Friedrich Kretschmer

Virtual experimental arena for behavioral experiments on small vertebrates

In this study, we present a virtual experimental arena to perform behavioral experiments on small vertebrates. It consists of a 360° visual stimulation realized by four LC displays and an automated camera-based head tracking system. Based on these two components, visual stimuli are repositioned depending of the animals head position in a closed-loop approach. We present two algorithms to automatically perform robust head tracking on mice and compare their precision to manual tracking by a human observer.

Automated measurement of spectral sensitivity of motion vision during optokinetic behavior

Optokinetic behavior is a commonly used paradigm to conveniently determine sensitivities of motion vision in animals. The optokinetic reflex (OKR) compensates global image motion on the retina by head and eye movements. It can reliably be elicited without training the test animal. In most OKR behavior experiments the animal is stimulated either with a rotating drum containing a pattern of vertical stripes or with computer monitors displaying a moving stripe pattern. In many of these studies, sensitivity thresholds are measured based on the subjective judgment of the experimenter. In this paper, we describe an alternative method to induce and measure OKR behavior. Our setup consists of a metal drum into which a slide is projected via a 360^o panoramic mirror, using a spectrally filtered LED as light source. The slide is mounted on a motor-driven turntable whose rotation leads to a horizontal movement of the stimulus on the drums wall, accordingly. By this means a spatial, temporal, and spectral well-defined and flexible panoramic stimulation is achieved. The animals head movements are video recorded under infrared illumination and tracked online. We introduce an objective criterion to automatically determine sensitivity thresholds based on the correlation of the animals head angle with the stimulus position. We exemplarily used this setup for an experiment that could not be performed with the state-of-the-art setup consisting of four monitors-the measurement of spectral sensitivity thresholds of the OKR behavior in turtles.

High speed coding for velocity by archerfish retinal ganglion cells

BackgroundArcherfish show very short behavioural latencies in response to falling prey. This raises the question, which response parameters of retinal ganglion cells to moving stimuli are best suited for fast coding of stimulus speed and direction.ResultsWe compared stimulus reconstruction quality based on the ganglion cell response parameters latency, first interspike interval, and rate. For stimulus reconstruction of moving stimuli using latency was superior to using the other stimulus parameters. This was true for absolute latency, with respect to stimulus onset, as well as for relative latency, with respect to population response onset. Iteratively increasing the number of cells used for reconstruction decreased the calculated error close to zero.ConclusionsLatency is the fastest response parameter available to the brain. Therefore, latency coding is best suited for high speed coding of moving objects. The quantitative data of this study are in good accordance with previously published behavioural response latencies.

A system for the model based emergency detection and communication for the telerehabilitation training of cardiopulmonary patients

Cardiopulmonary diseases affect millions of people and cause high costs in health care systems worldwide. Patients should perform regular endurance exercises to stabilize their health state and prevent further impairment. However, patients are often uncertain about the level of intensity they should exercise in their current condition.

Automated determinination of head gaze in rodents

Behavioral experiments to determine the sensory performance of mice or rats often require the measurement of the animals head gaze. Nevertheless, commercial systems for video tracking of laboratory animals are often restricted to tracking of the body position and sometimes the nose position, leading to defective estimates of the gaze direction or require several artificial markers. Most experimenters rely on subjective judgments of this important experimental measurement. In this study we present three algorithms to determine the exact gaze of freely moving rodents. Two of these algorithms rely on artificial markers which are attached to the animals head. One approach uses knowledge about the body shape of rodents to track gaze without the use of artificial markers. All three algorithms determine the direction of gaze more precisely than a human observer and avoid systematic errors occurring in previously available tracking algorithms.

Integration of Smart Home Health Data in the Clinical Decision Making Process

Patients suffering from COPD benefit from the performance of any kind of physical activity. The 3D layer context (3DLC) model characterizes data from smart home environments in relation to their relevance for the clinical decision making process. We have used this model to show how data from an ambient activity system in the domestic environment can be used to provide a more informed and thereby better treatment management for COPD patients. We set up an experiment to calculate an individual intensity relation between household activities and telerehabilitation training on a bicycle ergometer. We have extracted features from the power data of devices, which are used during the performance of two example every day activities to calculate the energy expenditure for the performance of these activities.

Integration of a Heart Rate Prediction Model into a Personal Health Record to Support the Telerehabilitation Training of Cardiopulmonary Patients

Chronic obstructive pulmonary disease (COPD) and coronary artery disease are severe diseases with increasing prevalence. Studies show that regular endurance exercise training affects the health state of patients positively. Heart Rate (HR) is an important parameter that helps physicians and (tele-) rehabilitation systems to assess and control exercise training intensity and to ensure the patients’ safety during the training. On the basis of 668 training sessions (325 F, 343 M), we created linear models predicting the training HR during five application scenarios. Personal Health Records (PHRs) are tools to support users to enter, manage and share their own health data, but usage of current products suffers under interoperability and acceptance problems. To overcome these problems, we implemented a PHR that is physically localized in the user’s home environment and that uses the predictive linear models to support physicians during the training plan creation process. The prediction accuracy of the model varies with a median root mean square error (RMSE) of ≈11 during the training plan creation scenario up to ≈3.2 in the scenario where the prediction takes place at the beginning of a training phase.

Automated quantification of optokinetic responses based on head-movement

In computational ethology, the measurement of optokinetic responses (OKR) is an established method [1] to determine thresholds of the visual system in various animal species. Wide-field movements of the visual environment elicit the typical body, head and eye-movements of optokinetic responses. Experimentally, usually regular patterns, e.g. black and white stripes, are moved continuously. Variation of stimulus parameters like contrast, spatial frequency and movement velocity allows to determine visual thresholds. The measurement of eye-movements is the most sensitive method to quantify optokinetic responses, but typically requires the fixation of the head by invasive surgery. Hence the measurement of head-movements is often used alternatively to rapidly measure the behavior of many individuals. While an animal performs these experiments, a human observer decides for each stimulus presentation if a tracking reaction was observed or not [1]. Since responses of the animals typically are not recorded, off-line analysis and the evaluation of other response characteristics is not possible. We developed a method to automatically quantify OKR behavior based on the head movement in small vertebrates. For this purpose, we built a system consisting of a visual 360° panorama stimulation realized by four LCD monitors and a camera, positioned above the animal to record the head movements. A tracking algorithm retrieves the angle of the animal’s head. Here, we present a method for automated detection of tracking behavior based on the difference between the angular velocities of head and stimulus movement. Tracking performance is measured as the amount of time the animal performs head movements corresponding to the stimulus movement for more than 1s. For the optokinetic responses of mice we show that the tracking time decreases with increasing spatial frequency of a sinusoidal stimulus pattern (Fig ​(Fig1).1). While a human observer was not able to detect tracking movements for spatial frequencies > 0.44 cyc/deg, the automated method revealed a certain amount of tracking behavior also at higher spatial frequencies. Thus, we were able to increase the sensitivity of the non-invasive measurement of optokinetic head movements into a sensitivity range that formerly required the measurement of eye movements. Figure 1 A: Head movements in response to sinusoidally moving stimuli of two different spatial frequencies. Red: Sequences, which were automatically identified as tracking behavior. B: Automatically identified tracking behavior at different spatial frequencies ...

Integration of medical models in personal health records using the example of rehabilitation training for cardiopulmonary patients

This article presents a prediction model for the development of the heart rate during rehabilitation training in patients suffering from cardiopulmonary diseases. The model helps to ensure a safe and effective training. Furthermore, the integration of the model into a personal health record system facilitating interoperability among doctors, hospitals and other healthcare institutions is discussed.