Spectroscopy of Charge-Coupled Devices

A systematic study of persistent, or residual, images occurring in CCD imagers and a systematic study of dark current generation in CCD imagers are presented. These effects are a source of unwanted signal in frames, and should be considered where very exact, low light-level signal processing is necessary. For both studies explanatory models and statistical analysis techniques have been developed which enable the derivation of the densities and the characteristic energies. Furthermore the importance of understanding these phenomena in the context of today\u27s low light-level imaging is discussed. Impurity sites are found to be responsible for residual images. Photoelectrons are trapped at impurity sites and thermally released over time. From the analysis the initial number of loaded traps and a trapping energy of 0.48 eV are found. In addition, the optical excitation rate and the maximum number of available trapping sites per pixel are derived. For the systematic study of the dark current, a quantization of the dark current and the existence of different pixel groups in a CCD imager are shown. The pixels are split into different groups (negative pixels, non-linear pixels, normal pixels, hot pixels) and models are developed to explain the different sources of dark current. For each group an explanatory model and a statistical analysis technique have been developed which enables the derivation of the densities and the characteristic energies responsible for these effects. For normal pixels the diffusion current is found to cause the dark current. Furthermore, for hot pixels and non-linear pixels an interface state generated component of the dark current is found in addition to diffusion current

Meyer-Neldel rule for dark current in charge-coupled devices

We present the results of a systematic study of the dark current in each pixel of a charged-coupled device chip. It was found that the Arrhenius plot, at temperatures between 222 and 291 K, deviated from a linear behavior in the form of continuous bending. However, as a first approximation, the dark current, D, can be expressed as: D=Dₒ exp(−ΔE/kT),where ΔE is the activation energy, k is Boltzmann’s constant, and T the absolute temperature. It was found that ΔE and the exponential prefactor Dₒ follow the Meyer–Neldel rule (MNR) for all of the more than 222,000 investigated pixels. The isokinetic temperature, Tₒ, for the process was found as 294 K. However, measurements at 313 K did not show the predicted inversion in the dark current. It was found that the dark current for different pixels merged at temperatures higher than Tₒ. A model is presented which explains the nonlinearity and the merging of the dark current for different pixels with increasing temperature. Possible implications of this finding regarding the MNR are discussed

Validity and reliability of a portable gait analysis system for measuring spatiotemporal gait characteristics: comparison to an instrumented treadmill

Gait analysis serves as an important tool for clinicians and other health professionals to assess gait patterns related to functional limitations due to neurological or orthopedic conditions. The purpose of this study was to assess the validity of a body-worn inertial sensor system (RehaGait®) for measuring spatiotemporal gait characteristics compared to a stationary treadmill (Zebris) and the reliability of both systems at different walking speeds and slopes.; Gait analysis was performed during treadmill walking at different speeds (habitual walking speed (normal speed); 15 % above normal walking speed; 15 % below normal walking speed) and slopes (0 % slope; 15 % slope) in 22 healthy participants twice 1 week apart. Walking speed, stride length, cadence and stride time were computed from the inertial sensor system and the stationary treadmill and compared using repeated measures analysis of variance. Effect sizes of differences between systems were assessed using Cohen's d, and limits of agreement and systematic bias were computed.; The RehaGait® system slightly overestimated stride length (+2.7 %) and stride time (+0.8 %) and underestimate cadence (-1.5 %) with small effect sizes for all speeds and slopes (Cohen's d ≤ 0.44) except slow speed at 15 % slope (Cohen's d > 0.80). Walking speed obtained with the RehaGait® system closely matched the speed set on the treadmill tachometer. Intraclass correlation coefficients (ICC) were excellent for speed, cadence and stride time and for stride length at normal and fast speed at 0 % slope (ICC: .91-1.00). Good ICC values were found for stride length at slow speed at 0 % slope and all speeds at 15 % slope (ICC: .73-.90). Both devices had excellent reliability for most gait characteristics (ICC: .91-1.00) except good reliability for the RehaGait® for stride length at normal and fast speed at 0 % slope and at slow speed at 15 % slope (ICC: .80-.87).; Larger limits of agreement for walking at 15 % slope suggests that uphill walking may influence the reliability of the RehaGait® system. The RehaGait® is a valid and reliable tool for measuring spatiotemporal gait characteristics during level and inclined treadmill walking

Residual images in charged-coupled device detectors

We present results of a systematic study of persistent, or residual, images that occur in charged-coupled device (CCD) detectors. A phenomenological model for these residual images, also known as ghosting, is introduced. This model relates the excess dark current in a CCD after exposure to the number of filled impurity sites which is tested for various temperatures and exposure times. We experimentally derive values for the cross section, density, and characteristic energy of the impurity sites responsible for the residual images

Effects of a Dynamic Chair on Chair Seat Motion and Trunk Muscle Activity during Office Tasks and Task Transitions

Employing dynamic office chairs might increase the physical (micro-) activity during prolonged office sitting. We investigated whether a dynamic BioSwing; ®; chair increases chair sway and alters trunk muscle activation. Twenty-six healthy young adults performed four office tasks (reading, calling, typing, hand writing) and transitions between these tasks while sitting on a dynamic and on a static office chair. For all task-transitions, chair sway was higher in the dynamic condition (; p; < 0.05). Muscle activation changes were small with lower mean activity of the left obliquus internus during hand writing (; p; = 0.07), lower mean activity of the right erector spinae during the task-transition calling to hand writing (; p; = 0.036), and higher mean activity of the left erector spinae during the task-transition reading to calling (; p; = 0.07) on the dynamic chair. These results indicate that an increased BioSwing; ®; chair sway only selectively alters trunk muscle activation. Adjustments of chair properties (i.e., swinging elements, foot positioning) are recommended

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. Methods: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. Results: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). Conclusion: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery