Human Activity Recognition (HAR) is widely applied on wearable devices in our daily lives. However, acquiring high-quality wearable sensor data set with ground-truths is challenging due to the high cost in collecting data and necessity of domain experts. In order to achieve generalization from limited data, we study augmentation-based Self-Supervised Learning (SSL) for data from wearable devices. However, there is an issue in one of the most popular SSL approaches, contrastive learning: it is sensitive to the choice of data augmentations. To resolve this, we first propose to combine contrastive learning with generative learning, which is robust to augmentations. Second, we propose an automatic augmentation policy search method to discover the most promising augmentation policy. We empirically verify our approaches on three public HAR datasets. Experimental results show that our proposed SSL approach is robust to augmentations, and delivers higher accuracy than contrastive learning. Additionally, with the searched augmentation policy we are able to further improve the accuracy of HAR task.

Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low. This work proposes REPresentation And INstance Transfer (REPAINT) algorithm for knowledge transfer in deep reinforcement learning. REPAINT not only transfers the representation of a pre-trained teacher policy in the on-policy learning, but also uses an advantage-based experience selection approach to transfer useful samples collected following the teacher policy in the off-policy learning. Our experimental results on several benchmark tasks show that REPAINT significantly reduces the total training time in generic cases of task similarity. In particular, when the source tasks are dissimilar to, or sub-tasks of, the target tasks, REPAINT outperforms other baselines in both training-time reduction and asymptotic performance of return scores.

The growing availability of eye-gaze tracking technology has allowed for its employment in a wide variety of applications, one of which is the objective diagnosis and monitoring of neuropsychiatric disorders from features of attentional bias extracted from visual scanning patterns. Current techniques in this field are largely comprised of non-generalizable methodologies that rely on domain expertise and study-specific assumptions. In this paper, we present a general, data-driven, end-to-end framework that extracts relevant features of attentional bias from visual scanning behaviour and uses these features to classify between subject groups with standard machine learning techniques. The general framework uses visual scanning data from free-viewing tasks. In these tasks, subjects look at sets of slides with several thematic images while their visual scanning patterns (sets of ordered fixations) are monitored by an eye-tracking system. Subjects' fixations are encoded into relative visual attention maps (RVAMs), and two data-driven methods are proposed to segment regions of interests (ROIs) from RVAMs: 1) using group average RVAMs, and 2) using differences of group average RVAMs. Relative fixation times within the segmented ROIs are then used as input features for a vanilla multilayered perceptron to classify between patient groups. The methods were evaluated on data from two studies: an anorexia nervosa (AN)/healthy controls study (AN study) with 37 subjects, and a bipolar disorder (BD)/major depressive disorder (MDD) study (BD-MDD study) with 73 subjects. Using leave-one-subject-out cross validation, the general methods achieved an area under the receiver operating curve (AUROC) score of 0.935 for the AN study and 0.888 for the BD-MDD study, the latter of which exceeds the performance of the state-of-the-art analysis model designed specifically for the BD-MDD study, which had an AUROC of 0.879. The results validate the proposed framework's efficacy as a generalizable, standard baseline for analyzing visual scanning data.

Bipolar Disorder (BD) and Major Depressive Disorder (MDD) are two common and debilitating mood disorders. Misdiagnosing BD as MDD is relatively common and the introduction of markers to improve diagnostic accuracy early in the course of the illness has been identified as one of the top unmet needs in the field. In this paper, we present novel methods to differentiate between BD and MDD patients. The methods use deep learning techniques to quantify differences between visual scanning patterns of BD and MDD patients. In the methods, visual scanning patterns that are described by ordered sequences of fixations on emotional faces are encoded into a lower dimensional space and are fed into a long-short term memory recurrent neural network (RNN). Fixation sequences are encoded by three different methods: 1) using semantic regions of interests (RoIs) that are manually defined by experts, 2) using semi-automatically defined grids of RoIs, or 3) using a convolutional neural network (CNN) to automatically extract visual features from saliency maps. Using data from 47 patients with MDD and 26 patients with BD we showed that using semantic RoIs, the RNN improved the performance of a baseline classifier from an AUC of 0.603 to an AUC of 0.878. Similarly using grid RoIs, the RNN improved the performance of a baseline classifier from an AUC of 0.450 to an AUC of 0.828. The classifier that automatically extracted visual features from saliency maps (a long recurrent convolutional network that is fully data-driven) had an AUC of 0.879. The results of the study suggest that by using RNNs to learn differences between fixation sequences the diagnosis of individual patients with BD or MDD can be disambiguated with high accuracy. Moreover, by using saliency maps and CNN to encode the fixation sequences the method can be fully automated and achieve high accuracy without relying on user expertise and/or manual labelling. When compared with other markers, the performance of the class of classifiers that was introduced in this paper is better than that of detectors that use differences in neural structures, neural activity or cortical hemodynamics to differentiate between BD and MDD patients. The novel use of RNNs to quantify differences between fixation sequences of patients with mood disorders can be easily generalized to studies of other neuropsychological disorders and to other fields such as psychology and advertising.

Assessment of apathy in patients with Alzheimer's disease (AD) relies heavily on interviews with caregivers and patients, which can be ambiguous and time consuming. More precise and objective methods of evaluation can better inform treatment decisions. In this study, visual scanning behaviours (VSBs) on emotional and non-emotional stimuli were used to detect apathy in patients with AD. Forty-eight AD patients participated in the study. Sixteen of the patients were apathetic. Patients looked at 48 slides with non-emotional images and 32 slides with emotional images. We described two methods that use recurrent neural networks (RNNs) to learn differences between the VSBs of apathetic and non-apathetic AD patients. Method 1 uses two separate RNNs to learn group differences between visual scanning sequences on emotional and non-emotional stimuli. The outputs of the RNNs are then combined and used by a logistic regression classifier to characterise patients as either apathetic or non-apathetic. Method 1 achieved an AUC gain of 0.074 compared to a previously presented handcrafted feature method of detecting emotional blunting (AUC handcrafted = 0.646). Method 2 assumes that each individual's "style of scanning" (stereotypical eye movements) is independent of the content of the visual stimuli and uses the "style of scanning" to normalise the individual's VSBs on emotional and non-emotional stimuli. Method 2 uses RNNs in a sequence-to-sequence configuration to learn the individual's "style of scanning". The trained model is then used to create vector representations that contain information on the individual's "style of scanning" (content independent) and her/his VSBs (content dependent) on emotional and non-emotional stimuli. The distance between these vector representations is used by a logistic regression classifier to characterise patients as either apathetic or non-apathetic. Using Method 2 the AUC of the classifier improved to 0.814. The results presented suggest that using RNNs to analyse differences between VSBs on emotional and non-emotional stimuli (a measure of emotional blunting) can improve objective detection of apathy in individual patients with AD.

Post-concussion syndrome (PCS) is associated with incomplete recovery following a mild traumatic brain injury (mTBI). Currently, there is no biomarker to diagnose post-concussion syndrome. Although microstructural damage to white matter tracts is postulated and reported in some studies, there is no diagnostic biomarker available. In this paper, we present preliminary results of a novel and simple-to-administer eye-tracking paradigm that relates visual scanning behaviour to microstructural integrity of the corpus callosum (CC). The novel paradigm is based on the Matching Familiar Figures Test (MFFT). In the MFFT test, a series of slides each containing a familiar standard figure and six variants are presented. Only one of the variants is identical to the standard figure and subjects are instructed to select the matched variant. The normalised number of transitions to the standard figure prior to the first selection (NNTP) is used as an indirect measure of the microstructural integrity of the CC. Microstructural integrity was evaluated by measuring the fractional anisotropy (FA) of the CC derived from tract based spatial statistics on diffusion tensor imaging (DTI). Fifty-five patients with PCS completed the visual scanning paradigm and magnetic resonance imaging. Significant inverse correlation was found between the NNTP and the FA of the CC (r= -0.463, p<;0.001). Mediation analysis showed that damage to the CC could affect NNTP directly or through pathways that affect attention, somatic and cognitive symptoms. The results suggest that visual scanning behaviour during the MFFT is a multifactorial cognitive and behavioural process that can provide a surrogate marker of the structural integrity of the corpus callosum.