose to integrate a variety of elements into the learning process. E.g., can tuning be helpful in learning tasks like time series regression using state-of-the-art machine learning algorithms? Are statistical methods capable to reduce noise e ffects? Can surrogate models like Kriging learn a reasonable mapping of the parameter landscape to the quality measures, or are they deteriorated by disturbing factors? Summarizing all these parts, we analyze if superior learning algorithms can be created, with a special focus on efficient runtimes. Besides the advantages of systematic tuning approaches, we also highlight possible obstacles and issues of tuning. Di fferent tuning methods are compared and the impact of their features are exposed. It is a goal of this work to give users insights into applying state-of-the-art learning algorithms profitably in practiceBundesministerium f ür Bildung und Forschung (Germany), Cologne University of Applied Sciences (Germany), Kind-Steinm uller-Stiftung (Gummersbach, Germany)Algorithms and the Foundations of Software technolog