Multi-Classifier Fusion Strategy for Activity and Intent Recognition of Torso Movements

As assistive, wearable robotic devices are being developed to physically assist their users, it has become crucial to develop safe, reliable methods to coordinate the device with the intentions and motions of the wearer. This dissertation investigates the recognition of user intent during flexion and extension of the human torso in the sagittal plane to be used for control of an assistive exoskeleton for the human torso. A multi-sensor intent recognition approach is developed that combines information from surface electromyogram (sEMG) signals from the user’s muscles and inertial sensors mounted on the user’s body. Intent recognition is implemented by following a pattern classification approach, wherein a linear discriminant analysis (LDA) based method of pattern classification is utilized. This method of classification builds on a traditional LDA by utilizing multiple classifiers from multiple sensors that are combined together using a majority voting based classifier fusion scheme, to deliver improved classification performance. Additionally, there is a focus on identification of suitable features for classification. Extraction of features in the time, frequency and time-frequency domains is discussed. Wavelet transform methods are employed for targeted extraction of nonlinear time-frequency domain features, and the effectiveness of these features in improving classification performance is emphasized. Experimental results using sEMG and inertial signals recorded from human subjects, to evaluate the pattern classification and feature extraction methods are presented. Results show that a combined sensor approach that utilizes both inertial and sEMG data leads to a 70% improvement in classification performance. Results also show that the use of multiple time-frequency domain features in conjunction with majority voting based classifier-fusion leads to an additional 75% improvement in classification performance, with a best case of up to 97% accuracy in recognizing user intent. This research has provided an effective demonstration of leveraging nonlinear time-frequency domain features with linear methods of classification to deliver accurate and computationally efficient intent recognition. In addition, the research effort has also developed a library of features that can serve as a starting point for future efforts in classifying torso motions

UV-excited SnO2SnO_{2} nanowire based electronic nose

Unsere Atemluft ist täglichen Schwankungen ausgesetzt und die Marktnachfrage nach Sensoren, die die Luftqualität messen können, steigt rapide an. Ein großer Teil dieser Nachfrage kann mit Metall-Oxid Gas Sensoren bedient werden. Diese Art von Gassensoren hat jedoch einige Nachteile im Bezug auf Genauigkeit, Langzeitstabilität, Leistungsaufnahme und Selektivität. Auch fehlen großvolumige Anwendungsbeispiele auf dem Markt, die Metall-Oxid (MOX) Gassensoren einsetzen und dabei alle Systemanforderungen erfüllen. Diese Arbeit stellt die neueste Entwicklung der "KArlsruhe MIkro NAse", einer im Rahmen der EU Horizon 2020 Initiative namens SMOKESENSE entwickelten elektrischen Nase, vor und vergleicht diese mit dem aktuellen Stand der Technik für Metalloxid-Gassensoren. Es wird gezeigt, dass durch UV-Anregung der $SnO_{2}$-Nanodrähte ein geringerer Stromverbrauch sowie eine minimierte Siloxan-Kontaminierung im Vergleich zu klassischen MOX-Sensoren erzielt wird. Zudem lässt sich mittels Aerosol-Jet-Druck eine vereinfachte und kostengünstigere Herstellung der Sensoren realisieren. Um die Massenproduktionstauglichkeit für eine Anwendung als intelligenter Feuersensor sicherzustellen, wird der Wachstumsprozess der Nanodrähte optimiert. Außerdem wird ein neuartiges chemisches FET-ähnliches Sensorkonzept namens Chem-FET vorgestellt, das im Vergleich zu UV-KAMINA ein verbessertes Signal-Rausch-Verhältnis und eine schnellere Reaktionszeit bietet. Eine überwachte Lernmethode des Maschinellen Lernens basierend auf einer linearen Diskriminanzfunktion wird verwendet, um verschiedene Zielgerüche zu klassifizieren. In einer Anwendung als Feuersensor erwiesen sich die entwickelten Sensorprototypen als konkurrenzfähig. Zusätzlich werden Möglichkeiten aufgezeigt, das Sensorprinzip als Plattform für andere Anwendungsarten verwenden zu können. Während mit den vorgestellten Methoden die Leistung des Gesamtsystems optimiert werden konne, bleibt als Ausblick Verbesserungsbedarf in Bereichen, wie z. B. der Charakterisierung von Gerüchen und der Testmethodik für die Anwendung in hohen Stückzahlen

Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

Machine learning and audio processing : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, Auckland, New Zealand

In this thesis, we addressed two important theoretical issues in deep neural networks and clustering, respectively. Also, we developed a new approach for polyphonic sound event detection, which is one of the most important applications in the audio processing area. The developed three novel approaches are: (i) The Large Margin Recurrent Neural Network (LMRNN), which improves the discriminative ability of original Recurrent Neural Networks by introducing a large margin term into the widely used cross-entropy loss function. The developed large margin term utilises the large margin discriminative principle as a heuristic term to navigate the convergence process during training, which fully exploits the information from data labels by considering both target category and competing categories. (ii) The Robust Multi-View Continuous Subspace Clustering (RMVCSC) approach, which performs clustering on a common view-invariant subspace learned from all views. The clustering result and the common representation subspace are simultaneously optimised by a single continuous objective function. In the objective function, a robust estimator is used to automatically clip specious inter-cluster connections while maintaining convincing intra-cluster correspondences. Thus, the developed RMVCSC can untangle heavily mixed clusters without pre-setting the number of clusters. (iii) The novel polyphonic sound event detection approach based on Relational Recurrent Neural Network (RRNN), which utilises the relational reasoning ability of RRNNs to untangle the overlapping sound events across audio recordings. Different from previous works, which mixed and packed all historical information into a single common hidden memory vector, the developed approach allows historical information to interact with each other across an audio recording, which is effective and efficient in untangling the overlapping sound events. All three approaches are tested on widely used datasets and compared with recently published works. The experimental results have demonstrated the effectiveness and efficiency of the developed approaches