Activity-driven detection of cognitive impairment using deep learning.

While life expectancy is on the rise all over the world, more people face health related problems such as cognitive decline. Cognitive impairment is a collective name for progressive brain syndromes which affect memory, cognition, behaviour and emotion. People suffering from cognitive impairment may lose their abilities to perform daily life activities and they get dependent on their caregivers. Although some medications can slow the progress of the disease, currently there is no way to stop its development. Sufferers may require special needs which increase the cost of care. Thus, detecting the indicators of cognitive decline before it gets worse would be very crucial. Current assessment methods mostly rely on queries from questionnaires or in-person examinations, which depend on recall of events that may poorly represent a person’s typical state. The aim in this thesis is to adapt deep learning techniques for analysing daily activities of elderly people and detecting abnormalities in the activity patterns. Recent studies suggest that indicators of cognitive decline can be observed in daily life activity patterns. The spatio-temporal and hierarchical relationship of activities and their intrinsic structures are important in the context of cognitive decline analysis. Existing studies treat each activity as an atomic unit and fail to capture the relationship among sub-activities. Also, existing studies rely on fixed length features to model activities, ignoring the granular level information coming from raw sensor activations. Moreover, there exists no daily activity dataset representing the behaviour of dementia sufferers because producing such datasets requires time and adequate experimental environment. Given these challenges, the present thesis addresses the following research questions: How can we cope with the scarcity of dataset reflecting on cognitive status of elderly people? How can activities be modelled taking into account their spatio-temporal neighbourhood and hierarchical information? How can we represent raw data to encode the granular level details? These research questions are addressed in the following way. Firstly, two methods are proposed to cope with the scarcity of data: (i) synthetic data generation and (ii) transfer learning adoption. Secondly, the activity recognition problem is emulated (i) as a sequence labelling problem to model spatio-temporal patterns. (ii) as a hierarchical learning problem to model sub-activities. (iii) as a graph labelling problem to encode granular level details. Thirdly, raw sensor measurements stemming from sequential data are used to model sensor activation relationships. The proposed methods are also compared against the state-of-art methods. The preliminary results obtained indicate that pro- posed data simulation and transfer learning approaches are useful to cope with the scarcity of data reflecting cognitive status of elderly people. Moreover, experiments show that the proposed deep learning methods are promising to detect abnormalities in the context of cognitive decline. Proposed methods are not only promising to detect abnormal behaviour at a fine-grained level, but some of them can also model activities hierarchically by taking sub-activities into account and then can detect abnormal behaviour occurring at granular levels

Human Gait Data Augmentation and Trajectory Prediction for Lower-Limb Rehabilitation Robot Control Using GANs and Attention Mechanism

To date, several alterations in the gait pattern can be treated through rehabilitative approaches and robot assisted therapy (RAT). Gait data and gait trajectories are essential in specific exoskeleton control strategies. Nevertheless, the scarcity of human gait data due to the high cost of data collection or privacy concerns can hinder the performance of controllers or models. This paper thus first creates a GANs-based (Generative Adversarial Networks) data augmentation method to generate synthetic human gait data while still retaining the dynamics of the real gait data. Then, both the real collected and the synthesized gait data are fed to our constructed two-stage attention model for gait trajectories prediction. The real human gait data are collected with the five healthy subjects recruited from an optical motion capture platform. Experimental results indicate that the created GANs-based data augmentation model can synthesize realistic-looking multi-dimensional human gait data. Also, the two-stage attention model performs better compared with the LSTM model; the attention mechanism shows a higher capacity of learning dependencies between the historical gait data to accurately predict the current values of the hip joint angles and knee joint angles in the gait trajectory. The predicted gait trajectories depending on the historical gait data can be further used for gait trajectory tracking strategies

Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia

A significant proportion of the population has become used to sharing private information on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates can depend on the privacy policy of large corporations. In an era of artificial intelligence, data mining, and cloud computing, is it necessary to share personal information with unidentified people? Our research shows that deep learning is possible using relatively low capacity computing. When applied, this demonstrates promising results in spatio-temporal positioning of subjects, in prediction of movement, and assessment of contextual risk. A private surveillance system is particularly suitable in the care of those who may be considered vulnerable

CodeMagic: Semi-Automatic Assignment of ICD-10-AM Codes to Patient Records

In this study, we present a recommendation system for semiautomatic assignment of ICD-10-AM codes to free-text patient records. Only expert annotators can assign codes to medical texts, and the lack of standardization of medical documentation and language specific problems make the assignment process even more challenging. Our system assigns a set of top k ICD codes for each document by exploiting the idea of bag-of-words and by using Lucene search engine and Borda Count voting schema. Before the code assignment task, we preprocess patient records to form query bags. Experiments on a set of clinical records show that promising results are possible for semiautomatic assignment of ICD codes

A Survey on Ambient Sensor-Based Abnormal Behaviour Detection for Elderly People in Healthcare

With advances in machine learning and ambient sensors as well as the emergence of ambient assisted living (AAL), modeling humans’ abnormal behaviour patterns has become an important assistive technology for the rising elderly population in recent decades. Abnormal behaviour observed from daily activities can be an indicator of the consequences of a disease that the resident might suffer from or of the occurrence of a hazardous incident. Therefore, tracking daily life activities and detecting abnormal behaviour are significant in managing health conditions in a smart environment. This paper provides a comprehensive and in-depth review, focusing on the techniques that profile activities of daily living (ADL) and detect abnormal behaviour for healthcare. In particular, we discuss the definitions and examples of abnormal behaviour/activity in the healthcare of elderly people. We also describe the public ground-truth datasets along with approaches applied to produce synthetic data when no real-world data are available. We identify and describe the key facets of abnormal behaviour detection in a smart environment, with a particular focus on the ambient sensor types, datasets, data representations, conventional and deep learning-based abnormal behaviour detection methods. Finally, the survey discusses the challenges and open questions, which would be beneficial for researchers in the field to address