Kalman Filters for Time Delay of Arrival-based Source Localization

In this work, we propose an algorithm for acoustic source localization based on time delay of arrival (TDOA) estimation. In earlier work by other authors, an initial closed-form approximation was first used to estimate the true position of the speaker followed by a Kalman filtering stage to smooth the time series of estimates. In the proposed algorithm, this closed-form approximation is eliminated by employing a Kalman filter to directly update the speaker\u27s position estimate based on the observed TDOAs. In particular, the TDOAs comprise the observation associated with an extended Kalman filter whose state corresponds to the speaker\u27s position. We tested our algorithm on a data set consisting of seminars held by actual speakers. Our experiments revealed that the proposed algorithm provides source localization accuracy superior to the standard spherical and linear intersection techniques. Moreover, the proposed algorithm, although relying on an iterative optimization scheme, proved efficient enough for real-time operation

RECOGNITION AND ESTIMATION OF HUMAN LOCOMOTION WITH HIDDEN MARKOV MODELS

INTRODUCTION: The Collaborative Research Centre “Humanoid Robots” situated at the University of Karlsruhe is aimed to construct a learning and cooperating service robot. To cope with its tasks it is necessary that the robot is able to identify diverse objects as well as different persons. Looking at stochastic models for pattern recognition Hidden Markov Models (HMMs) are described to be most suitable to classify time arranged data (Bilmes 2002). The objective of this study is to screen if the HMMs supply satisfying rates of recognition of human trajectory and angle data. METHOD: Kinematic data of eight men and three women was captured at different walking and running speed (1.2 m/s, 3 m/s, 4 m/s, 5 m/s) on a treadmill. Data acquisition was realised with an infrared camera system with a frequency of 250Hz. For each walking/running speed there were 120 gait cycles of every test person available. The construction and training of the stochastic model was based on the gait data. Due to the fixed sequence of gait phases a HMM with a simple linear topology was chosen. Each state of the HMM represented a phase of the gait cycle. The different states were equipped with Gaussian distributions and transition probabilities to model the run of the angles observed. The HMM modelling human gait best was selected and trained with data of 17 double gait cycles for each data sequence of every test person. RESULTS: The trained HMMs showed recognition rates from 63% to 100% for the observed data sequences for five male test persons. Highest rates could be obtained with Centre of Mass and head angles. For some test person recognition rates decreased with data of gait cycles that were captured towards the end of one run. DISCUSSION: The high recognition rates based on kinematic data of Centre of Mass were expected due to the different mean values of the test persons according to their body height. The decrease of recognition rates that could be observed at some of the test person on late data of one run seems to be caused by acclimatisation to treadmill running. The achieved recognition rates exceed rates typical for speech recognition (Rabiner 1989). A combination of different angle data seems to promise increasing recognition rates. CONCLUSION: The study showed that HMMs seem to be suitable to identify humans based on their kinematic gait data satisfyingly stable. According to dislocation of the Gaussian distributions it could be possible to suggest on systematic changes on patterns over changes in walking-/running speed. REFERENCES: Bilmes, J. (2002). What HMMs Can Do. UWEE Technical Report, No UWEETR-2002-2003, University of Washington, Dept. of EE. Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77 (2), 257-286 Acknowledgement V. Wank, Institute of Sport Science, University of Tübingen German Research Foundation – CRC 588 Humanoid Robot

The health-related quality of life journey of gynecologic oncology surgical patients: Implications for the incorporation of patient-reported outcomes into surgical quality metrics

To report the changes in patient-reported quality of life for women undergoing gynecologic oncology surgeries

Who presents satisfied? Non-modifiable factors associated with patient satisfaction among gynecologic oncology clinic patients

To examine associations between non-modifiable patient factors and patient satisfaction (PS) among women presenting to a gynecologic oncology clinic

Prevalence of pelvic floor disorders in women with suspected gynecological malignancy: a survey-based study

Understanding of pelvic floor disorders among women with gynecological cancer is limited. The objective of this study was to describe the prevalence of pelvic floor disorders in women with suspected gynecological malignancy before surgery

I-care-an interaction system for the individual activation of people with dementia

I-CARE is a hand-held activation system that allows professional and informal caregivers to cognitively and socially activate people with dementia in joint activation sessions without special training or expertise. I-CARE consists of an easy-to-use tablet application that presents activation content and a server-based backend system that securely manages the contents and events of activation sessions. It tracks various sources of explicit and implicit feedback from user interactions and different sensors to estimate which content is successful in activating individual users. Over the course of use, I-CARE’s recommendation system learns about the individual needs and resources of its users and automatically personalizes the activation content. In addition, information about past sessions can be retrieved such that activations seamlessly build on previous sessions while eligible stakeholders are informed about the current state of care and daily form of their protegees. In addition, caregivers can connect with supervisors and professionals through the I-CARE remote calling feature, to get activation sessions tracked in real time via audio and video support. In this way, I-CARE provides technical support for a decentralized and spontaneous formation of ad hoc activation groups and fosters tight engagement of the social network and caring community. By these means, I-CARE promotes new care infrastructures in the community and the neighborhood as well as relieves professional and informal caregivers

Does the Robotic Platform Reduce Morbidity Associated With Combined Radical Surgery and Adjuvant Radiation for Early Cervical Cancers?

Open radical hysterectomy followed by adjuvant radiation for cervical cancer has been associated with significant rates of morbidity. Radical hysterectomy is now often performed robotically. We sought to examine if the robotic platform decreased the morbidity associated with radical hysterectomy followed by adjuvant radiation

False-negative results using Neisseria gonorrhoeae porA pseudogene PCR - a clinical gonococcal isolate with an N. meningitidis porA sequence, Australia, March 2011

The gonococcal porA pseudogene is a popular target for in-house Neisseria gonorrhoeae PCR methods. With this study we present two novel findings: the first case of an N. gonorrhoeae porA pseudogene PCR false-negative result caused by sequence variation, and in the same organism, the first description of a clinical N. gonorrhoeae strain harbouring an N. meningitidis porA sequence