Modelling Activities of Daily Living Using Local Interpretable Model-Agnostic Explanation Algorithm

The use of Artificial Intelligence (AI) in healthcare, particularly in recognising anomalous behaviour during Activities of Daily Living (ADLs), is useful for supporting independent living. Transparency and interpretability of ADLs can play a vital role in decision-making processes, particularly in healthcare sectors. This work intends to offer additional information to AI-based prediction of ADLs through the use of Local Interpretable Model-agnostic Explanations (LIME). In this study, 5,125 low resolution thermal images gleaned from ADLs in a laboratory environment which mimics a smart home were clustered and analysed using Data Mining software and AI algorithms respectively. Results indicated that LIME presented saliency maps of ADLs in diverse scenarios such as ‘Making Tea’ and ‘Sitting Down’ to consume it. Further work will seek to fine-tune the models for better accurac

Human Activity Recognition: A Comparison of Machine Learning Approaches

This study aims to investigate the performance of Machine Learning (ML) techniques used in Human Activity Recognition (HAR). Techniques considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Decision Tree with entropy, Random Forest, Gradient Boosting Decision Tree, and NGBoost algorithm. Following the activity recognition chain model for preprocessing, segmentation, feature extraction, and classification of human activities, we evaluate these ML techniques against classification performance metrics such as accuracy, precision, recall, F1 score, support, and run time on multiple HAR datasets. The findings highlight the importance to tailor the selection of ML technique based on the specific HAR requirements and the characteristics of the associated HAR dataset. Overall, this research helps in understanding the merits and shortcomings of ML techniques and guides the applicability of different ML techniques to various HAR datasets

SmartWall: Novel RFID-enabled Ambient Human Activity Recognition using Machine Learning for Unobtrusive Health Monitoring

YesHuman activity recognition from sensor readings have proved to be an effective approach in pervasive computing for smart healthcare. Recent approaches to ambient assisted living (AAL) within a home or community setting offers people the prospect of more individually-focused care and improved quality of living. However, most of the available AAL systems are often limited by computational cost. In this paper, a simple, novel non-wearable human activity classification framework using the multivariate Gaussian is proposed. The classification framework augments prior information from the passive RFID tags to obtain more detailed activity profiling. The proposed algorithm based on multivariate Gaussian via maximum likelihood estimation is used to learn the features of the human activity model. Twelve sequential and concurrent experimental evaluations are conducted in a mock apartment environment. The sampled activities are predicted using a new dataset of the same activity and high prediction accuracy is established. The proposed framework suits well for the single and multi-dwelling environment and offers pervasive sensing environment for both patients and carers.Tertiary Education Trust Fund of Federal Government of Nigeria and by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement H2020-MSCA-ITN-2016 SECRET-72242

Physical Activity Recognition and Identification System

Background: It is well-established that physical activity is beneficial to health. It is less known how the characteristics of physical activity impact health independently of total amount. This is due to the inability to measure these characteristics in an objective way that can be applied to large population groups. Accelerometry allows for objective monitoring of physical activity but is currently unable to identify type of physical activity accurately. Methods: This thesis details the creation of an activity classifier that can identify type from accelerometer data. The current research in activity classification was reviewed and methodological challenges were identified. The main challenge was the inability of classifiers to generalize to unseen data. Creating methods to mitigate this lack of generalisation represents the bulk of this thesis. Using the review, a classification pipeline was synthesised, representing the sequence of steps that all activity classifiers use. 1. Determination of device location and setting (Chapter 4) 2. Pre-processing (Chapter 5) 3. Segmenting into windows (Chapters 6) 4. Extracting features (Chapters 7,8) 5. Creating the classifier (Chapter 9) 6. Post-processing (Chapter 5) For each of these steps, methods were created and tested that allowed for a high level of generalisability without sacrificing overall performance. Results: The work in this thesis results in an activity classifier that had a good ability to generalize to unseen data. The classifier achieved an F1-score of 0.916 and 0.826 on data similar to its training data, which is statistically equivalent to the performance of current state of the art models (0.898, 0.765). On data dissimilar to its training data, the classifier achieved a significantly higher performance than current state of the art methods (0.759, 0.897 versus 0.352, 0.415). This shows that the classifier created in this work has a significantly greater ability to generalise to unseen data than current methods. Conclusion: This thesis details the creation of an activity classifier that allows for an improved ability to generalize to unseen data, thus allowing for identification of type from acceleration data. This should allow for more detailed investigation into the specific health effects of type in large population studies utilising accelerometers