Algorithms for time series clustering applied to biomedical signals

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical EngineeringThe increasing number of biomedical systems and applications for human body understanding creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the user, has a significant value in the biosignal’s processing field. The present dissertation introduces new algorithms for time series clustering, where we are able to separate and organize unlabeled data into different groups whose signals are similar to each other. Signal processing algorithms were developed for the detection of a meanwave, which represents the signal’s morphology and behavior. The algorithm designed computes the meanwave by separating and averaging all cycles of a cyclic continuous signal. To increase the quality of information given by the meanwave, a set of wave-alignment techniques was also developed and its relevance was evaluated in a real database. To evaluate our algorithm’s applicability in time series clustering, a distance metric created with the information of the automatic meanwave was designed and its measurements were given as input to a K-Means clustering algorithm. With that purpose, we collected a series of data with two different modes in it. The produced algorithm successfully separates two modes in the collected data with 99.3% of efficiency. The results of this clustering procedure were compared to a mechanism widely used in this area, which models the data and uses the distance between its cepstral coefficients to measure the similarity between the time series.The algorithms were also validated in different study projects. These projects show the variety of contexts in which our algorithms have high applicability and are suitable answers to overcome the problems of exhaustive signal analysis and expert intervention. The algorithms produced are signal-independent, and therefore can be applied to any type of signal providing it is a cyclic signal. The fact that this approach doesn’t require any prior information and the preliminary good performance make these algorithms powerful tools for biosignals analysis and classification

Identification of Cross-Country Skiing Movement Patterns Using Micro-Sensors

This study investigated the potential of micro-sensors for use in the identification of the main movement patterns used in cross-country skiing. Data were collected from four elite international and four Australian athletes in Europe and in Australia using a MinimaxX™ unit containing accelerometer, gyroscope and GPS sensors. Athletes performed four skating techniques and three classical techniques on snow at moderate velocity. Data from a single micro-sensor unit positioned in the centre of the upper back was sufficient to visually identify cyclical movement patterns for each technique. The general patterns for each technique were identified clearly across all athletes while at the same time distinctive characteristics for individual athletes were observed. Differences in speed, snow condition and gradient of terrain were not controlled in this study and these factors could have an effect on the data patterns. Development of algorithms to process the micro-sensor data into kinematic measurements would provide coaches and scientists with a valuable performance analysis tool. Further research is needed to develop such algorithms and to determine whether the patterns are consistent across a range of different speeds, snow conditions and terrain, and for skiers of differing ability

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications

Time series morphological analysis applied to biomedical signals events detection

Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical EngineeringAutomated techniques for biosignal data acquisition and analysis have become increasingly powerful, particularly at the Biomedical Engineering research field. Nevertheless, it is verified the need to improve tools for signal pattern recognition and classification systems, in which the detection of specific events and the automatic signal segmentation are preliminary processing steps. The present dissertation introduces a signal-independent algorithm, which detects significant events in a biosignal. From a time series morphological analysis, the algorithm computes the instants when the most significant standard deviation discontinuities occur, segmenting the signal. An iterative optimization step is then applied. This assures that a minimal error is achieved when modeling these segments with polynomial regressions. The adjustment of a scale factor gives different detail levels of events detection. An accurate and objective algorithm performance evaluation procedure was designed. When applied on a set of synthetic signals, with known and quantitatively predefined events, an overall mean error of 20 samples between the detected and the actual events showed the high accuracy of the proposed algorithm. Its ability to perform the detection of signal activation onsets and transient waveshapes was also assessed, resulting in higher reliability than signal-specific standard methods. Some case studies, with signal processing requirements for which the developed algorithm can be suitably applied, were approached. The algorithm implementation in real-time, as part of an application developed during this research work, is also reported. The proposed algorithm detects significant signal events with accuracy and significant noise immunity. Its versatile design allows the application in different signals without previous knowledge on their statistical properties or specific preprocessing steps. It also brings added objectivity when compared with the exhaustive and time-consuming examiner analysis. The tool introduced in this dissertation represents a relevant contribution in events detection, a particularly important issue within the wide digital biosignal processing research field

Muscle activation patterns in shoulder impingement patients

Introduction: Shoulder impingement is one of the most common presentations of shoulder joint problems 1. It appears to be caused by a reduction in the sub-acromial space as the humerus abducts between 60o -120o – the 'painful arc'. Structures between the humeral head and the acromion are thus pinched causing pain and further pathology 2. Shoulder muscle activity can influence this joint space but it is unclear whether this is a cause or effect in impingement patients. This study aimed to observe muscle activation patterns in normal and impingement shoulder patients and determine if there were any significant differences. Method: 19 adult subjects were asked to perform shoulder abduction in their symptomatic arm and non-symptomatic. 10 of these subjects (age 47.9 ± 11.2) were screened for shoulder impingement, and 9 subjects (age 38.9 ± 14.3) had no history of shoulder pathology. Surface EMG was used to collect data for 6 shoulder muscles (Upper, middle and lower trapezius, serratus anterior, infraspinatus, middle deltoids) which was then filtered and fully rectified. Subjects performed 3 smooth unilateral abduction movements at a cadence of 16 beats of a metronome set at 60bpm, and the mean of their results was recorded. T-tests were used to indicate any statistical significance in the data sets. Significance was set at P<0.05. Results: There was a significant difference in muscle activation with serratus anterior in particular showing a very low level of activation throughout the range when compared to normal shoulder activation patterns (<30%). Middle deltoid recruitment was significantly reduced between 60-90o in the impingement group (30:58%).Trends were noted in other muscles with upper trapezius and infraspinatus activating more rapidly and erratically (63:25%; 60:27% respectively), and lower trapezius with less recruitment (13:30%) in the patient group, although these did not quite reach significance. Conclusion: There appears to be some interesting alterations in muscle recruitment patterns in impingement shoulder patients when compared against their own unaffected shoulders and the control group. In particular changes in scapula control (serratus anterior and trapezius) and lateral rotation (infraspinatus), which have direct influence on the sub-acromial space, should be noted. It is still not clear whether these alterations are causative or reactionary, but this finding gives a clear indication to the importance of addressing muscle reeducation as part of a rehabilitation programme in shoulder impingement patients

The frequency of falls in children judo training

Purpose: Falling techniques are inseparable part of youth judo training. Falling techniques are related to avoiding injuries exercises (Nauta et al., 2013). There is not good evidence about the ratio of falling during the training in children. Methods: 26 children (age 8.88±1.88) were video recorded on ten training sessions for further indirect observation and performance analysis. Results: Research protocol consisted from recording falls and falling techniques (Reguli et al., 2015) in warming up, combat games, falling techniques, throwing techniques and free fighting (randori) part of the training session. While children were taught almost exclusively forward slapping roll, backward slapping roll and sideward direct slapping fall, in other parts of training also other types of falling, as forward fall on knees, naturally occurred. Conclusions: Judo coaches should stress also on teaching unorthodox falls adding to standard judo curriculum (Koshida et al., 2014). Various falling games to teach children safe falling in different conditions should be incorporated into judo training. Further research to gain more data from groups of different age in various combat and non-combat sports is needed

Fear of crime and victimization among the elderly participating in the self-defence course

Purpose. Self-defence training could enhance seniors´ defensive skills and fitness. There is lack of evidence about fear and concerns of seniors participating in the self-defence course. Methods. 18 elderly persons (16 female, 1 male; age 66.2, SD=5.86) participated in the self-defence course lasting 8 training units (each unit 60 minutes). Standardized tool for fear of crime and victimization analysis previously used in Euro-Justis project in the Czech Republic (2011) was used in pretest and posttest. Results. We explored the highest fear of crime by participants in their residence area after dark (mean=2,77; median=3; SD=0,80), lower fear at the night in their homes (mean=2,29; median=2; SD=0,75) and in their residence area at the daytime (mean=2,00; median=2; SD=0,77) at the beginning of the course. We noticed certain decrease of fear of crime after the intervention. Participant were less afraid of crime in their residence area after dark (mean=2,38; median=2; SD=0,77), they felt lower fear of crime at the night in their homes (mean=2,00; median=2; SD=0,48) and in their residence area at the daytime (mean=1,82; median=2; SD=0,63). Conclusions. The approach to self-defence teaching for elderly should be focused not just on the motor development, but also on their emotional state, fear of crime, perception of dangerousness of diverse situations and total wellbeing. Fear of crime analysis can contribute to create tailor made structure of the self-defence course for specific groups of citizens

Analysis of the backpack loading efects on the human gait

Gait is a simple activity of daily life and one of the main abilities of the human being. Often during leisure, labour and sports activities, loads are carried over (e.g. backpack) during gait. These circumstantial loads can generate instability and increase biomechanicalstress over the human tissues and systems, especially on the locomotor, balance and postural regulation systems. According to Wearing (2006), subjects that carry a transitory or intermittent load will be able to find relatively efficient solutions to compensate its effects.info:eu-repo/semantics/publishedVersio