The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

The aim of this study is to explore the usage of statistical learning methods on wearable devices and realize an experimental study for recognition of human activities by using smartwatch sensor data. To achieve this objective, mobile applications that run on smartwatch and smartphone were developed to gain training data and detect human activity momentarily; 500 pattern data were obtained with 4‐second intervals for each activity (walking, typing, stationary, running, standing, writing on board, brushing teeth, cleaning and writing). Created dataset was tested with five different statistical learning methods (Naive Bayes, k nearest neighbour (kNN), logistic regression, Bayesian network and multilayer perceptron) and their performances were compared

Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

Physical activity recognition (PAR) using wearable devices can provide valued information regarding an individual's degree of functional ability and lifestyle. In this regards, smartphone-based physical activity recognition is a well-studied area. Research on smartwatch-based PAR, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based PAR domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based PAR system for both personal and impersonal models. To further validate our hypothesis for both personal (The classifier is built using the data only from one specific user) and impersonal (The classifier is built using the data from every user except the one under study) models, we tested single subject validation process for smartwatch-based activity recognition.Comment: 15 pages, 2 figures, Accepted in CVC'1

Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Model

Smart devices, such as smartphones, smartwatches, etc., are examples of promising platforms for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities on these platforms due to interclass pattern similarities, which occur when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these devices. Therefore, apart from using information from the traditional sensors, these systems lack the contextual information to support automatic activity recognition. In this article, we explore environmental contexts, such as illumination (light conditions) and noise level, to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) learning models. The models performed sensor fusion by augmenting low-level sensor signals with rich contextual data to improve the models’ recognition accuracy and generalization. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used triaxial inertial sensing signals to train baseline models, while the second set of experiments combined the inertial signals with contextual information from environmental sensors. The obtained results demonstrate that contextual information, such as environmental noise level and light conditions using hybrid deep learning models, achieved better recognition accuracy than the traditional baseline activity recognition models without contextual information

Data Informed Health Simulation Modeling

Combining reliable data with dynamic models can enhance the understanding of health-related phenomena. Smartphone sensor data characterizing discrete states is often suitable for analysis with machine learning classifiers. For dynamic models with continuous states, high-velocity data also serves an important role in model parameterization and calibration. Particle filtering (PF), combined with dynamic models, can support accurate recurrent estimation of continuous system state. This thesis explored these and related ideas with several case studies. The first employed multivariate Hidden Markov models (HMMs) to identify smoking intervals, using time-series of smartphone-based sensor data. Findings demonstrated that multivariate HMMs can achieve notable accuracy in classifying smoking state, with performance being strongly elevated by appropriate data conditioning. Reflecting the advantages of dynamic simulation models, this thesis has contributed two applications of articulated dynamic models: An agent-based model (ABM) of smoking and E-Cigarette use and a hybrid multi-scale model of diabetes in pregnancy (DIP). The ABM of smoking and E-Cigarette use, informed by cross-sectional data, supports investigations of smoking behavior change in light of the influence of social networks and E-Cigarette use. The DIP model was evidenced by both longitudinal and cross-sectional data, and is notable for its use of interwoven ABM, system dynamics (SD), and discrete event simulation elements to explore the interaction of risk factors, coupled dynamics of glycemia regulation, and intervention tradeoffs to address the growing incidence of DIP in the Australia Capital Territory. The final study applied PF with an SD model of mosquito development to estimate the underlying Culex mosquito population using various direct observations, including time series of weather-related factors and mosquito trap counts. The results demonstrate the effectiveness of PF in regrounding the states and evolving model parameters based on incoming observations. Using PF in the context of automated model calibration allows optimization of the values of parameters to markedly reduce model discrepancy. Collectively, the thesis demonstrates how characteristics and availability of data can influence model structure and scope, how dynamic model structure directly affects the ways that data can be used, and how advanced analysis methods for calibration and filtering can enhance model accuracy and versatility

Sampling Strategies for Tackling Imbalanced Data in Human Activity Recognition

Human activity recognition (HAR) using wearable sensors is a topic that is being actively researched in machine learning. Smart, sensor-embedded devices, such as smartphones, fitness trackers, or smart watches that collect detailed data on movement, are widely available now. HAR may be applied in areas such as healthcare, physiotherapy, and fitness to assist users of these smart devices in their daily lives. However, one of the main challenges facing HAR, particularly when it is used in supervised learning, is how balanced data may be obtained for algorithm optimisation and testing. Because users engage in some activities more than others, e.g. walking more than running, HAR datasets are typically imbalanced. The lack of dataset representation from minority classes, therefore, hinders the ability of HAR classifiers to sufficiently capture new instances of those activities. Inspired by the concept of data fusion, this thesis will introduce three new hybrid sampling methods. Thus, the diversity of the synthesised samples will be enhanced by combining output from separate sampling methods into three hybrid approaches. The advantage of the hybrid method is that it provides diverse synthetic data that can increase the size of the training data from different sampling approaches. This leads to improvements in the generalisation of a learning activity recognition model. The first strategy, known as the (DBM), combines synthetic minority oversampling techniques (SMOTE) with Random_SMOTE, both of which are built around the k-nearest neighbours algorithm. The second technique, called the noise detection-based method (NDBM), combines Tomek links (SMOTE_Tomeklinks) and the modified synthetic minority oversampling technique (MSMOTE). The third approach, titled the cluster-based method (CBM), combines cluster-based synthetic oversampling (CBSO) and the proximity weighted synthetic oversampling technique (ProWSyn). The performance of the proposed hybrid methods is compared with existing methods using accelerometer data from three commonly used benchmark datasets. The results show that the DBM, NDBM and CBM can significantly reduce the impact of class imbalance and enhance F1 scores of the multilayer perceptron (MLP) by as much as 9 % to 20 % compared with their constituent sampling methods. Also, the Friedman statistical significance test was conducted to compare the effect of the different sampling methods. The test results confirm that the CBM is more effective than the other sampling approaches. This thesis also introduces a method based on the Wasserstein generative adversarial network (WGAN) for generating different types of data on human activity. The WGAN is more stable to train than a generative adversarial network (GAN) and this is due to the use of a stable metric, namely Wasserstein distance, to compare the similarity between the real data distribution with the generated data distribution. WGAN is a deep learning approach, and in contrast to the six existing sampling methods referred to previously, it can operate on raw sensor data as convolutional and recurrent layers can act as feature extractors. WGAN is used to generate raw sensor data to overcome the limitations of the traditional machine learning-based sampling methods that can only operate on extracted features. The synthetic data that is produced by WGAN is then used to oversample the imbalanced training data. This thesis demonstrates that this approach significantly enhances the learning ability of the convolutional neural network(CNN) by as much as 5 % to 6 % from imbalanced human activity datasets. This thesis concludes that the proposed sampling methods based on traditional machine learning are efficient when human activity training data is imbalanced and small. These methods are less complex to implement, require less human activity training data to produce synthetic data and fewer computational resources than the WGAN approach. The proposed WGAN method is effective at producing raw sensor data when a large quantity of human activity training data is available. Additionally, it is time-consuming to optimise the hyperparameters related to the WGAN architecture, which significantly impacts the performance of the method

From Cellular to Holistic: Development of Algorithms to Study Human Health and Diseases

The development of theoretical computational methods and their application has become widespread in the world today. In this dissertation, I present my work in the creation of models to detect and describe complex biological and health related problems. The first major part of my work centers around the creation and enhancement of methods to calculate protein structure and dynamics. To this end, substantial enhancement has been made to the software package REDCRAFT to better facilitate its usage in protein structure calculation. The enhancements have led to an overall increase in its ability to characterize proteins under difficult conditions such as high noise and low data density. Secondly, a database that allows for easy and comprehensive mining of protein structures has been created and deployed. We show preliminary results for its application to protein structure calculation. This database, among other applications, can be used to create input sets for computational models for prediction of protein structure. Lastly, I present my work on the creation of a theoretical model to describe discrete state protein dynamics. The results of this work can be used to describe many real-world dynamic systems. The second major part of my work centers around the application of machine learning techniques to create a system for the automated detection of smoking using accelerometer data from smartwatches. The first aspect of this work that will be presented is binary detection of smoking puffs. This model was then expanded to perform full cigarette session detection. Next, the model was reformulated to perform quantification of smoking (such as puff duration and the time between puffs). Lastly, a rotational matrix was derived to resolve ambiguities of smartwatches due to position of the watch on the wrist

Event-driven Middleware for Body and Ambient Sensor Applications

Continuing development of on-body and ambient sensors has led to a vast increase in sensor-based assistance and monitoring solutions. A growing range of modular sensors, and the necessity of running multiple applications on the sensor information, has led to an equally extensive increase in efforts for system development. In this work, we present an event-driven middleware for on-body and ambient sensor networks allowing multiple applications to define information types of their interest in a publish/subscribe manner. Incoming sensor data is hereby transformed into the required data representation which lifts the burden of adapting the application with respect to the connected sensors off the developer's shoulders. Furthermore, an unsupervised on-the-fly reloading of transformation rules from a remote server allows the system's adaptation to future applications and sensors at run-time as well as reducing the number of connected sensors. Open communication channels distribute sensor information to all interested applications. In addition to that, application-specific event channels are introduced that provide tailor-made information retrieval as well as control over the dissemination of critical information. The system is evaluated based on an Android implementation with transformation rules implemented as OSGi bundles that are retrieved from a remote web server. Evaluation shows a low impact of running the middleware and the transformation rules on a phone and highlights the reduced energy consumption by having fewer sensors serving multiple applications. It also points out the behavior and limits of the open and application-specific event channels with respect to CPU utilization, delivery ratio, and memory usage. In addition to the middleware approach, four (preventive) health care applications are presented. They take advantage of the mediation between sensors and applications and highlight the system's capabilities. By connecting body sensors for monitoring physical and physiological parameters as well as ambient sensors for retrieving information about user presence and interactions with the environment, full-fledged health monitoring examples for monitoring a user throughout the day are presented. Vital parameters are gathered from commercially available biosensors and the mediator device running both the middleware and the application is an off-the-shelf smart phone. For gaining information about a user's physical activity, custom-built body and ambient sensors are presented and deployed