CDBMGCIG: Design of a Cross-Domain Bioinspired Model for identification of Gait Components via Iterated GANs

This Gait identification assists in recognition of human body components from temporal image sequences. Such components consist of connected-body entities including head, upper body, lower body regions. Existing Gait recognition models use deep learning methods including variants of Convolutional Neural Networks (CNNs), Q-Learning, etc. But these methods are either highly complex, or do not perform well under complex background conditions. Moreover, most of these models are validated on a specific environmental condition, and cannot be scaled for general-purpose deployments. To overcome these issues, this text proposes design of a novel cross-domain bioinspired model for identification of gait components via Iterated Generative Adversarial Networks (IGANs). The proposed model initially extracts multidomain pixel-level feature sets from different images. These include frequency components via Fourier analysis, entropy components via Cosine analysis, spatial components via Gabor analysis, and window-based components via Wavelet &Convolutional analysis. These feature sets are processed via a Grey Wolf Optimization (GWO) Model, which assists in identification of high-density & highly variant features for different gait components. These features are classified via an iterated GAN, which comprises of Generator & Discriminator ssModels that assist in evaluating connected body components. These operations generate component-level scores that assist in identification of gait from complex background images. Due to which, the proposed model was observed to achieve 9.5% higher accuracy, 3.4% higher precision, and 2.9% higher recall than existing gait identification methods. The model also uses iterative learning, due to which its accuracy is incrementally improved w.r.t. number of evaluated image sets

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms

Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG system was used to acquire EEG signals from 13 healthy adults. Participants performed walking trials for four different stable and unstable conditions: (i) normal walking, (ii) normal walking with medial-lateral perturbation (MLP), (iii) normal walking with dual-tasking (Stroop), (iv) normal walking with center of mass visual feedback. Digital biomarkers were extracted using wavelet energy and entropies from the EEG signals. Algorithms like the ChronoNet, SVM, Random Forest, gradient boosting and recurrent neural networks (LSTM) could classify with 67 to 82% accuracy. The classification results show that it is possible to accurately classify different gait patterns (from stable to unstable) using EEG-based digital biomarkers. This study develops various machine-learning-based classification models using EEG datasets with potential applications in detecting unsteady gait neural signals and intervening by preventing falls and injuries

Intensifying the Security of Multiomodal Biometric Authentication System using Watermarking

In Multimodal biometrics system two or more biometric attributes are combined which makes it far more secure than unimodal system as it nullifies all the vulnerabilities of it. But with the prompt ontogenesis of information technology, even the biometric data is not secure. There is one such technique that is implemented to secure the biometric data from inadvertent or deliberate attacks is known as Digital watermarking. This paper postulate an approach that is devise in both the directions of enlarging the security through watermarking technique and improving the efficiency of biometric identification system by going multimodal. Three biometric traits are consider in this paper two of them are physical traits i.e. ; face, fingerprint and one is behavioral trait (signature).The biometric traits are initially metamorphose using Discrete Wavelet and Discrete Cosine Transformation and then watermarked using Singular Value Decomposition. Scheme depiction and presented results rationalize the effectiveness of the scheme

A pervasive body sensor network for monitoring post-operative recovery

Over the past decade, miniaturisation and cost reduction brought about by the semiconductor industry has led to computers smaller in size than a pin head, powerful enough to carry out the processing required, and affordable enough to be disposable. Similar technological advances in wireless communication, sensor design, and energy storage have resulted in the development of wireless “Body Sensor Network (BSN) platforms comprising of tiny integrated micro sensors with onboard processing and wireless data transfer capability, offering the prospect of pervasive and continuous home health monitoring. In surgery, the reduced trauma of minimally invasive interventions combined with initiatives to reduce length of hospital stay and a socioeconomic drive to reduce hospitalisation costs, have all resulted in a trend towards earlier discharge from hospital. There is now a real need for objective, pervasive, and continuous post-operative home recovery monitoring systems. Surgical recovery is a multi-faceted and dynamic process involving biological, physiological, functional, and psychological components. Functional recovery (physical independence, activities of daily living, and mobility) is recognised as a good global indicator of a patient’s post-operative course, but has traditionally been difficult to objectively quantify. This thesis outlines the development of a pervasive wireless BSN system to objectively monitor the functional recovery of post-operative patients at home. Biomechanical markers were identified as surrogate measures for activities of daily living and mobility impairment, and an ear-worn activity recognition (e-AR) sensor containing a three-axis accelerometer and a pulse oximeter was used to collect this data. A simulated home environment was created to test a Bayesian classifier framework with multivariate Gaussians to model activity classes. A real-time activity index was used to provide information on the intensity of activity being performed. Mobility impairment was simulated with bracing systems and a multiresolution wavelet analysis and margin-based feature selection framework was used to detect impaired mobility. The e-AR sensor was tested in a home environment before its clinical use in monitoring post-operative home recovery of real patients who have undergone surgery. Such a system may eventually form part of an objective pervasive home recovery monitoring system tailored to the needs of today’s post-operative patient.Open acces