John Y. Goulermas

Bio: John Y. Goulermas is an academic researcher from University of Liverpool. The author has contributed to research in topics: Artificial neural network & Dimensionality reduction. The author has an hindex of 28, co-authored 126 publications receiving 3762 citations. Previous affiliations of John Y. Goulermas include University of Salford & University of Manchester.

Activity identification using body-mounted sensors — a review of classification techniques

Abstract: With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.

A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data

Abstract: Driven by the demands on healthcare resulting from the shift toward more sedentary lifestyles, considerable effort has been devoted to the monitoring and classification of human activity. In previous studies, various classification schemes and feature extraction methods have been used to identify different activities from a range of different datasets. In this paper, we present a comparison of 14 methods to extract classification features from accelerometer signals. These are based on the wavelet transform and other well-known time- and frequency-domain signal characteristics. To allow an objective comparison between the different features, we used two datasets of activities collected from 20 subjects. The first set comprised three commonly used activities, namely, level walking, stair ascent, and stair descent, and the second a total of eight activities. Furthermore, we compared the classification accuracy for each feature set across different combinations of three different accelerometer placements. The classification analysis has been performed with robust subject-based cross-validation methods using a nearest-neighbor classifier. The findings show that, although the wavelet transform approach can be used to characterize nonstationary signals, it does not perform as accurately as frequency-based features when classifying dynamic activities performed by healthy subjects. Overall, the best feature sets achieved over 95% intersubject classification accuracy.

Electric Load Forecasting Based on Locally Weighted Support Vector Regression

Abstract: The forecasting of electricity demand has become one of the major research fields in electrical engineering. Accurately estimated forecasts are essential part of an efficient power system planning and operation. In this paper, a modified version of the support vector regression (SVR) is presented to solve the load forecasting problem. The proposed model is derived by modifying the risk function of the SVR algorithm with the use of locally weighted regression (LWR) while keeping the regularization term in its original form. In addition, the weighted distance algorithm based on the Mahalanobis distance for optimizing the weighting function's bandwidth is proposed to improve the accuracy of the algorithm. The performance of the new model is evaluated with two real-world datasets, and compared with the local SVR and some published models using the same datasets. The results show that the proposed model exhibits superior performance compare to that of LWR, local SVR, and other published models.

Multivoxel fMRI analysis of color tuning in human primary visual cortex

Abstract: We use multivoxel pattern analysis (MVPA) to study the spatial clustering of color-selective neurons in the human brain. Our main objective was to investigate whether MVPA reveals the spatial arrangements of color-selective neurons in human primary visual cortex (V1). We measured the distributed fMRI activation patterns for different color stimuli (Experiment 1: cardinal colors (to which the LGN is known to be tuned), Experiment 2: perceptual hues) in V1. Our two main findings were that (i) cone-opponent cardinal color modulations produce highly reproducible patterns of activity in V1, but these were not unique to each color. This suggests that V1 neurons with tuning characteristics similar to those found in LGN are not spatially clustered. (ii) Unique activation patterns for perceptual hues in V1 support current evidence for a spatially clustered hue map. We believe that our work is the first to show evidence of spatial clustering of neurons with similar color preferences in human V1.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

How accurately can sitting and the intensity of walking and cycling be classified using an accelerometer on the waist for the purpose of the “Global recommendations on physical activity for health”?

Abstract: The society has become more sedentary and has developed a lack of physical activity, therefore increasing health risks. Feedback is needed to change these behaviours. For this feedback, first accurate monitoring is needed: sedentary behaviour must be classified as well as the intensity of physical activity. In this report a State of the Art analysis is performed to compare different classification techniques and finally two methods, both using an accelerometer on the waist, are worked out. These methods are Integral of the Modulus of the Accelerometer (IMA) classification and a machine learning technique (MLT): support vector machine (SVM). These methods are then applied in a laboratory experiment to study their quality. A measurement setup is made to create a dataset of the following activities: standing, sitting, lying, walking (2.4 - 7.5 km/h) and cycling (10.1-19 km/h). This dataset (n=15) is analysed and classified using Matlab for both methods. The IMA method was unable to monitor sedentary behaviour, but could classify the physical activity (PA) intensity with an accuracy of 66%. The SVM method within subjects was able to monitor sedentary behaviour with an accuracy of 91±20% and the classification of the PA intensity has an accuracy of 94±5%. For between subjects the accuracies decrease to 71±13% for PA intensity accuracy and 45±35% for the sedentary behaviour classification. IMA was implemented in the old feedback system, monitoring the overall daily amount of physical activity, but can significantly be outperformed by replacing it with the current SVM implementation. At this moment however, SVM can only be used to improve the old system, it cannot yet be used to create new additions to the feedback system, such as the implementation of the feedback of the sedentary behaviour and specific physical activity intensities. New methods and properties of the current SVM system have been found that might increase the accuracies of the between subject analysis and therefore might enable SVM to become applicable for the extra feedback options.