A framework for unsupervised change detection in activity recognition.

Purpose - This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is deployed for recognizing new unseen activities without access to the ground truth. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the recognition model without explicit detection of changes in the model performance. Design/methodology/approach - The approach determines the variation between reference activity data belonging to different classes and newly classified unseen data. If there is coherency between the data, it means the model is correctly classifying the instances; otherwise, a significant variation indicates wrong instances are being classified to different classes. Thus, the approach is formulated as a two-level architectural framework comprising of the off-line phase and the online phase. The off-line phase extracts of Shewart Chart change parameters from the training data set. The online phase performs classification of new samples and the detection of the changes in each class of activity present in the data set by using the change parameters computed earlier. Findings - The approach is evaluated using a real activity-recognition data set. The results show that there are consistent detections that correlate with the error rate of the model. Originality/value - The developed approach does not use ground truth to detect classifier performance degradation. Rather, it uses a data discrimination method and a base classifier to detect the changes by using the parameters computed from the reference data of each class to discriminate outliers in the new data being classified to the same class. The approach is the first, to the best of the authors' knowledge, that addresses the problem of detecting within-user and cross-user variations that lead to concept drift in activity recognition. The approach is also the first to use statistical process control method for change detection in activity recognition, with a robust integrated framework that seamlessly detects variations in the underlying model performance

Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D.

For engineers to create durable and effective electrical assemblies, modelling and controlling heat transfer in rotating electrical machines (such as motors) is crucial. In this paper, we compare the performance of three multi-objective evolutionary algorithms, namely NSGA-II, NSGA-III, and MOEA/D in finding the best trade-offs between data collection costs/effort and expected modelling errors when creating low-complexity Linear Regression (LR) models that can accurately estimate key motor component temperatures under various operational scenarios. The algorithms are integrated into a multi-objective thermal modelling strategy that aims to guide the discovery of models that are suitable for microcontroller deployment. Our findings show that while NSGA-II and NSGA-III yield comparably good optimisation outcomes, with a slight, but statistically significant edge for NSGA-II, the results achieved by MOEA/D for this use case are below par

A multi-objective evolutionary approach to discover explainability trade-offs when using linear regression to effectively model the dynamic thermal behaviour of electrical machines.

Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving accurate data-driven estimators of key motor temperatures, we propose a multi-objective strategy for creating Linear Regression (LR) models that integrate optimised synthetic features. The main strength of our approach is that it provides decision makers with a clear overview of the optimal trade-offs between data collection costs, the expected modelling errors and the overall explainability of the generated thermal models. Moreover, as parsimonious models are required for both microcontroller deployment and domain expert interpretation, our modelling strategy contains a simple but effective step-wise regularisation technique that can be applied to outline domain-relevant mappings between LR variables and thermal profiling capabilities. Results indicate that our approach can generate accurate LR-based dynamic thermal models when training on data associated with a limited set of load points within the safe operating area of the electrical machine under study

The Impact of Feature Vector Length on Activity Recognition Accuracy on Mobile Phone

A key challenge for large scale activity recognition on mobile phones is the requirement for producing non-static classifiers that cater for differences in individual user characteristics when performing similar activities in a diverse environment. A static classifier is fixed throughout the system lifetime and does not adapt to different users or environmental changes. Therefore, a personalized recognition model is desirable for each user of the system to ensure accurate recognition in a diverse population of people. One of the main approaches for personalization of activity recognition is the generation of the classification model from user annotated data on mobile itself. However, giving the resource constraints on such devices there is a need to examine the effects of system parameters such as the feature extraction parameter that can affect the performance of the system. Thus, this paper examines the effects of feature vector lengths and varying data set sizes on the classification accuracy of four selected supervised machine learning algorithms running on off the shelf mobile phones. Our results show that out of the three feature vector lengths of 32, 64 and 128 considered, the 128 vector length yields the best accuracy for all the algorithms tested. Also, the time taken to train the algorithms with samples of this length is minimal compare to 64 and 32 feature lengths

Evolving ANN-based sensors for a context-aware cyber physical system of an offshore gas turbine.

An adaptive multi-tiered framework, that can be utilised for designing a context-aware cyber physical system to carry out smart data acquisition and processing, while minimising the amount of necessary human intervention is proposed and applied. The proposed framework is applied within the domain of offshore asset integrity assurance. The suggested approach segregates processing of the input stream into three distinct phases of Processing, Prediction and Anomaly detection. The Processing phase minimises the data volume and processing cost by analysing only inputs from easily obtainable sources using context identification techniques for finding anomalies in the acquired data. During the Prediction phase, future values of each of the gas turbine's sensors are estimated using a linear regression model. The final step of the process - Anomaly Detection - classifies the significant discrepancies between the observed and predicted values to identify potential anomalies in the operation of the cyber physical system under monitoring and control. The evolving component of the framework is based on an Artificial Neural Network with error backpropagation. Adaptability is achieved through the combined use of machine learning and computational intelligence techniques. The proposed framework has the generality to be applied across a wide range of problem domains requiring processing, analysis and interpretation of data obtained from heterogeneous resources

The Effect of Window Length on Accuracy of Smartphone-Based Activity Recognition

One of the main approaches for personalization of activity recognition is the generation of the classification model from user annotated data on mobile itself. However, giving the resource constraints on such devices there is a need to examine the effects of system parameters such as the feature extraction parameter that can affect the performance of the system. Thus, this paper examines the effects of window length of the sensor data and varying data set sizes on the classification accuracy of four selected supervised machine learning algorithms running on off the shelf smartphone. Our results show that out of the three window lengths of 32, 64 and 128 considered, the 128 window length yields the best accuracy for all the algorithms tested. Also, the time taken to train the algorithms with samples of this length is minimal compare to 64 and 32 window lengths. A smartphone based activity recognition is implemented to utilize the results in an online activity recognition scenario

Rare variant contribution to human disease in 281,104 UK Biobank exomes.

Genome-wide association studies have uncovered thousands of common variants associated with human disease, but the contribution of rare variants to common disease remains relatively unexplored. The UK Biobank contains detailed phenotypic data linked to medical records for approximately 500,000 participants, offering an unprecedented opportunity to evaluate the effect of rare variation on a broad collection of traits1,2. Here we study the relationships between rare protein-coding variants and 17,361 binary and 1,419 quantitative phenotypes using exome sequencing data from 269,171 UK Biobank participants of European ancestry. Gene-based collapsing analyses revealed 1,703 statistically significant gene-phenotype associations for binary traits, with a median odds ratio of 12.4. Furthermore, 83% of these associations were undetectable via single-variant association tests, emphasizing the power of gene-based collapsing analysis in the setting of high allelic heterogeneity. Gene-phenotype associations were also significantly enriched for loss-of-function-mediated traits and approved drug targets. Finally, we performed ancestry-specific and pan-ancestry collapsing analyses using exome sequencing data from 11,933 UK Biobank participants of African, East Asian or South Asian ancestry. Our results highlight a significant contribution of rare variants to common disease. Summary statistics are publicly available through an interactive portal ( http://azphewas.com/ )

De Novo Mutations in SLC1A2 and CACNA1A Are Important Causes of Epileptic Encephalopathies

Epileptic encephalopathies (EEs) are the most clinically important group of severe early-onset epilepsies. Next-generation sequencing has highlighted the crucial contribution of de novo mutations to the genetic architecture of EEs as well as to their underlying genetic heterogeneity. Our previous whole-exome sequencing study of 264 parent-child trios revealed more than 290 candidate genes in which only a single individual had a de novo variant. We sought to identify additional pathogenic variants in a subset (n = 27) of these genes via targeted sequencing in an unsolved cohort of 531 individuals with a diverse range of EEs. We report 17 individuals with pathogenic variants in seven of the 27 genes, defining a genetic etiology in 3.2% of this unsolved cohort. Our results provide definitive evidence that de novo mutations in SLC1A2 and CACNA1A cause specific EEs and expand the compendium of clinically relevant genotypes for GABRB3. We also identified EEs caused by genetic variants in ALG13, DNM1, and GNAO1 and report a mutation in IQSEC2. Notably, recurrent mutations accounted for 7/17 of the pathogenic variants identified. As a result of high-depth coverage, parental mosaicism was identified in two out of 14 cases tested with mutant allelic fractions of 5%–6% in the unaffected parents, carrying significant reproductive counseling implications. These results confirm that dysregulation in diverse cellular neuronal pathways causes EEs, and they will inform the diagnosis and management of individuals with these devastating disorders