Outlier detection is an essential part of modern systems. It is used to detect anomalies in behaviour or performance of systems or subjects, such as fall detection in smartwatches or voltage irregularity detection in batteries. This provides early indications of something of potential problems.

A part of outlier detection that is not often analysed is the performance of algorithms in environments with data from only one subject, versus environments with data from multiple subjects. This paper aims to answer the questions regarding the performance of Gaussian Mixture Models (GMM) and DBSCAN in these different environments. This paper focuses on time series data collected from consumer-grade wearables like smartwatches. In this paper, the outliers are defined manually, as the used data set did not contain predefined outliers. This research considers both outliers defined within the subject data, and the use of other subjects as outliers.

Results from this paper indicate that the amount of subjects in the environment is not the sole factor in the performance of these algorithms. Rather, it is a combination of the amount of subjects in the environment and the type of outlier to be detected. Results show that a GMM has difficulty distinguishing subjects that are similar when using another subject as outlier data. On average, DBSCAN outperforms a GMM in almost all cases, and DBSCAN is a lot more consistent in its performance than a GMM.

Advancements in the precision and accuracy of consumer-grade wearables, such as a Fitbit, have enabled the identification and therefore authentication of individuals based on their emitted heart frequencies using these wrist-worn devices. With this type of authentication, a password is essentially sent out every second. This makes it a perfect form of authentication in fields where constant authentication is crucial. However, not much is known about how different types of cardiac diagnosis (e.g. fit or obese) influence the accuracy of this type of authentication. In this paper, it will be shown how different types of cardiac diagnosis affect the accuracy of Deep Neural Networks to identify individuals by heart rate. This study is done with data obtained from 14 subjects, having different types of cardiac diagnosis. A deep neural network consisting of multiple convolutional layers is being used to conduct the experiments. It has been shown that subjects with a paroxysmal atrial fibrillation diagnosis improve the accuracy the most, compared to the reference (normal healthy) subjects. On the other hand, (very) fit subjects decrease the accuracy the most. Heart failure and obese subjects have a similar accuracy compared to reference subjects.

Smartwatches are equipped with sensors that allow continuous monitoring of physiological and physical activities, making them ideal sources of data for data analysis. However, accurately identifying individuals based on smartwatch data can be challenging due to the presence of outliers. Hence, outlier detection techniques play a crucial part in this context by identifying and handling these data points. Auto-encoders are one of the prominent ways to address outlier detection. Auto-encoders minimize a loss function to identify outlier samples. To explore the most optimal loss function for smartwatch data, this paper conducts a comparative analysis between three unsupervised loss functions, fused directional loss, mean square error, and regularized loss extracted from the current literature. The performance of three functions in personal identification is employed as the performance criteria due to the lack of outlier labels. The results indicate that the auto-encoder's performance in personal identification is slightly better than random guessing. The model struggled to effectively capture individual characteristics of the training data. This led to the outlier samples and non-outlier samples not being separable in the evaluation set. Consequently, the variation in the performance across and within a loss function was primarily influenced by the characteristics of the data rather than the model itself. Thus, the auto-encoder has limitations in personal identification, which led to an inconclusive comparison of the loss functions.

The aim of this paper is to complete the gap in the knowledge and experiment using as little as only the heart rate of some subjects to manage to successfully authorise them in some supposed system. The focus will be on the Gaussian Mixture model and the One Class Support Vector Machine, both outlier detectors, because most of the past research was focused on supervised models. Using these two, this paper will experiment with recognising intruders with models trained to distinguish one authorised person and multiple authorised persons. In the first case, multiple data processing methods and hyperparameters will be tested together and compared. In the second case, the goal will be to use and modify the best-found parameters of the first case to train models that are able to detect multiple persons as authorised. This time, there are two methods that this paper is going to look into and compare their performance: training one single model to detect all the subjects and training multiple models, one per subject. The most notable results are with one authorised person, with a score of 0.936, with two authorised, 0.88, and with 12 authorised, 0.713, when using the area under the curve metric.

Heart rate data and other data collected by consumer-grade wearable devices can give away quite useful information about the user. It can for example be used by machine learning algorithms such as Deep Neural Networks (DNN) to learn patterns about cardiovascular disease and fitness, or be used for identification. Heart rate patterns can also change quickly within the span of several months, which could make older heart rate data less useful when training a DNN. This paper shows that the DNN did indeed perform significantly worse when trying to identify people on older data compared to recent data. The accuracy calculated from the test set was 63.64% when trained on the most recently available training data, in comparison to 33.88% when trained on the least recent data which was more than 200 days older. When changing the recentness of training data only for a single user, there was also always an improvement in the accuracy of the model to identify that particular person. The accuracy to identify all users however did not necessarily increase, and sometimes even decreased. Using more data for training still outperforms using a smaller amount of samples of more recent data by slight margins, showing the trade-off between the recentness of data and the amount of data used for training. However, if fast training times are required, taking the most recent data windows can still lead to a similar performance as when training on all available data.