Human Gait Database for Normal Walk Collected by Smart Phone Accelerometer

The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints during two different sessions and record their gait data using two smartphones, one was attached to the right thigh and another one on the left side of the waist. This data is collected with the intention to be utilized by a deep learning-based method which requires enough time points. The metadata including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available

Recognizing Stereotyped Behavior in Children with Autism

This project works on helping in identifying and recognizing autistic children's stereotyped behaviors, which can help in diagnosing autism on children. The recognition accomplished by building a signal processing model that collects data from a smartwatch equipped with a gyroscope and accelerometer in order to produce a feature vector of 316 features. This feature vector is used to choose a predictive model with the highest accuracy, which is Ridge classifier in this project. The results show that those common stereotype behaviors could be recognized using the Ridge machine learning algorithm with overall average accuracy ranges between 98.7% to 99.5 %. For hand flapping, head banging, and running back and forth, the overall precision ranges between 98% to 100 %, overall recall ranges between 98% to 100 %, overall F1-score ranges between 98% to 100 % and overall macro, weighted and micro averages is 99 %. This Ridge classifier used to implement a real-time application developed on a smartphone (iPhone) to detect the stereotyped behaviors for autistic children who are wearing the smartwatch (Apple watch

Perspektivenorientierte Erkennung chirurgischer Aktivitäten im Operationssaal

Die Dissertation beschäftigt sich mit der automatischen Erkennung chirurgischer Aktivitäten im Operationssaal, welche einen wichtigen Bestandteil im automatischen chirurgischen Assistenzprozess darstellt. Die automatische Assistenz ist eine der wichtigen Entwicklungen bei der fortschreitenden Technisierung in der Chirurgie. Es werden Anforderungen an ein Erkennungssystem definiert sowie ein entsprechendes Erkennungsmodell entworfen und untersucht. Die Evaluation bedient sich simulierter chirurgischer Eingriffe mit hoher Realitätsnähe. Die Ergebnisse zeigen eine grundlegende Eignung des Modells für die automatische Aktivitätserkennung multipler Eingriffstypen. Mögliche Weiterentwicklungen könnten die vorgestellte Lösung weiter vorantreiben

Artificial Intelligence for Data Analysis and Signal Processing

Artificial intelligence, or AI, currently encompasses a huge variety of fields, from areas such as logical reasoning and perception, to specific tasks such as game playing, language processing, theorem proving, and diagnosing diseases. It is clear that systems with human-level intelligence (or even better) would have a huge impact on our everyday lives and on the future course of evolution, as it is already happening in many ways. In this research AI techniques have been introduced and applied in several clinical and real world scenarios, with particular focus on deep learning methods. A human gait identification system based on the analysis of inertial signals has been developed, leading to misclassification rates smaller than 0.15%. Advanced deep learning architectures have been also investigated to tackle the problem of atrial fibrillation detection from short length and noisy electrocardiographic signals. The results show a clear improvement provided by representation learning over a knowledge-based approach. Another important clinical challenge, both for the patient and on-board automatic alarm systems, is to detect with reasonable advance the patterns leading to risky situations, allowing the patient to take therapeutic decisions on the basis of future instead of current information. This problem has been specifically addressed for the prediction of critical hypo/hyperglycemic episodes from continuous glucose monitoring devices, carrying out a comparative analysis among the most successful methods for glucose event prediction. This dissertation also shows evidence of the benefits of learning algorithms for vehicular traffic anomaly detection, through the use of a statistical Bayesian framework, and for the optimization of video streaming user experience, implementing an intelligent adaptation engine for video streaming clients. The proposed solution explores the promising field of deep learning methods integrated with reinforcement learning schema, showing its benefits against other state of the art approaches. The great knowledge transfer capability of artificial intelligence methods and the benefits of representation learning systems stand out from this research, representing the common thread among all the presented research fields