Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

On recognition of gestures arising in flight deck officer (FDO) training

This thesis presents an on-line recognition machine RM for the continuous and isolated recognition of dynamic and static gestures that arise in Flight Deck Officer (FDO) training. This thesis considers 18 distinct and commonly used dynamic and static gestures of FDO. Tracker and computer vision based systems are used to acquire the gestures. The recognition machine is based on the generic pattern recognition framework. The gestures are represented as templates using summary statistics. The proposed recognition algorithm exploits temporal and spatial characteristics of the gestures via dynamic programming and Markovian process. The algorithm predicts the correspond-ing index of incremental input data in the templates in an on-line mode. Accumulated consistency in the sequence of prediction provides a similarity measurement (Score) between input data and the templates. Having estimated Score, some heuristics are employed to control the declaration in the final stages. The recognition machine addresses general gesture recognition issues: to recognize real time and dynamic gesture, no starting/end point and inter-intra personal tem-poral and spatial variance. The first two issues and temporal variance are addressed by the proposed algorithm. The spatial invariance is addressed by introducing inde-pendent units to construct gesture models. An important aspect of the algorithm is that it provides an intuitive mechanism for automatic detection of start/end frames of continuous gestures. The algorithm has the additional advantage of providing timely feedback for training purposes. In this thesis, we consider isolated and continuous gestures. The performance of RM is evaluated using six datasets - artificial (W_TTest), hand motion (Yang, Perrotta), Gesture Panel and FDO (tracker, vision). The Hidden Markov Model (HMM) and Dynamic Time Warping (DTW) are used to compare RM's results. Various data analyses techniques are deployed to reveal the complexity and inter similarity of the datasets before experiments are conducted. In the isolated recogni-tion experiments, the recognition machine obtains comparable results with HMM and outperforms DTW. In the continuous experiments, RM surpasses HMM in terms of sentence and word recognition. In addition to these experiments, a multilayer per-ceptron neural network (MLPNN) is introduced for the prediction process of RM to validate modularity of RM. The overall conclusion of the thesis is that, RM achieves comparable results which are in agreement with HMM and DTW. Furthermore, the recognition machine pro-vides more reliable and accurate recognition in the case of missing and noisy data. The recognition machine addresses some common limitations of these algorithms and general temporal pattern recognition in the context of FDO training. The recognition algorithm is thus suited for on-line recognition.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Recognising Complex Mental States from Naturalistic Human-Computer Interactions

New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer