Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition

A wide variety of applications like patient monitoring, rehabilitation sensing, sports and senior surveillance require a considerable amount of knowledge in recognizing physical activities of a person captured using sensors. The goal of human activity recognition is to identify human activities from a collection of observations based on the behavior of subjects and the surrounding circumstances. Movement is examined in psychology, biomechanics, artificial intelligence and neuroscience. To be specific, the availability of pervasive devices and the low cost to record movements with machine learning (ML) techniques for the automatic and quantitative analysis of movement have resulted in the growth of systems for rehabilitation monitoring, user authentication and medical diagnosis. The self-regulated detection of human activities from time-series smartphone sensor datasets is a growing study area in intelligent and smart healthcare. Deep learning (DL) techniques have shown enhancements compared to conventional ML methods in many fields, which include human activity recognition (HAR). This paper presents an improved wolf swarm optimization with deep learning based movement analysis and self-regulated human activity recognition (IWSODL-MAHAR) technique. The IWSODL-MAHAR method aimed to recognize various kinds of human activities. Since high dimensionality poses a major issue in HAR, the IWSO algorithm is applied as a dimensionality reduction technique. In addition, the IWSODL-MAHAR technique uses a hybrid DL model for activity recognition. To further improve the recognition performance, a Nadam optimizer is applied as a hyperparameter tuning technique. The experimental evaluation of the IWSODL-MAHAR approach is assessed on benchmark activity recognition data. The experimental outcomes outlined the supremacy of the IWSODL-MAHAR algorithm compared to recent models

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

Directing and orienting ICT healthcare solutions to address the needs of the aging population

Background: With an aging population, it is essential to maintain good health and autonomy for as long as possible. Instead of hospitalisation or institutionalisation, older people with chronic conditions can be assisted in their own home with numerous “smart” devices that support them in their activities of daily living, manage their medical conditions, and prevent fall incidents. Information and Communication Technology (ICT) solutions facilitate the monitoring and management of older people’s health to improve quality of life and physical activity with a decline in caregivers’ burden. Method: The aim of this paper was to conduct a systematic literature review to analyse the state of the art of ICT solutions for older people with chronic conditions, and the impact of these solutions on their quality of life from a biomedical perspective. Results: By analysing the literature on the available ICT proposals, it is shown that different approaches have been deployed by noticing that the more cross-interventions are merged then the better the results are, but there is still no evidence of the effects of ICT solutions on older people’s health outcomes. Furthermore, there are still unresolved ethical and legal issues. Conclusion: While there has been much research and development in healthcare ICT solutions for the aging population, ICT solutions still need significant development in order to be user-oriented, affordable, and to manage chronic conditions in the aging wider population

Cross-Domain Activity Recognition Using Shared Representation in Sensor Data

Existing models based on sensor data for human activity recognition are reporting state-of-the-art performances. Most of these models are conducted based on single-domain learning in which for each domain a model is required to be trained. However, the generation of adequate labelled data and a learning model for each domain separately is often time-consuming and computationally expensive. Moreover, the deployment of multiple domain-wise models is not scalable as it obscures domain distinctions, introduces extra computational costs, and limits the usefulness of training data. To mitigate this, we propose a multi-domain learning network to transfer knowledge across different but related domains and alleviate isolated learning paradigms using a shared representation. The proposed network consists of two identical causal convolutional sub-networks that are projected to a shared representation followed by a linear attention mechanism. The proposed network can be trained using the full training dataset of the source domain and a dataset of restricted size of the target training domain to reduce the need of large labelled training datasets. The network processes the source and target domains jointly to learn powerful and mutually complementary features to boost the performance in both domains. The proposed multi-domain learning network on six real-world sensor activity datasets outperforms the existing methods by applying only 50% of the labelled data. This confirms the efficacy of the proposed approach as a generic model to learn human activities from different but related domains in a joint effort, to reduce the number of required models and thus improve system efficiency

Digital phenotyping through multimodal, unobtrusive sensing

The growing adoption of multimodal wearable and mobile devices, such as smartphones and wrist-worn watches has generated an increase in the collection of physiological and behavioural data at scale. This digital phenotyping data enables researchers to make inferences regarding users’ physical and mental health at scale, for the first time. However, translating this data into actionable insights requires computational approaches that turn unlabelled, multimodal time-series sensor data into validated measures that can be interpreted at scale. This thesis describes the derivation of novel computational methods that leverage digital phenotyping data from wearable devices in large-scale populations to infer physical behaviours. These methods combine insights from signal processing, data mining and machine learning alongside domain knowledge in physical activity and sleep epidemiology. First, the inference of sleeping windows in free-living conditions through a heart rate sensing approach is explored. This algorithm is particularly valuable in the absence of ground truth or sleep diaries given its simplicity, adaptability and capacity for personalization. I then explore multistage sleep classification through combined movement and cardiac wearable sensing and machine learning. Further, I demonstrate that postural changes detected through wrist accelerometers can inform habitual behaviours and are valuable complements to traditional, intensity-based physical activity metrics. I then leverage the concomitant responses of heart rate to physical activity that can be captured through multimodal wearable sensors through a self-supervised training task. The resulting embeddings from this task are shown to be useful for the downstream classification of demographic factors, BMI, energy expenditure and cardiorespiratory fitness. Finally, I describe a deep learning model for the adaptive inference of cardiorespiratory fitness (VO2max) using wearable data in free living conditions. I demonstrate the robustness of the model in a large UK population and show the models’ adaptability by evaluating its performance in a subset of the population with repeated measures ~6 years after the original recordings. Together, this work increases the potential of multimodal wearable and mobile sensors for physical activity and behavioural inferences in population studies. In particular, this thesis showcases the potential of using wearable devices to make valuable physical activity, sleep and fitness inferences in large cohort studies. Given the nature of the data collected and the fact that most of this data is currently generated by commercial providers and not research institutes, laying the foundations for responsible data governance and ethical use of these technologies will be critical to building trust and enabling the development of the field of digital phenotyping.I was funded by GlaxoSmithKline and the Engineering and Physical Sciences Research Council. I was also supported by the Alan Turing Institute through their Enrichment Scheme

Sequential learning and shared representation for sensor-based human activity recognition

Human activity recognition based on sensor data has rapidly attracted considerable research attention due to its wide range of applications including senior monitoring, rehabilitation, and healthcare. These applications require accurate systems of human activity recognition to track and understand human behaviour. Yet, developing such accurate systems pose critical challenges and struggle to learn from temporal sequential sensor data due to the variations and complexity of human activities. The main challenges of developing human activity recognition are accuracy and robustness due to the diversity and similarity of human activities, skewed distribution of human activities, and also lack of a rich quantity of wellcurated human activity data. This thesis addresses these challenges by developing robust deep sequential learning models to boost the performance of human activity recognition and handle the imbalanced class problems as well as reduce the need for a large amount of annotated data. This thesis develops a set of new networks specifically designed for the challenges in building better HAR systems compared to the existing methods. First, this thesis proposes robust and sequential deep learning models to accurately recognise human activities and boost the performance of the human activity recognition systems against the current methods from smart home and wearable sensors collected data. The proposed methods integrate convolutional neural networks and different attention mechanisms to efficiently process human activity data and capture significant information for recognising human activities. Next, the thesis proposes methods to address the imbalanced class problems for human activity recognition systems. Joint learning of sequential deep learning algorithms, i.e., long short-term memory and convolutional neural networks is proposed to boost the performance of human activity recognition, particularly for infrequent human activities. In addition to that, also propose a data-level solution to address imbalanced class problems by extending the synthetic minority over-sampling technique (SMOTE) which we named (iSMOTE) to accurately label the generated synthetic samples. These methods have enhanced the results of the minority human activities and outperformed the current state-of-the-art methods. In this thesis, sequential deep learning networks are proposed to boost the performance of human activity recognition in addition to reducing the dependency for a rich quantity of well-curated human activity data by transfer learning techniques. A multi-domain learning network is proposed to process data from multi-domains, transfer knowledge across different but related domains of human activities and mitigate isolated learning paradigms using a shared representation. The advantage of the proposed method is firstly to reduce the need and effort for labelled data of the target domain. The proposed network uses the training data of the target domain with restricted size and the full training data of the source domain, yet provided better performance than using the full training data in a single domain setting. Secondly, the proposed method can be used for small datasets. Lastly, the proposed multidomain learning network reduces the training time by rendering a generic model for related domains compared to fitting a model for each domain separately. In addition, the thesis also proposes a self-supervised model to reduce the need for a considerable amount of annotated human activity data. The self-supervised method is pre-trained on the unlabeled data and fine-tuned on a small amount of labelled data for supervised learning. The proposed self-supervised pre-training network renders human activity representations that are semantically meaningful and provides a good initialization for supervised fine tuning. The developed network enhances the performance of human activity recognition in addition to minimizing the need for a considerable amount of labelled data. The proposed models are evaluated by multiple public and benchmark datasets of sensorbased human activities and compared with the existing state-of-the-art methods. The experimental results show that the proposed networks boost the performance of human activity recognition systems