Utilizing similarity information in industrial applications

Abstract The amount of digital data surrounding us has exploded within the past years. In industry, data are gathered from different production phases with the intent to use the data to improve the overall manufacturing process. However, management and utilization of these huge data sets is not straightforward. Thus, a computer-driven approach called data mining has become an attractive research area. Using data mining methods, new and useful information can be extracted from enormous data sets. In this thesis, diverse industrial problems are approached using data mining methods based on similarity. Similarity information is shown to give an additional advantage in different phases of manufacturing. Similarity information is utilized with smaller-scale problems, but also in a broader perspective when aiming to improve the whole manufacturing process. Different ways of utilizing similarity are also introduced. Methods are chosen to emphasize the similarity aspect; some of the methods rely entirely on similarity information, while other methods just preserve similarity information as a result. The actual problems covered in this thesis are from quality control, process monitoring, improvement of manufacturing efficiency and model maintenance. They are real-world problems from two different application areas: spot welding and steel manufacturing. Thus, this thesis clearly shows how the industry can benefit from the presented data mining methods

Experiences with publicly open human activity data sets:studying the generalizability of the recognition models

Abstract In this article, it is studied how well inertial sensor-based human activity recognition models work when training and testing data sets are collected in different environments. Comparison is done using publicly open human activity data sets. This article has four objectives. Firstly, survey about publicly available data sets is presented. Secondly, one previously not shared human activity data set used in our earlier work is opened for public use. Thirdly, the genaralizability of the recognition models trained using publicly open data sets are experimented by testing them with data from another publicly open data set to get knowledge to how models work when they are used in different environment, with different study subjects and hardware. Finally, the challenges encountered using publicly open data sets are discussed. The results show that data gathering protocol can have a statistically significant effect to the recognition rates. In addition, it was noted that often publicly open human activity data sets are not as easy to apply as they should be

OpenHAR:a Matlab toolbox for easy access to publicly open human activity data sets

Abstract This study introduces OpenHAR, a free Matlab toolbox to combine and unify publicly open data sets. It provides an easy access to accelerometer signals of ten publicly open human activity data sets. Data sets are easy to access as OpenHAR provides all the data sets in the same format. In addition, units, measurement range and labels are unified, as well as, body position IDs. Moreover, data sets with different sampling rates are unified using downsampling. What is more, data sets have been visually inspected to find visible errors, such as sensor in wrong orientation. OpenHAR improves re-usability of data sets by fixing these errors. Altogether OpenHAR contains over 65 million labeled data samples. This is equivalent to over 280 hours of data from 3D accelerometers. This includes data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions

Personalizing human activity recognition models using incremental learning

Abstract In this study, the aim is to personalize inertial sensor databased human activity recognition models using incremental learning. At first, the recognition is based on user-independent model. However, when personal streaming data becomes available, the incremental learning-based recognition model can be updated, and therefore personalized, based on the data without user-interruption. The used incremental learning algorithm is Learn++ which is an ensemble method that can use any classifier as a base classifier. In fact, study compares three different base classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and classification and regression tree (CART). Experiments are based on publicly open data set and they show that already a small personal training data set can improve the classification accuracy. Improvement using LDA as base classifier is 4.6 percentage units, using QDA 2.0 percentage units, and 2.3 percentage units using CART. However, if the user-independent model used in the first phase of the recognition process is not accurate enough, personalization cannot improve recognition accuracy

Reviderad svensk kod för bolagsstyrning : en fallstudie ur revisorers perspektiv

Validerat; 20101217 (root

From user-independent to personal human activity recognition models using smartphone sensors

Abstract In this study, a novel method to obtain user-dependent human activity recognition models unobtrusively by using the sensors of a smartphone is presented. The recognition consists of two models: sensor fusion-based user-independent model for data labeling and single sensor-based user-dependent model for final recognition. The functioning of the presented method is tested with human activity data set, including data from accelerometer and magnetometer, and with two classifiers. Comparison of the detection accuracies of the proposed method to traditional user-independent model shows that the presented method has potential, in nine cases out of ten it is better than the traditional method, but more experiments using different sensor combinations should be made to show the full potential of the method

Importance of user inputs while using incremental learning to personalize human activity recognition models

Abstract In this study, importance of user inputs is studied in the context of personalizing human activity recognition models using incremental learning. Inertial sensor data from three body positions are used, and the classification is based on Learn++ ensemble method. Three different approaches to update models are compared: non-supervised, semi-supervised and supervised. Non-supervised approach relies fully on predicted labels, supervised fully on user labeled data, and the proposed method for semisupervised learning, is a combination of these two. In fact, our experiments show that by relying on predicted labels with high confidence, and asking the user to label only uncertain observations (from 12% to 26% of the observations depending on the used base classifier), almost as low error rates can be achieved as by using supervised approach. In fact, the difference was less than 2%-units. Moreover, unlike non-supervised approach, semisupervised approach does not suffer from drastic concept drift, and thus, the error rate of the non-supervised approach is over 5%-units higher than using semi-supervised approach

MyoGym:introducing an open gym data set for activity recognition collected using myo armband

Abstract The activity recognition research has remained popular although the first steps were taken almost two decades ago. While the first ideas were more like a-proof-of-concept studies the area has become a fruitful soil to novel methods of machine learning, to adaptive modeling, signal fusion and several different types of application areas. Nevertheless, one of the slowing aspects in methodology development is the burden in collecting and labeling enough versatile data sets. In this article, a MyoGym data set is introduced to be used in activity recognition classifier development, in development of models for unseen activities, in signal fusion, and many other areas not yet known. The data set includes 6D motion signals and 8 channel electromyogram data from 10 persons and from 30 different gym exercises, each of them consisting a set of ten repetitions. The benchmark results provided, in this article, are in purpose made straightforward that their repetitiveness should be easy for any newcomer in the area

Wishes for wearables from patients with migraine

Abstract Migraine is a long-term failure mode, including a risk of disease-related deficits, that leads to social exclusion. The study was conducted among members of the Finnish Migraine Association and was aimed at identifying migraine patients with pre-symptoms and whether they would be willing to use wearable sensors to detect pre-symptoms. The survey received responses from 565 persons, 90% of whom were willing to use wearable sensors to measure pre-symptoms and support treatment. Moreover, the study revealed that 87.8% of migraine patients identified migraine’s early symptoms, the most common of which are tiredness, slow thinking, difficulty finding words and visual disturbances. Most of the respondents wanted the device placed on their wrist as a watch, wristband or skin patch

Exploring use of wearable sensors to identify early symptoms of migraine attack

Abstract Migraine is a long-term failure mode including a risk of disease-related deficits that lead to social exclusion. The study was conducted among members of the Finnish Society for Migraine, and it aimed to determine and recognize the migraine patients with pre-symptoms and whether they would be willing to use wearable sensors in identifying pre-symptoms of migraine. The survey received responses from 565 persons, and more than 90 per cent of the respondents were willing to use the wearable sensors for the measurement of pre-symptoms, as well as to support the treatment. Moreover, the study revealed that 87.8 percent of migraine patients identified migraine early symptoms. The most common symptoms are tiredness, slow thinking, difficulty to find words and visual disturbances. Most of the respondents wanted the device placed on wrist as a watch, wristband or a skin patch