28 research outputs found
Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification
No full textIn this paper we present a methodology for recognizing three fundamental movements of the human forearm (extension, flexion and rotation) using pattern recognition applied to the data from a single wrist-worn, inertial sensor. We propose that this technique could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g. prescribed exercises) with their paretic arm throughout the day. We demonstrate this with healthy subjects and stroke patients in a simple proof of concept study in whichthese arm movements are detected during an archetypal activity of daily-living (ADL) – ‘making-a-cup-of-tea’. Data is collected from a tri-axial accelerometer and a tri-axial gyroscope located proximal to the wrist. In a training phase, movements are initially performed in a controlled environment which are represented by a ranked set of 30 time-domain features. Using a sequential forward selection technique, for each set of feature combinations three clusters are formed using k-means clustering followed by 10 runs of 10-fold cross validation on the training data to determine the best feature combinations. For the testing phase, movements performed during the ADL are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprised of the best ranked features, using Euclidean or Mahalanobis distance as the metric. Experiments were performed with four healthy subjects and four stroke survivors and our results showthat the proposed methodology can detect the three movements performed during the ADL with an overall average accuracy of 88% using the accelerometer data and 83% using the gyroscope data across all healthy subjects and arm movement types. The average accuracy across all stroke survivors was 70% using accelerometer data and 66% using gyroscope data. We also use a Linear Discriminant Analysis (LDA) classifier and a Support Vector Machine (SVM) classifier in association with the same set of features to detect the three arm movements and compare the results to demonstrate the effectiveness of our proposed methodology
Movement fluidity of the impaired arm during stroke rehabilitation
No full textWe present an initial study on the measure of movement fluidity of the upper arm for 4 stroke patients for a duration of 3 weeks as they performed an archetypal activity of daily living – ‘making-a-cup-of-tea’ in an uncontrolled environment. Results of two complimenting measures – jerk metric and peak number computed from accelerometer data on the wrist are in agreement with the clinical scores from the Box and Block test and the Nine Hole Peg tes
Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist
No full textIn this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients
Real-time arm movement recognition using FPGA
No full textIn this paper we present a FPGA-based system to detect three elementary arm movements in real-time (reach and retrieve, lift cup to mouth, rotation of the arm) using data from a wrist-worn accelerometer. Recognition is carried out by accurately mapping transitions of predefined, standard orientations of an accelerometer to the corresponding arm movements. The algorithm is coded in HDL and synthesized on the Altera DE2-115 FPGA board. For real-time operation, interfacing between the streaming sensor unit, host PC and the FPGA was achieved through a combination of Bluetooth, RS232 and an application software developed in C# using the .NET framework to facilitate serial port controls. The synthesized design used 1804 logic elements and recognised the performed arm movement in 41.2 μs, @50 MHz clock on the FPGA. Our experimental results show that the system can recognise all three arm movements with accuracies ranging 85%-96% for healthy subjects and 63%-75% for stroke survivors involved in 'making-a-cup-of-tea', typical of an activity of daily living (ADL)
Low-complexity framework for movement classification using body-worn sensors
No full textWe present a low-complexity framework for classifying elementary arm-movements (reach-retrieve, lift-cup-to-mouth, rotate-arm) using wrist-worn, inertial sensors. We propose that this methodology could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies tracking occurrence of specific movements performed by patients with their paretic arm. Movements performed in a controlled training-phase are processed to form unique clusters in a multi-dimensional feature-space. Subsequent movements performed in an uncontrolled testing-phase are associated to the proximal cluster using a minimum distance classifier (MDC). The framework involves performing the compute-intensive clustering on the training-dataset offline (Matlab) whereas the computation of selected features on the testing-dataset and the minimum distance (Euclidean) from pre-computed cluster centroids are done in hardware with an aim of low-power execution on sensor nodes.The architecture for feature-extraction and MDC are realized using Coordinate Rotation Digital Computer based design which classifies a movement in (9n+31) clock cycles, n being number of data samples. The design synthesized in STMicroelectronics 130nm technology consumed 5.3 nW @50 HZ, besides being functionally verified upto 20 MHz, making it applicable for real-time high-speed operations. Our experimental results show that the system can recognize all three arm-movements with average accuracies of 86% and 72% for four healthy subjects using accelerometer and gyroscope data respectively, whereas for stroke survivors the average accuracies were 67% and 60%. The framework was further demonstrated as a FPGA-based real-time system, interfacing with a streaming sensor unit
Berechnung von Druckfeldern im Bereich von Vielblasensystemen
No full textSIGLETIB: RA 489 (452) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman
Tratamiento quirúrgico de la endometriosis . Consideraciones técnicas. Nuestro concepto
No full textThe author supports the conservative surgery in the genital endometriosis. Is, however, some difficulties in practice that rarely diagnosed preoperatively is associated with injuries requiring a greater resection or is diagnosed in women above the fourth decade of life. The possibilities of conservative surgery in endometriosis are: conserving surgery generative function, resection of uterine endometriosis, ovarian and partial resection or oophorectomy unillaterales salpingectomy, etc. Menstruo conservative surgery, indicated exclusively internal endometriosis, hysterectomy with Achner fundic, partial amputations and histeromiometrectomía. Conservative surgery of endocrine function, internal endometriosis heritage, with preservation of gonads. Study the possibilities of hormone therapy as an adjunct to conservative surgery but with residual lesions, using progestin.El autor apoya la cirugía conservadora en la endometriosis genital. Encuentra, sin embargo, algunas dificultades en su práctica por el hecho de que con poca frecuencia se diagnostica pre-operatoriamente, se asocia con lesiones que obligan a, una mayor exéresis o se diagnostica en mujeres por encima del cuarto decenio de la vida. Las posibilidades de la cirugía conservadora en la endometriosis son: Cirugía conservadora de la función generativa, con resecciones de endometriosis uterinas, resección parcial de ovarios y ooforectomía o salpingectomías unillaterales, etc. Cirugía menstruo conservadora, de indicación exclusiva en la endometriosis interna, con la histerectomía fúndica de Achner, las amputaciones parciales y la histeromiometrectomía. La cirugía conservadora de la función endocrina, patrimonio de la endometriosis interna, con conservación de gonadas. Estudio las posibilidades de la terapia hormonal como complemento de una cirugía conservadora pero con lesiones residuales, aprovechando los gestágenos
Detecting elementary arm movements by tracking upper limb joint angles with MARG sensors
No full textThis paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a two-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and four stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a seminaturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and seminaturalistic experiment, respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the seminaturalistic were detected correctly. Finally, the detection ratio remains close (±6%) to the average value, for different task durations further attesting to the algorithms robustness
Seed morphology of the subfamily Ornithogaloideae (Hyacinthaceae)
No full textIm Rahmen dieser Studie konnten 99 Arten aus 19 monophyletischen Gattungen der Unterfamilie Ornithogaloideae samenmorphologisch untersucht werden. Es wurden Habitusbilder in der Lateral- und Raphenansicht, sowie im Längs- und Querschnitt mittels einer Stereolupe angefertigt. Insgesamt wurden von den Samenproben 43 Merkmale anhand des Embryos, Endosperms und des Samens erhoben. Mit dem Programm Open Delta wurden die gewonnen morphologischen Daten verwaltet und die Arten-Beschreibung erstellt. Um einen besseren Überblick zu bekommen, wurden die drei Triben (Albuceae, Dipcadieae und Ornithogaleae) mit ihren Gattungen gegenüber gestellt, statistisch mittels des Programms IBM SPSS 22 ausgewertet und die Ergebnisse mit bestehender Literatur verglichen. Die Erfassung und Beurteilung der Merkmalsausprägungen der Samen und die deskriptiven Werte der vorliegenden Arbeit zeigen, dass die Erhebung samenmorphologischer Daten für die Klassifizierung der Unterfamilie Ornithogaloideae einen wichtigen Beitrag leistet und die photographische Dokumentation eine gute Arbeitsgrundlage für weitere Studien auf dem Gebiet der Samenmorphologie darstellt.In this study, 99 species of 19 monophyletic genera of the subfamily Ornithogaloideae could be researched based on seed-morphology. Habitus pictures were made in the lateral and raphe view, as well as in longitudinal and cross section by a stereo microscope. Altogether 43 characteristics of the seed samples based on the embryo, the endosperm and the seed were collected. The morphological data were managed with the software Open Delta and the description of species were created. For a better overview, the three tribes (Albuceae, Dipcadieae and Ornithogaleae) were contrasted with their genera; statistically analyzed by using the software IBM SPSS 22 and the results were compared with existing literature. The collection and assessment of the characteristic attributes of the seeds and the descriptive values of the present study show that the collection of morphological data of seeds makes an important contribution to the classification of the subfamily Ornithogaloideae, and the photographic documentation offers a good working basis for further studies in the faculty of seed morphology.vorgelegt von Katharina AchnerAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassungen in dt. und engl. SpracheGraz, Univ., Masterarb., 2015 691
