Wildlife Communication

This report contains a progress report for the ph.d. project titled “Wildlife Communication”. The project focuses on investigating how signal processing and pattern recognition can be used to improve wildlife management in agriculture. Wildlife management systems used today experience habituation from wild animals which makes them ineffective. An intelligent wildlife management system could monitor its own effectiveness and alter its scaring strategy based on this

FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360-degree camera, lidar, and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.Comment: Submitted to special issue of MDPI Sensors: Sensors in Agricultur

Multimodal ptsd characterization via the startlemart game

Computer games have recently shown promise as a diagnostic and treatment tool for psychiatric rehabilitation. This paper examines the potential of combining multiple modalities for detecting affective responses of patients interacting with a simulation built on game technology, aimed at the treatment of mental diagnoses such as Post Traumatic Stress Disorder (PTSD). For that purpose, we couple game design and game technology to create a game-based tool for exposure therapy and stress inoculation training that utilizes stress detection for the automatic profiling and potential personalization of PTSD treatments. The PTSD treatment game we designed forces the player to go through various stressful experiences while a stress detection mechanism profiles the severity and type of PTSD by analyzing the physiological responses to those in-game stress elicitors in two separate modalities: skin conductance (SC) and blood volume pulse (BVP). SC is often used to monitor stress as it is connected to the activation of the sympathetic nervous system (SNS). By including BVP into the model we introduce information about para-sympathetic activation, which offers a more complete view of the psycho-physiological experience of the player; in addition, as BVP is also modulated by SNS, a multimodal model should be more robust to changes in each modality due to particular drugs or day-to-day bodily changes. Overall, the study and analysis of 14 PTSD-diagnosed veteran soldiers presented in this paper reveals correspondence between diagnostic standard measures of PTSD severity and SC and BVP responsiveness and feature combinations thereof. The study also reveals that these features are significantly correlated with subjective evaluations of the stressfulness of experiences, represented as pairwise preferences. More importantly, the results presented here demonstrate that using the modalities of skin conductance and blood volume pulse captures a more nuanced representation of player stress responses than using skin conductance alone. We conclude that the results support the use of the simulation as a relevant treatment tool for stress inoculation training, and suggest the feasibility of using such a tool to profile PTSD patients. The use of multiple modalities appears to be key for an accurate profiling, although further research and analysis are required to identify the most relevant physiological features for capturing user stress.peer-reviewe

Stress detection for PTSD via the StartleMart game

Computer games have recently shown promise as a diagnostic and treatment tool for psychiatric rehabilitation. This paper examines the positive impact of affect detection and advanced game technology on the treatment of mental diagnoses such as Post Traumatic Stress Disorder (PTSD). For that purpose, we couple game design and game technology with stress detection for the automatic profiling and the personalized treatment of PTSD via game-based exposure therapy and stress inoculation training. The PTSD treatment game we designed forces the player to go through various stressful experiences while a stress detection mechanism profiles the severity and type of PTSD via skin conductance responses to those in-game stress elicitors. The initial study and analysis of 14 PTSD-diagnosed veteran soldiers presented in this paper reveals clear correspondence between diagnostic standard measures of PTSD severity and skin conductance responses. Significant correlations between physiological responses and subjective evaluations of the stressfulness of experiences, represented as pairwise preferences, are also found. We conclude that this supports the use of the simulation as a relevant treatment tool for stress inoculation training. This points to future avenues of research toward discerning between degrees and types of PTSD using game-based diagnostic and treatment tools.This research was supported by the Danish Council for Technology and Innovation under the Games for Health project and by the FP7 ICT project SIREN (project no: 258453).peer-reviewe

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

Korthals T, Kragh M, Christiansen P, Karstoft H, Jørgensen RN, Rückert U. Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI. 2018;5: 26.Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multi-modal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes

Does size really matter? A multisite study assessing the latent structure of the proposed ICD-11 and DSM-5 diagnostic criteria for PTSD

Background: Researchers and clinicians within the field of trauma have to choose between different diagnostic descriptions of posttraumatic stress disorder (PTSD) in the DSM-5 and the proposed ICD-11. Several studies support different competing models of the PTSD structure according to both diagnostic systems; however, findings show that the choice of diagnostic systems can affect the estimated prevalence rates. Objectives: The present study aimed to investigate the potential impact of using a large (i.e. the DSM-5) compared to a small (i.e. the ICD-11) diagnostic description of PTSD. In other words, does the size of PTSD really matter? Methods: The aim was investigated by examining differences in diagnostic rates between the two diagnostic systems and independently examining the model fit of the competing DSM-5 and ICD-11 models of PTSD across three trauma samples: university students (N = 4213), chronic pain patients (N = 573), and military personnel (N = 118). Results: Diagnostic rates of PTSD were significantly lower according to the proposed ICD-11 criteria in the university sample, but no significant differences were found for chronic pain patients and military personnel. The proposed ICD-11 three-factor model provided the best fit of the tested ICD-11 models across all samples, whereas the DSM-5 seven-factor Hybrid model provided the best fit in the university and pain samples, and the DSM-5 six-factor Anhedonia model provided the best fit in the military sample of the tested DSM-5 models. Conclusions: The advantages and disadvantages of using a broad or narrow set of symptoms for PTSD can be debated, however, this study demonstrated that choice of diagnostic system may influence the estimated PTSD rates both qualitatively and quantitatively. In the current described diagnostic criteria only the ICD-11 model can reflect the configuration of symptoms satisfactorily. Thus, size does matter when assessing PTSD

A Vocal-Based Analytical Method for Goose Behaviour Recognition

Since human-wildlife conflicts are increasing, the development of cost-effective methods for reducing damage or conflict levels is important in wildlife management. A wide range of devices to detect and deter animals causing conflict are used for this purpose, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviours could form a critical component of a system capable of altering the disruptive stimuli to avoid this. In this paper we present a novel method to automatically recognise goose behaviour based on vocalisations from flocks of free-living barnacle geese (Branta leucopsis). The geese were observed and recorded in a natural environment, using a shielded shotgun microphone. The classification used Support Vector Machines (SVMs), which had been trained with labeled data. Greenwood Function Cepstral Coefficients (GFCC) were used as features for the pattern recognition algorithm, as they can be adjusted to the hearing capabilities of different species. Three behaviours are classified based in this approach, and the method achieves a good recognition of foraging behaviour (86–97% sensitivity, 89–98% precision) and a reasonable recognition of flushing (79–86%, 66–80%) and landing behaviour(73–91%, 79–92%). The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities are important. We conclude that vocalisations can be used to automatically detect behaviour of conflict wildlife species, and as such, may be used as an integrated part of a wildlife management system