Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation

Over the last years, inertial sensing has proven to be a suitable ambulatory alternative to traditional human motion tracking based on optical position measurement systems, which are generally restricted to a laboratory environment. Besides many advantages, a major drawback is the inherent drift caused by integration of acceleration and angular velocity to obtain position and orientation. In addition, inertial sensing cannot be used to estimate relative positions and orientations of sensors with respect to each other. In order to overcome these drawbacks, this study presents an Extended Kalman Filter for fusion of inertial and magnetic sensing that is used to estimate relative positions and orientations. In between magnetic updates, change of position and orientation are estimated using inertial sensors. The system decides to perform a magnetic update only if the estimated uncertainty associated with the relative position and orientation exceeds a predefined threshold. The filter is able to provide a stable and accurate estimation of relative position and orientation for several types of movements, as indicated by the average rms error being 0.033 m for the position and 3.6 degrees for the orientation

Neonatal head and torso vibration exposure during inter-hospital transfer

Inter-hospital transport of premature infants is increasingly common, given the centralisation of neonatal intensive care. However, it is known to be associated with anomalously increased morbidity, most notably brain injury, and with increased mortality from multifactorial causes. Surprisingly, there have been relatively few previous studies investigating the levels of mechanical shock and vibration hazard present during this vehicular transport pathway. Using a custom inertial datalogger, and analysis software, we quantify vibration and linear head acceleration. Mounting multiple inertial sensing units on the forehead and torso of neonatal patients and a preterm manikin, and on the chassis of transport incubators over the duration of inter-site transfers, we find that the resonant frequency of the mattress and harness system currently used to secure neonates inside incubators is ~9Hz. This couples to vehicle chassis vibration, increasing vibration exposure to the neonate. The vibration exposure per journey (A(8) using the ISO 2631 standard) was at least 20% of the action point value of current European Union regulations over all 12 neonatal transports studied, reaching 70% in two cases. Direct injury risk from linear head acceleration (HIC15) was negligible. Although the overall hazard was similar, vibration isolation differed substantially between sponge and air mattresses, with a manikin. Using a Global Positioning System datalogger alongside inertial sensors, vibration increased with vehicle speed only above 60 km/h. These preliminary findings suggest there is scope to engineer better systems for transferring sick infants, thus potentially improving their outcomes

On magnetometer heading updates for inertial pedestrian navigation system

A magnetometer is often used to aid heading estimation of a low-cost Inertial Pedestrian Navigation System (IPNS) without which the latter will not be able to accurately estimate heading for more than a few seconds, even with the help of Zero Velocity Update (ZVU). Heading measurements from the magnetometer are typically integrated with gyro heading in an estimation filter such as Kalman Filter (KF) — to best estimate the true IPNS heading, resulting in a better positioning accuracy. However indoors the reliability of these measurements is often questionable because of the magnetic disturbances that can disrupt the measurements. To solve this problem, a filtering method is often used to select the best measurements. However, the importance of the frequency of these measurement updates has not been highlighted. In this paper, the impact of frequency of magnetometer updates on the overall accuracy of the navigation system is presented. The paper starts by discussing the use of a magnetometer in a low-cost IPNS. An exemplary filter to extract reliable heading measurements from the magnetometer is then described. From real field trial results, it will be shown that even if reliable heading measurements may be obtained indoors, it is still insufficient to increase the positioning accuracy of the low-cost IPNS unless it is reliable on every epoch

Sensory Communication

Contains table of contents for Section 2, an introduction and reports on twelve research projects.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Grant 5 R01 DC00126National Institutes of Health Grant R01-DC00270U.S. Air Force - Office of Scientific Research Contract AFOSR-90-0200National Institutes of Health Grant R29-DC00625U.S. Navy - Office of Naval Research Grant N00014-88-K-0604U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-1814U.S. Navy - Naval Training Systems Center Contract N61339-93-M-1213U.S. Navy - Naval Training Systems Center Contract N61339-93-C-0055U.S. Navy - Naval Training Systems Center Contract N61339-93-C-0083U.S. Navy - Office of Naval Research Grant N00014-92-J-4005U.S. Navy - Office of Naval Research Grant N00014-93-1-119

Sensory Communication

Contains table of contents for Section 2 and reports on five research projects.National Institutes of Health Contract 2 R01 DC00117National Institutes of Health Contract 1 R01 DC02032National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Contract N01 DC22402National Institutes of Health Grant R01-DC001001National Institutes of Health Grant R01-DC00270National Institutes of Health Grant 5 R01 DC00126National Institutes of Health Grant R29-DC00625U.S. Navy - Office of Naval Research Grant N00014-88-K-0604U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-1814U.S. Navy - Naval Air Warfare Center Training Systems Division Contract N61339-94-C-0087U.S. Navy - Naval Air Warfare Center Training System Division Contract N61339-93-C-0055U.S. Navy - Office of Naval Research Grant N00014-93-1-1198National Aeronautics and Space Administration/Ames Research Center Grant NCC 2-77

Sensory Communication

Contains table of contents for Section 2, an introduction and reports on fifteen research projects.National Institutes of Health Grant RO1 DC00117National Institutes of Health Grant RO1 DC02032National Institutes of Health Contract P01-DC00361National Institutes of Health Contract N01-DC22402National Institutes of Health/National Institute on Deafness and Other Communication Disorders Grant 2 R01 DC00126National Institutes of Health Grant 2 R01 DC00270National Institutes of Health Contract N01 DC-5-2107National Institutes of Health Grant 2 R01 DC00100U.S. Navy - Office of Naval Research/Naval Air Warfare Center Contract N61339-94-C-0087U.S. Navy - Office of Naval Research/Naval Air Warfare Center Contract N61339-95-K-0014U.S. Navy - Office of Naval Research/Naval Air Warfare Center Grant N00014-93-1-1399U.S. Navy - Office of Naval Research/Naval Air Warfare Center Grant N00014-94-1-1079U.S. Navy - Office of Naval Research Subcontract 40167U.S. Navy - Office of Naval Research Grant N00014-92-J-1814National Institutes of Health Grant R01-NS33778U.S. Navy - Office of Naval Research Grant N00014-88-K-0604National Aeronautics and Space Administration Grant NCC 2-771U.S. Air Force - Office of Scientific Research Grant F49620-94-1-0236U.S. Air Force - Office of Scientific Research Agreement with Brandeis Universit

Tracking of head position relative to the screen using head mounted camera / Galvos pozicijos nustatymas ekrano atžvilgiu naudojant ant galvos pritvirtintą kamerą

In this paper head position locating systems were analyzed. There were reviewed scientific articles with different proposed methods. The chosen system is with the camera located on the user head. The main parameters of the head positioning systems were analyzed. The procedure laid down in what order parameters are found. The diagram of the system and detail block diagram of the algorithm were provided. Realization of the algorithm used: edge detection method (Sobel), the adjustment algorithm (Subpixel). System is realized in Matlab and C# environment. Determine the optimal parameters for the algorithm execution. Execution of the algorithm in Matlab environment is 1.2 s and C # environment – 126 ms. During the examination of the longest executing algorithm segment, it was found that image filtering is carried out in 107 ms. It is noted that the uncertainty of the algorithm can be divided into static and measurement. The maximum static uncertainty while measuring head position parameters is 1.63 mm and orientation parameters – 0.16°. The maximum measurement uncertainty while measuring head position parameters is 4 mm and orientation parameters – 0.11°. Santrauka Nagrinėjamos galvos padėties pozicionavimo sistemos. Apžvelgti moksliniai straipsniai, kuriuose pateikiami įvairūs siūlomi metodai. Pasirinkta sistema, kai kamera pritvirtinama vartotojui ant galvos. Išskirti pagrindiniai galvos padėties nustatymo parametrai, nustatytas parametrų suradimo eiliškumas, pateikta sistemos schema ir detali algoritmo blokinė schema. Algoritmui įgyvendinti taikyti briaunų radimo (Sobel) ir algoritmo tikslinimo (Subpixel) metodai. Sistema įgyvendinta Matlab ir C# aplinkose. Nustatyti optimalūs algoritmo vykdymo parametrai. Naudojamo kompiuterio procesorius Intel Pentium dual core T4500 – 2,3 GHz. Vidutinis algoritmo veikimo laikas Matlab aplinkoje yra 1,2 s, o C# aplinkoje – 126 ms. Ištyrus algoritmo segmentų vykdymo laiką, pastebėta, kad ilgiausiai vykdomas vaizdo filtravimas – 107 ms. Parodyta, kad algoritmo neapibrėžtis galima suskirstyti į statines ir matavimo neapibrėžtis. Maksimali statinė neapibrėžtis, matuojant galvos pozicijos padėtį, yra 1,63 mm, matuojant orientacijos parametrus – 0,16°. Maksimali matavimo neapibrėžtis yra 4,0 mm, matuojant galvos padėties parametrus, ir 0,11° – orientacijos parametrus. Reikšminiai žodžiai: vaizdų apdorojimas, kompiuterinė rega, galvos padėties pozicija