Modeling Individual Cyclic Variation in Human Behavior

Cycles are fundamental to human health and behavior. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present CyHMMs, a cyclic hidden Markov model method for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to model variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets -- of menstrual cycle symptoms and physical activity tracking data -- yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.Comment: Accepted at WWW 201

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.Karjojen keskikoko kasvaa jatkuvasti ja automaatio lypsyssä ja ruokinnassa lisääntyy. Maailmassa oli vuonna 2006 käytössä yli 6000 lypsyrobottia ja Suomessakin noin 200. Tilakoon kasvun seurauksena karjanhoitajan yksittäisen eläimen tarkkailemiseen käyttämä aika lyhenee ja mahdollisuus havaita eläinten terveysongelmat heikkenee. Tästä johtuen automaattisia menetelmiä tarvitaan tuotannon lisäksi myös lehmien terveyden seurantaan. Lypsykarjan ontuminen on yksi maailman suurimmista lehmien terveys- ja hyvinvointiongelmista. Jalkaongelmat aiheuttavat lehmille kipua ja heikentävät niiden hyvinvointia sekä heikentävät niiden maitotuotosta. Tuotoksen heikkeneminen, lehmien ennenaikainen poisto ja jalkavikojen hoito aiheuttavat merkittäviä taloudellisia menetyksiä karjan kasvattajille. Ontumisen aiheuttamia taloudellisia ja hyvinvointivaikutuksia voidaan pienentää merkittävästi, jos ongelma havaitaan ja hoidetaan aikaisessa vaiheessa. Jalkavikojen automaattinen mittaaminen tilatasolla mahdollistaa ontumisen nykyistä tarkemman seurannan ja säästää viljelijän työaikaa avustamalla eläinten tarkkailussa. Tutkimukset osoittavat, että suuri osa ontuvista lehmistä jää tiloilla kokonaan huomaamatta. Ongelman tunnistaminen mahdollistaa sen hoitamisen ja parantaa samalla eläinten hyvinvointia ja tilan taloudellista tulosta. Väitöstyö tehtiin Helsingin yliopiston Agroteknologian laitoksella ja mittaukset Suitian opetus- ja tutkimustilalla. Tutkimuksessa kehitettiin ensin nelivaakajärjestelmä, jolla punnitaan lehmän jokaisen jalan paino erikseen lypsyn aikana. Järjestelmä koostui neljästä leikkausvoima-anturista joiden päälle oli asennettu vaakasillat, vahvistimesta ja tietokoneesta sekä seurantaohjelmistosta. Järjestelmällä kerätyn yli 10 000 mittauksen ja säännöllisten eläinlääkärin tarkastusten perusteella kehitettiin jalkaviat havaitseva neuroverkkomalli. Valvontajärjestelmä havaitsi kaikki tutkimuksen aikaiset jalkaongelmat. Hälytyksen herkkyyttä voidaan säätää yhtä parametria muuttamalla. Kun valvonta säädetään varmasti havaitsemaan kaikki ongelmat, myös väärien hälytysten määrä lisääntyy. Tämä on useimmiten hyväksyttävää, koska hälytyksen tarkoitus on kiinnittää viljelijän huomio mahdollisesti sairaaseen eläimeen ja viljelijä tekee päätöksen hoitotarpeesta itse

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

A real-time rodent tracking system for both light and dark cycle behavior analysis

Abstrac

3D video based detection of early lameness in dairy cattle

Lameness is a major issue in dairy cattle and its early and automated detection offers animal welfare benefits together with potentially high commercial savings for farmers. Current advancements in automated detection have not achieved a sensitive measure for classifying early lameness; it remains to be a key challenge to be solved. The state-of-the-art also lacks behind on other aspects e.g. robust feature detection from a cow's body and the identification of the lame leg/side. This multidisciplinary research addresses the above issues by proposing an overhead, non-intrusive and covert 3-Dimensional (3D) video setup. This facilitates an automated process in order to record freely walking Holstein dairy cows at a commercial farm scale, in an unconstrained environment.The 3D data of the cow's body have been used to automatically track key regions such as the hook bones and the spine using a curvedness feature descriptor which operates at a high detection accuracy (100% for the spine, >97% for the hooks). From these tracked regions, two locomotion traits have been developed. First, motivated by a novel biomechanical approach, a proxy for the animal's gait asymmetry is introduced. This dynamic proxy is derived from the height variations in the hip joint (hooks) during walking, and extrapolated into right/left vertical leg motion signals. This proxy is evidently affected by minor lameness and directly contributes in identifying the lame leg. Second, back posture, which is analysed using two cubic-fit curvatures (X-Z plane and X-Y plane) from the spine region. The X-Z plane curvature is used to assess the spine's arch as an early lameness trait, while the X-Y plane curvature provides a novel definition for localising the lame side. Objective variables were extracted from both traits to be trained using a linear Support Vector Machine (SVM) classifier. Validation is made against ground truth data manually scored using a 1–5 locomotion scoring (LS) system, which consist of two datasets, 23 sessions and 60 sessions of walking cows. A threshold has been identified between LS 1 and 2 (and above). This boundary is important as it represents the earliest point in time at which a cow is considered lame, and its early detection could improve intervention outcome, thereby minimising losses and reducing animal suffering. The threshold achieved an accuracy of 95.7% with a 100% sensitivity (detecting lame cows), and 75% specificity (detecting non-lame cows) on dataset 1 and an accuracy of 88.3% with an 88% sensitivity and 92% specificity on dataset 2. Thereby outperforming the state-of-the-art at a stricter lameness boundary. The 3D video based multi-trait detection strives towards providing a comprehensive locomotion assessment on dairy farms. This contributes to the detection of developing lameness trends using regular monitoring which will improve the lack of robustness of existing methods and reduce reliance on expensive equipment and/or expertise in the dairy industry

Visual Tracking: From An Individual To Groups Of Animals

This thesis is concerned with the development and application of visual tracking techniques to the domain of animal monitoring. The development and evaluation of a system which uses image analysis to control the robotic placement of a sensor on the back of a feeding pig is presented first. This single-target monitoring application is then followed by the evaluation of suitable techniques for tracking groups of animals, of which the most suitable existing technique is found to be a Markov chain Monte Carlo particle filtering algorithm with a Markov random field motion prior (MCMC MRF, Khan et al. 2004). Finally, a new tracking technique is developed which uses social motion information present in groups of social targets to guide the tracking. This is used in the new Motion Parameter Sharing (MPS) algorithm. MPS is designed to improve the tracking of groups of targets with coordinated motion by incorporating motion information from targets that have been moving in a similar way. Situations where coordinated motion information should improve tracking include animal flocking, people moving as a group or any situation where some targets are moving in a correlated fashion. This new method is tested on a variety of real and artificial data sequences, and its performance compared to that of the MCMC MRF algorithm. The new MPS algorithm is found to outperform the MCMC MRF algorithm during a number of different types of sequences (including during occlusion events and noisy sequences) where correlated motion is present between targets. This improvement is apparent both in the accuracy of target location and robustness of tracking, the latter of which is greatly improved

Visual Tracking: From An Individual To Groups Of Animals

This thesis is concerned with the development and application of visual tracking techniques to the domain of animal monitoring. The development and evaluation of a system which uses image analysis to control the robotic placement of a sensor on the back of a feeding pig is presented first. This single-target monitoring application is then followed by the evaluation of suitable techniques for tracking groups of animals, of which the most suitable existing technique is found to be a Markov chain Monte Carlo particle filtering algorithm with a Markov random field motion prior (MCMC MRF, Khan et al. 2004). Finally, a new tracking technique is developed which uses social motion information present in groups of social targets to guide the tracking. This is used in the new Motion Parameter Sharing (MPS) algorithm. MPS is designed to improve the tracking of groups of targets with coordinated motion by incorporating motion information from targets that have been moving in a similar way. Situations where coordinated motion information should improve tracking include animal flocking, people moving as a group or any situation where some targets are moving in a correlated fashion. This new method is tested on a variety of real and artificial data sequences, and its performance compared to that of the MCMC MRF algorithm. The new MPS algorithm is found to outperform the MCMC MRF algorithm during a number of different types of sequences (including during occlusion events and noisy sequences) where correlated motion is present between targets. This improvement is apparent both in the accuracy of target location and robustness of tracking, the latter of which is greatly improved

Video-Based Tracking and Incremental Learning Applied to Rodent Behavioral Activity Under Near-Infrared Illumination

Abstract-This paper describes a noninvasive video tracking system for measurement of rodent behavioral activity under near-infrared (NIR) illumination, where the rodent is of a similar color to the background. This novel method allows position tracking in the dark, when rodents are generally most active, or under visible light. It also improves current video tracking methods under low-contrast conditions. We also manually extracted rodent features and classified three common behaviors (sitting, walking, and rearing) using an inductive algorithm-a decision tree (ID3). In addition, we proposed the use of a time-spatial incremental decision tree (ID5R), with which new behavior instances can be used to update the existing decision tree in an online manner. These were implemented using incremental tree induction. Open-field locomotor activity was investigated under "visible" (460.5−561.1 nm), 880-and 940-nm wavelengths of NIR, as well as a "dark" condition consisting of a very small level of NIR illumination. A widely used NIR crossbeam-based tracking system (Activity Monitor, MED Associates, Inc., Georgia, VT) was used to record simultaneous position data for validation of the video tracking system. The classification accuracy for the set of new test data was 81.3%