An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression

Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This paper presents a new unsupervised approach for human activity recognition from raw acceleration data measured using inertial wearable sensors. The proposed method is based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context. The model is learned in an unsupervised framework using the Expectation-Maximization (EM) algorithm where no activity labels are needed. The proposed method takes into account the sequential appearance of the data. It is therefore adapted for the temporal acceleration data to accurately detect the activities. It allows both segmentation and classification of the human activities. Experimental results are provided to demonstrate the efficiency of the proposed approach with respect to standard supervised and unsupervised classification approache

Children’s Fitness and Quality of Movement

Introduction: Movement is essential to life and plays a key role in development throughout childhood. Movement can be assessed by its quantity and quality. Movement is important to measure as it can aid early intervention. Current research suggests that global levels of fitness are declining, with a lack of research surrounding children’s natural fitness levels as they get older. Quantity of movement is commonly studied, however quality is becoming increasingly popular. A clear understanding of the methods of technology used to measure quality of movement is important as understanding this area will aid in designing appropriate interventions.Methods: This thesis comprises of two experimental studies. Study one is a repeated measures design using previously collected Swanlinx data to investigate how components of children’s fitness change over a one-year period. Study two is a scoping review investigating the measurement of quality of movement with technology in the form of MEM’s devices, while aiming to gain clarity on the definition of quality.Results: Study one revealed that children’s fitness levels increase across a one-year period, in all components of fitness, except sit and reach. Boys performed significantly better in all fitness components, apart from sit and reach. Study two demonstrated the broad field that is included under the term of quality, showing clarity is needed in this area. A large number of devices, movements and populations are being observed, with multiple definitions of quality which is dependent on the metrics collected.Conclusion: Study one concludes that children’s fitness levels increase over one-year, with boys performing better than girls. This can be used to understand children’s natural fitness levels and aid future interventions in participation. Study two concludes that there are multiple ways to assess quality of movement however a clear definition of the quality should be stated, aiding comparison of quality

Enhanced tracking system based on micro inertial measurements unit to measure sensorimotor responses in pigeons

The ability to orientate and navigate is critically important for the survival of all migratory birds and other animals. Progress in understanding the mechanisms underlying these capabilities and, in particular, the importance of a sensitivity to the Earth’s magnetic field has, thus far, been constrained by the limited number of techniques available for the analysis of often complex behavioural responses. Methods used to track the movements of animals, such as birds, have varied depending on the degree of accuracy required. Most conventional approaches involve the use of a camera for recording and then measuring an animal's head movements in response to a variety of external stimuli, such as changes in magnetic fields. However, video tracking analysis (VTA) will generally only provide a 2D tracking of head angle. Moreover, such a video analysis can only provide information about movements when the head is in view of the camera. In order to overcome these limitations, the novel invention reported here utilises a lightweight (<10g) Inertial Measurement Unit (IMU), positioned on the head of a homing pigeon, which contains a sensor with tri-axial orthogonal accelerometers, gyroscopes, and magnetometers. This highly compact (20.3×12.7×3 mm) system, can be programmed and calibrated to provide measurements of the three rotational angles (roll, pitch and yaw) simultaneously, eliminating any drift, i.e. the movement of the pigeon's head is determined by detecting and estimating the directions of motion at all angles (even those outside the defined areas of tracking). Using an existing VTA approach as a baseline for comparison, it is demonstrated IMU technology can comprehensively track a pigeon’s normal head movements with greater precision and in all 3 axes

DEVELOPMENT OF PIEZOELECTRIC SENSORS AND METHODOLOGY FOR NONINVASIVE SIMULTANEOUS DETECTION OF MULTIPLE VITAL SIGNS

The activity of piezoelectric material linked the applied electric field with the strain generated that can be translated into geometrical variations. Flexible steel substrate exhibits fascinating mechanical properties which enable their integration into the emerging field of flexible microelectronics. This work presents an extended technique based on capacitance-voltage dependency to extract the geometrical variations in thin-film piezoelectric materials deposited on a flexible steel. A 50 μm flexible steel sheet has been sandwiched by two PZT film layers, each of 2.4 μm in thickness deposited by sputtering. An aluminum layer of 370 nm has been deposited above each PZT layer to form the electrical contact. The steel sheet represents the common electrode for both PZT structures. Gamry references 3000 analyzers were used to collect the capacitance-voltage measurements then estimating the piezoelectric charge constant. Experimental work has been validated by implementing the same method on a bulk piezoelectric film. Results have shown that the measured capacitance varies by 1% due to dielectric constant voltage dependency. On the other hand, 99% of capacitance variations depend on the change in physical dimensions of the sample via the piezoelectric effect. Further to that, this thesis explores the utilization of piezoelectric-based sensors to collect a corresponding representative signal from the chest surface. The subject typically needs to hold his or her breath to eliminate the respiration effect. This work further contributes to the extraction of the corresponding representative vital signs directly from the measured respiration signal. The contraction and expansion of the heart muscles, as well as the respiration activities, will induce a mechanical vibration across the chest wall. This vibration can be converted into an electrical output voltage via piezoelectric sensors. During breathing, the measured voltage signal is composed of the cardiac cycle activities modulated along with the respiratory cycle activity. The proposed technique employs the principles of piezoelectric and signal-processing methods to extract the corresponding signal of cardiac cycle activities from a breathing signal measured in real-time. All the results were validated step by step by a conventional apparatus, with good agreement observed

Wearable-based human activity recognition: from a healthcare application to a kinetic energy harvesting approach

Wearable technology is changing society by becoming an essential component of daily life. Human activity recognition (HAR) is one of the most prominent research areas where wearable devices play a key role. The first major contribution to the field in this dissertation is a smart physical work- load tracking system that combines wearable-based HAR and heart rate tracking. The proposed system employs a concept from ergonomics, the Frimat’s method, to compute the physical workload from heart rate measurements within a specified time window. This dissertation includes a case of study where tracking of an individual over the course of 20 days corroborates the ability of the system to assess adaptation to an exercise routine. The second and third contributions of this dissertation point to KEH in wearable environments. The second contribution is an energy logger for wrist-worn systems, with the purpose of tracking energy generation in KEH systems during daily activities. Thus, it is possible to determine if the harvested energy is enough to power a conventional wearable device. The proposed system computes the harvested energy using the characteristics of the objective load, which in this case is a battery charger. I carried out experiments with multiple subjects to examine the generation capabilities of a commercial harvester under the conditions of human motion. This study provides insights of the performance and limitations of kinetic harvesters as battery chargers. The third contribution is a KEH-based HAR system using deep learning, data augmentation and transfer learning to outperform existing classification approaches in the KEH domain. The proposed architecture comprises convolutional neural networks (CNN) and long short-term memory networks (LSTM), which has been demonstrated to outperform other architectures found in the literature. Since deep learning classifiers require large amounts of data, and KEH datasets are limited in size, this thesis also includes the proposal of three data augmentation methods to synthesize KEH signals simulating new users. Finally, transfer learning is employed to build a system that maintains performance independent of device location or the subject wearing the device.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic