Linear Gaussian exploratory tools such as principal component analysis (PCA) and factor analysis (FA) are widely used for exploratory analysis, pre-processing, data visualization, and related tasks. Because the linear-Gaussian assumption is restrictive, for very high dimensional problems, they have been replaced by robust, sparse extensions or more flexible discrete-continuous latent feature models. Discrete-continuous latent feature models specify a dictionary of features dependent on subsets of the data and then infer the likelihood that each data point shares any of these features. This is often achieved using rich-get-richer assumptions about the feature allocation process where the dictionary tries to couple the feature frequency with the portion of total variance that it explains. In this work, we propose an alternative approach that allows for better control over the feature to data point allocation. This new approach is based on two-parameter discrete distribution models which decouple feature sparsity and dictionary size, hence capturing both common and rare features in a parsimonious way. The new framework is used to derive a novel adaptive variant of factor analysis (aFA), as well as an adaptive probabilistic principal component analysis (aPPCA) capable of flexible structure discovery and dimensionality reduction in a wide variety of scenarios. We derive both standard Gibbs sampling, as well as efficient expectation-maximisation inference approximations converging orders of magnitude faster, to a reasonable point estimate solution. The utility of the proposed aPPCA and aFA models is demonstrated on standard tasks such as feature learning, data visualization, and data whitening. We show that aPPCA and aFA can extract interpretable, high-level features for raw MNIST or COLI-20 images, or when applied to the analysis of autoencoder features. We also demonstrate that replacing common PCA pre-processing pipelines in the analysis of functional magnetic resonance imaging (fMRI) data with aPPCA, leads to more robust and better-localised blind source separation of neural activity.

Passive monitoring in daily life may provide valuable insights into a person's health throughout the day. Wearable sensor devices play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflect multiple health and behavior-related factors together. This creates the need for a structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of many different movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free-living using accelerometers, we present an unsupervised probabilistic framework designed to segment signals into differing gait and non-gait patterns. We evaluate the approach using a new video-referenced dataset including 25 PD patients with motor fluctuations and 25 age-matched controls, performing unscripted daily living activities in and around their own houses. Using this dataset, we demonstrate the framework's ability to detect gait and predict medication induced fluctuations in PD patients based on free-living gait. We show that our approach is robust to varying sensor locations, including the wrist, ankle, trouser pocket and lower back.

The performance of voice-based systems for remote monitoring of Parkinson's disease is highly dependent on the degree of adherence of the recordings to the test protocols, which probe for specific symptoms. Identifying segments of the signal that adhere to the protocol assumptions is typically performed manually by experts. This process is costly, time consuming, and often infeasible for large-scale data sets. In this paper, we propose a method to automatically identify the segments of signals that violate the test protocol with a high accuracy. In our approach, the signal is first split into variable duration segments by fitting an infinite hidden Markov model (iHMM) to the frames of the signals in the mel-frequency cepstral domain. The complexity of the iHMM is capable of growing jointly with the data allowing us to infer a potentially large (asymptotically infinite) number of different phenomena segmented into different hidden states. Then, we identify the segments that adhere to the test protocol by applying a multinomial naive Bayes classifier to the state indicators of segments. The experimental results show that even by using a small amount of training data, we can achieve around 96% accuracy in identifying short-term protocol violations with a 0.2 s resolution.

Passive infrared sensors have widespread use in many applications, including motion detectors for alarms, lighting systems and hand dryers. Combinations of multiple PIR sensors have also been used to count the number of humans passing through doorways. In this paper, we demonstrate the potential of the PIR sensor as a tool for occupancy estimation inside of a monitored environment. Our approach shows how flexible nonparametric machine learning algorithms extract useful information about the occupancy from a single PIR sensor. The approach allows us to understand and make use of the motion patterns generated by people within the monitored environment. The proposed counting system uses information about those patterns to provide an accurate estimate of room occupancy which can be updated every 30 seconds. The system was successfully tested on data from more than 50 real office meetings consisting of at most 14 room occupants.