The cow pedogram-Analysis of gait cycle variables allows the detection of lameness and foot pathologies

Photogrammetric analysis of limb joint angles in normal-gait cows before and after hoof trimming

The aim of this study was to evaluate the effect of hoof trimming on overall limb movements by comparing the changes in 8 limb joint angles before and after one week of hoof trimming. Seventeen Holstein-Friesian dairy cows that were able to move freely and had no history of hoof diseases were included in the study. The cows were walked on a rubber mat with a high friction coefficient (HFM) and a low friction coefficient by the spraying of sodium polyacrylate (LFM). A high-speed camera was set to 200 fps on the image analysis software, and the images of the cows that were given 15 reflective markers on their right side were captured while walking on the test mat. The tests were conducted before and after one week of hoof trimming, and the cows were trimmed by the functional hoof trimming method. With image analysis software, video clips of walking cows were confirmed visually and tracked during one gait cycle by each reflective marker attached to the hoof of the forelimb and hindlimb, after which the stance phase and swing phase were identified. The durations of the stance phase and swing phase of the forelimb and hindlimb, respectively, and the maximum, minimum, and range of motion (ROM) values of the 8 joint angles, shoulder joint, elbow joint, carpus joint, forelimb fetlock joint, hip joint, stifle joint, hock joint and hindlimb fetlock joint during one gait cycle were included in the analysis. The maximum and minimum angles of the hip and stifle joints were narrower after hoof trimming than before, although the ROM did not change and was clearer for HFM than for LFM. It was thought that the flexion of the proximal hindlimb would progress smoothly during walking after trimming.

Technology applications in bovine gait analysis: A scoping review

Quantitative bovine gait analysis using technology has evolved significantly over the last two decades. However, subjective methods of gait assessment using visual locomotion scoring remain the primary on-farm and experimental approach. The objective of this review is to map research trends in quantitative bovine gait analysis and to explore the technologies that have been utilized to measure biomechanical parameters of gait. A scoping literature review was conducted according to PRISMA guidelines. A search algorithm based on PICO framework generated three components-bovine, gait, and technology-to address our objectives. Three online databases were searched for original work published from January 2000 to June 2020. A two-step screening process was then conducted, starting with the review of article titles and abstracts based on inclusion criteria. A remaining 125 articles then underwent a full-text assessment, resulting in 82 final articles. Thematic analysis of research aims resulted in four major themes among the studies: gait/claw biomechanics, lameness detection, intervention/comparison, and system development. Of the 4 themes, lameness detection (55% of studies) was the most common reason for technology use. Within the literature identified three main technologies were used: force and pressure platforms (FPP), vision-based systems (VB), and accelerometers. FPP were the first and most popular technologies to evaluate bovine gait and were used in 58.5% of studies. They include force platforms, pressure mapping systems, and weight distribution platforms. The second most applied technology was VB (34.1% of studies), which predominately consists of video analysis and image processing systems. Accelerometers, another technological method to measure gait characteristics, were used in 14.6% of studies. In sum, the strong demand for automatic lameness detection influenced the path of development for quantitative gait analysis technologies. Among emergent technologies, deep learning and wearable sensors (e.g., accelerometers) appear to be the most promising options. However, although progress has been made, more research is needed to develop more accurate, practical, and user-friendly technologies.

Optimization of In Situ Indentation Protocol to Map the Mechanical Properties of Articular Cartilage

Tissue engineering aims at developing complex composite scaffolds for articular cartilage repair. These scaffolds must exhibit a mechanical behavior similar to the whole osteochondral unit. In situ spherical indentation allows us to map the mechanical behavior of articular cartilage, avoiding removal of the underlying bone tissue. Little is known about the impact of grid spacing, indenter diameter, and induced deformation on the cartilage response to indentation. We investigated the impact of grid spacing (range: a to 3a, where a is the radius of the contact area between cartilage and indenter), indenter diameter (range: 1 to 8 mm), and deformation induced by indentation (constant indentation depth versus constant nominal deformation) on cartilage response. The bias induced by indentations performed in adjacent grid points was minimized with a 3a grid spacing. The cartilage response was indenter-dependent for diameters ranging between 1 and 6 mm with a nominal deformation of 15%. No significant differences were found using 6 mm and 8 mm indenters. Six mm and 8 mm indenters were used to map human articular cartilage with a grid spacing equal to 3a. Instantaneous elastic modulus E₀ was calculated for constant indentation depth and constant nominal deformation. E₀ value distribution did not change significantly by switching the two indenters, while dispersion decreased by 5-6% when a constant nominal deformation was applied. Such an approach was able to discriminate changes in tissue response due to doubling the indentation rate. The proposed procedure seems to reduce data dispersion and properly determine cartilage mechanical properties to be compared with those of complex composite scaffolds.

Gait Analysis in Walking and Trotting Dairy Cows on Different Flooring Types with Novel Mobile Pressure Sensors and Inertial Sensors

Mechanical overburdening is a major risk factor that provokes non-infectious claw diseases. Moreover, lameness-causing lesions often remain undetected and untreated. Therefore, prevention of claw tissue overburdening is of interest, especially by analyzing harmful effects within dairy cows’ housing environment. However, objective “on-cow” methods for bovine gait analysis are underdeveloped. The purpose of the study was to apply an innovative mobile pressure sensor system attached at the claws to perform pedobarometric gait analysis. A further goal was the supplementation with accelerative data, generated simultaneously by use of two inertial measurement units (IMUs), attached at metatarsal level. IMU data were analyzed with an automatic step detection algorithm. Gait analysis was performed in ten dairy cows, walking and trotting on concrete flooring and rubber mats. In addition to the basic applicability of the sensor systems and with the aid of the automatic step detection algorithm for gait analysis in cows, we were able to determine the impact of the gait and flooring type on kinematic and kinetic parameters. For pressure sensor output, concrete was associated with significantly (p < 0.001) higher maximum and average pressure values and a significantly smaller contact area, compared to rubber mats. In contrast to walking, trotting led to a significantly higher force, especially under the medial claw. Further, IMU-derived parameters were significantly influenced by the gait. The described sensor systems are useful tools for detailed gait analysis in dairy cows. They allow the investigation of factors which may affect claw health negatively.

Why Is the Grass the Best Surface to Prevent Lameness? Integrative Analysis of Functional Ranges as a Key for Dairy Cows’ Welfare

Simple Summary

Lameness is a highly prevalent clinical condition that causes movement disorders in dairy cows worldwide. With an estimated global population of one billion dairy cows, producing 522 million metric tons of milk per year, this problem affects food availability as well as the global economy. While grass is considered to be the best support surface for cattle, in many places it cannot be used, particularly when climate conditions are too harsh for grass to grow or be maintained. In this paper, we investigate whether grass is the best surface to prevent lameness. The answer to this question is fundamental to establishing better farming practices for cattle welfare. We built an integrative analysis of functional ranges to establish the minimum and maximum movement capacities that a cow has, according to the surfaces to which it is subjected in free housing systems. Using this analysis, we identified many aspects that make a grass surface the healthiest option for cattle. However, when grass is not available, this type of strategy can help to find the best characteristics for other possible surfaces. Our study applies movement analysis to one of the most critical problems in the world of livestock management and contributes towards finding the balance between animal welfare and production.

Abstract

Lameness is a painful clinical condition of the bovine locomotor system that results in alterations of movement. Together with mastitis and infertility, lameness is the main welfare, health, and production problem found in intensive dairy farms worldwide. The clinical assessment of lameness results in an imprecise diagnosis and delayed intervention. Hence, the current approach to the problem is palliative rather than preventive. The five main surfaces used in free housing systems in dairy farms are two natural (grass and sand) and three artificial (rubber, asphalt, and concrete). Each surface presents a different risk potential for lameness, with grass carrying the lowest threat. The aim of the present study is to evaluate the flooring type influences on cows’ movement capabilities, using all the available information relating to kinematics, kinetics, behavior, and posture in free-housed dairy cows. Inspired by a refurbished movement ecology concept, we conducted a literature review, taking into account kinematics, kinetics, behavior, and posture parameters by reference to the main surfaces used in free housing systems for dairy cows. We built an integrative analysis of functional ranges (IAFuR), which provides a combined welfare status diagram for the optimal (i.e., within the upper and lower limit) functional ranges for movement (i.e., posture, kinematics, and kinetics), navigation (i.e., behavior), and recovery capacities (i.e., metabolic cost). Our analysis confirms grass’ outstanding clinical performance, as well as for all of the movement parameters measured. Grass boosts pedal joint homeostasis; provides reliable, safe, and costless locomotion; promotes longer resting times. Sand is the best natural alternative surface, but it presents an elevated metabolic cost. Rubber is an acceptable artificial alternative surface, but it is important to consider the mechanical and design properties. Asphalt and concrete surfaces are the most harmful because of the high traffic abrasiveness and loading impact. Furthermore, IAFuR can be used to consider other qualitative and quantitative parameters and to provide recommendations on material properties and the design of any surface, so as to move towards a more grass-like feel. We also suggest the implementation of a decision-making pathway to facilitate the interpretation of movement data in a more comprehensive way, in order to promote consistent, adaptable, timely, and adequate management decisions.

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Simple Summary

The welfare of farm animals is a growing concern in the EU and across the world. In milk production, there is a strong need to assess the welfare of dairy cows. One of the most sound assessment initiatives has been practiced using protocols developed by the Welfare Quality project. These protocols mainly support the assessment of cow welfare with animal-based indicators. However, evaluating these indicators is time-consuming and expensive, so using precision livestock farming (PLF) solutions is a way forward and is becoming a reality in the dairy industry. This review presents advances in PLF solutions, particularly in the last five years, and for assessing the animal-based indicators of lameness, mastitis, and body condition in dairy cattle farming.

Abstract

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.

Kinematic gait characteristics of straight line walk in clinically sound dairy cows

The aim of this study is to describe the kinematic gait characteristics of straight line walk in clinically sound dairy cows using body mounted Inertial Measurement Units (IMUs) at multiple anatomical locations. The temporal parameters used are speed and non-speed normalized stance duration, bipedal and tripedal support durations, maximal protraction and retraction angles of the distal limbs and vertical displacement curves of the upper body. Gait analysis was performed by letting 17 dairy cows walk in a straight line at their own chosen pace while equipped with IMU sensors on tubera sacrale, left and right tuber coxae (LTC and RTC), back, withers, head, neck and all four lower limbs. Data intervals with stride by stride regularity were selected based on video data. For temporal parameters, the median was calculated and 95% confidence intervals (CI) were estimated based on linear mixed model (LMM) analysis, while for limb and vertical displacement curves, the median and most typical curves were calculated. The temporal parameters and distal limb angles showed consistent results with low variance and LMM analysis showed non-overlapping CI for all temporal parameters. The distal limb angle curves showed a larger and steeper retraction angle range for the distal front limbs compared with the hind limbs. The vertical displacement curves of the sacrum, withers, LTC and RTC showed a consistent sinusoidal pattern while the head, back and collar curves were less consistent and showed more variation between and within cows. This kinematic description might allow to objectively differentiate between normal and lame gait in the future and determine the best anatomical location for sensor attachment for lameness detection purposes.

On-farm detection of claw lesions in dairy cows based on acoustic analyses and machine learning

Claw lesions are a serious problem on dairy farms, affecting both the health and welfare of the cow. Automated detection of lameness with a practical, on-farm application would support the early detection and treatment of lame cows, potentially reducing the number and severity of claw lesions. Therefore, in this study, a method was proposed for the detection of claw lesions based on the acoustic analysis of a cow's gait. A panel was constructed to measure the impact sound of animals walking over it. The recorded impact sound was edited, and 640 sound files from 64 cows were analyzed. The classification of animal-lameness status was performed using a machine-learning process with a random forest algorithm. The gold standard was a 2-point scale of hoof-trimming results (healthy vs. affected), and 38 properties of the recorded sound files were used as influencing factors. A prediction model for classifying the cow lameness was built using a random forest algorithm. This was validated by comparing the reference output from hoof-trimming with the model output concerning the impact sound. Altering the likelihood settings and changing the cutoff value to predict lame animals improved the prediction model. At a cutoff at 0.4, a decreased false-negative rate was generated, and the false-positive rate only increased slightly. This model obtained a sensitivity of 0.81 and a specificity of 0.97. With this procedure, Cohen's Kappa value of 0.80 showed good agreement between model classification and diagnoses from hoof-trimming. In summary, the prediction model enabled the detection of cows with claw lesions. This study shows that lameness can be detected by machine learning from the impact sound of hoofs in dairy cows.

Prediction of lameness using automatically recorded activity, behavior and production data in post-parturient Irish dairy cows

BACKGROUND

Although visual locomotion scoring is inexpensive and simplistic, it is also time consuming and subjective. Automated lameness detection methods have been developed to replace the visual locomotion scoring and aid in early and accurate detection. Several types of sensors are measuring traits such as activity, lying behavior or temperature. Previous studies on automatic lameness detection have been unable to achieve high accuracy in combination with practical implementation in a on farm commercial setting. The objective of our research was to develop a prediction model for lameness in dairy cattle using a combination of remote sensor technology and other animal records that will translate sensor data into easy to interpret classified locomotion information for the farmer. During an 11-month period, data from 164 Holstein-Friesian dairy cows were gathered, housed at an Irish research farm. A neck-mounted accelerometer was used to gather behavioral metrics, additional automatically recorded data consisted of milk production and live weight. Locomotion scoring data were manually recorded, using a one-to-five scale (1 = non-lame, 5 = severely lame). Locomotion scores where then used to label the cows as sound (locomotion score 1) or unsound (locomotion score ≥ 2). Four supervised classification models, using a gradient boosted decision tree machine learning algorithm, were constructed to investigate whether cows could be classified as sound or unsound. Data available for model building included behavioral metrics, milk production and animal characteristics.

RESULTS

The resulting models were constructed using various combinations of the data sources. The accuracy of the models was then compared using confusion matrices, receiver-operator characteristic curves and calibration plots. The model which achieved the highest performance according to the accuracy measures, was the model combining all the available data, resulting in an area under the curve of 85% and a sensitivity and specificity of 78%.

CONCLUSION

These results show that 85% of this model's predictions were correct in identifying cows as sound or unsound, showing that the use of a neck-mounted accelerometer, in combination with production and other animal data, has potential to replace visual locomotion scoring as lameness detection method in dairy cows.

A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications

The computer vision technique has been rapidly adopted in cow lameness detection research due to its noncontact characteristic and moderate price. This paper attempted to summarize the research progress of computer vision in the detection of lameness. Computer vision lameness detection systems are not popular on farms, and the accuracy and applicability still need to be improved. This paper discusses the problems and development prospects of this technique from three aspects: detection methods, verification methods and application implementation. The paper aims to provide the reader with a summary of the literature and the latest advances in the field of computer vision detection of lameness in dairy cows.

Short communication: Claw block application improves locomotion and weight-bearing characteristics in cattle with foot diseases

In cattle with foot diseases, application of a block on the healthy partner claw is a common method of pain relief. The aim of this study was to evaluate the effect of wooden claw blocks on locomotion characteristics and weight distribution in healthy (group C; n = 17) versus lame (group L; n = 17) cattle. Group L was further subdivided into group L1 (lameness score ≤3; n = 7) and group L2 (lameness score >3; n = 10). We performed lameness scoring using a numeric rating system and measured locomotion characteristics using 2 accelerometers (400 Hz; kinematic outcome = stance phase duration; kinetic outcomes = foot load and toe-off) and a 4-scale weighing platform (difference of mean weight distribution across the limbs; ∆_weight) before and after application of a claw block. We applied claw blocks to a randomly assigned lateral or medial claw of the fore or hindlimb in group C cows, and on the healthy partner claw in group L cows. Variables were expressed as differences across limbs. We used 1-way ANOVA to determine the differences between groups C and L and between groups L1 and L2 for ∆_weight after application of the claw block. We performed paired t tests to compare variables before and after application of the claw block in groups C and L. Group L scored higher on the numeric rating system than group C (mean ± SD, 3.40 ± 0.62 vs. 1.87 ± 0.28) and showed greater differences in relative stance phase duration (16.34 ± 10.78% vs. 2.13 ± 1.94%), foot load (9.68 ± 8.06 g vs. 3.26 ± 3.69 g), toe-off (3.91 ± 3.14 g vs. 0.78 ± 0.66 g), and ∆_weight (53.62 ± 28.85% vs. 8.52 ± 6.19%). In group C, we observed an increase of 12.17 percentage points in ∆_weight after block application, from 8.52 ± 6.19% to 20.69 ± 17.01%. Compared with the baseline, group L showed a decrease in numeric rating system score (2.88 ± 0.49 vs. 3.40 ± 0.62) and a decrease in differences between the limbs in relative stance phase duration (7.66 ± 9.96% vs. 16.34 ± 10.78%) and foot load (4.26 ± 4.14 g vs. 9.68 ± 8.06 g) after application of a claw block. Group L2 showed smaller ∆_weight after application of a claw block than group L1 (-7.8 ± 8.7% vs. 10.4 ± 7.6%). After block application in group L, we observed smaller differences across the limbs in variables measured to describe gait-cycle characteristics while walking, but no significant improvement while standing. We concluded that application of a claw block must be combined with other methods of pain relief, such as analgesic medication.

Association between hoof lesions and fertility in lactating Jersey cows

The objectives of this study were to evaluate the association between hoof lesions and fertility in dairy cows. Lactating Jersey cows (n = 1,639) were enrolled at 20 ± 3 d in milk (D20), examined and treated for presence of hoof lesions (HL), and evaluated for body condition score (BCS). Afterward, they were managed according to standard farm procedures, including estrus detection and presynchronization and a 5 d Cosynch-72 protocol for cows that failed to show estrus. Ovaries were scanned at 27 and 41 ± 3 d in milk, and cows with a corpus luteum greater than 20 mm on at least 1 exam were considered cyclic. At 120 ± 3 d in milk (D120), cows were re-examined for HL and BCS. Cows were classified at D20 according to HL status as healthy (n = 1,197) or having HL (n = 429), and according to HL category as healthy (n = 1,197) or having a sole hemorrhage (n = 280), noninfectious HL (sole ulcer, toe ulcer, or white line disease; n = 113), or infectious HL (digital dermatitis and foot rot; n = 36). Cows with HL at D20 had reduced odds of being cyclic (38.3 vs. 51.9%) and a longer interval from calving to first service (58 vs. 51 d) compared with healthy cows. Cows with infectious HL at D20 had reduced odds of pregnancy to first service (16.7 vs. 38.3%) compared with healthy cows. Cows with sole hemorrhage at D20 were more likely to lose pregnancies between d 32 and 64 after the first service postpartum compared with healthy cows (10.5 vs. 5.2%). Cows with sole hemorrhage at D20 had a smaller hazard of pregnancy (67.9 vs. 75.5%) at 150 d in milk and more days open (88 vs. 77d) compared with healthy cows. To assess the relationship between the development of HL and fertility, cows were classified as healthy (no HL at D20 and D120; n = 308), cured (any HL at D20 and no HL at D120; n = 72), new HL (no HL at D20 and any HL at D120; n = 597), and chronic (any HL at D20 and D120; n = 226). Sole hemorrhage accounted for 93% of new HL. The proportions of cows with HL at D20 and D120 were 26.9 and 68.4%, respectively. We found no evidence for a difference in pregnancy hazard at 150 d in milk between cows that remained healthy (n = 308) and cows that developed new HL (n = 597). Hoof lesions at D20, but not new HL, were associated with decreased odds of cyclicity, longer interval from calving to first service postpartum, and reduced pregnancy hazard in Jersey cows. The effect of an HL diagnosis in early lactation and management to reduce chronic HL in dairy cows warrants further investigation.

Use of validated objective methods of locomotion characteristics and weight distribution for evaluating the efficacy of ketoprofen for alleviating pain in cows with limb pathologies

In veterinary practice pain alleviation plays a part in managing lameness. The aim of this randomized and placebo-controlled clinical study was to evaluate the effect of a single administration of ketoprofen on locomotion characteristics and weight distribution in cattle with foot (located up to and including the fetlock; n = 31) and (proximal to the fetlock; n = 10) pathologies. Cattle were randomly allocated to either the ketoprofen (group K; intravenous 3 mg/kg of body weight; n = 21) or an equivalent volume of isotonic sterile saline solution (group P; n = 20). Two accelerometers (400 Hz; kinematic outcome = stance phase duration; kinetic outcome = foot load and toe-off), a 4-scale weighing platform (weight distribution and SD of the weight) and a subjective locomotion score were measured before (baseline) and after 1 h and 18 h of treatment. All variables were expressed as differences across contralateral limbs, and the measurements at 1 h and 18 h were compared to the baseline. A repeated measures ANOVA was used to determine the differences between groups K and P. A logistic regression model with a binary outcome (0 = no improvement and 1 = improvement of the differences across the contralateral limbs over time) was calculated. Mean (± SD) of locomotion scores at baseline were not significantly different (P = 0.102) in group K (3.10 ± 0.80) as compared to group P (3.48 ± 0.64). Cattle of group K showed significantly lower differences across contralateral limbs at 1 h as compared to group P for the relative stance phase and the weight distribution. Only the treatment (P versus K) remained a significant factor in the model for relative stance phase (odds ratio (OR) = 6.5; 95% CI = 1.38–30.68) and weight distribution (OR = 6.36; 95% CI = 1.30–31.07). The effects of ketoprofen were evident in improving the differences across contralateral limbs—both for stance phase during walking and weight bearing during standing—after 1 h but not after 18 h of administration.

Dairy Farmers' Perceptions of and Actions in Relation to Lameness Management

Simple Summary

Lameness is a pressing issue in dairy production. Dairy farmers are primarily responsible for the welfare of their cows and decision-making regarding lameness management. However, there are concerns regarding the communication of the importance of lameness to farmers and their motivation towards proper management. A review of the literature indicates that various factors influence farmers’ perceptions about lameness, their adoption of recommended measures and whether or not they end up treating a lame cow. This review summarizes these related issues in consideration of the welfare and economic implications of farmers’ decisions. The information herein is vital to the identification of measures on how to motivate dairy farmers towards appropriate lameness management.

Abstract

Lameness continues to be a welfare and economic issue for dairy cows. However, the consequences of lameness seem to be better understood by veterinarians and related personnel in comparison to dairy farmers. Prompt detection and treatment of lame cows is essential in reducing its negative impact on milk processing systems. To that end, understanding farmers’ perceptions regarding the significance of lameness to dairy cows is vital. One fundamental aspect is the underestimation of lameness prevalence by dairy farmers, which is as a result of different understanding of the problem. The same applies to their decision to treat lame cows and to adopt various detection and management practices. All of these shortcomings contribute to poor cattle welfare and economic losses in dairy production. This review summarizes the results of studies that have investigated dairy farmers’ perceptions of lameness and the associated implications on the wellbeing and productivity of dairy cows. Factors associated with farmers’ attitudes toward claw health and lameness management are also presented. Additionally, economic observations relating to lameness prevention, treatment and the adoption of lameness detection systems are also highlighted. To strengthen these points, interventional programmes requiring farmers’ participation are discussed as a promising approach in answering some of these challenges. A review of the literature indicates both the opportunities and barriers inherent in the tackling the lameness issue from the farmers’ perspectives. Such knowledge is crucial in identifying measures on how to motivate dairy farmers towards proper lameness management.

Automatic lameness detection in cattle

There is an increasing demand for health and welfare monitoring in modern dairy farming. The development of various innovative techniques aims at improving animal behaviour monitoring and thus animal welfare indicators on-farm. Automated lameness detection systems have to be valid, reliable and practicable to be applied in veterinary practice or under farm conditions. The objective of this literature review was to describe the current automated systems for detection of lameness in cattle, which have been recently developed and investigated for application in dairy research and practice. The automatic methods of lameness detection broadly fall into three categories: kinematic, kinetic and indirect methods. The performance of the methods were compared with the reference standard (locomotion score and/or lesion score) and evaluated based on level-based scheme defining the degree of development (level I, sensor technique; level II, validation of algorithm; level III, performance for detection of lameness and/or lesion; level IV, decision support with early warning system). Many scientific studies have been performed on levels I-III, but there are no studies of level IV technology. The adoption rate of automated lameness detection systems by herd managers mainly yields returns on investment by the early identification of lame cows. Long-term studies, using validated automated lameness detection systems aiming at early lameness detection, are still needed in order to improve welfare and production under field conditions.

Objective assessment of lameness in cattle after foot surgery

Assessment of lameness in cattle after foot surgery is important to monitor the recovery period, to improve the long-term success and the cows` welfare. This longitudinal multicenter retrospective study was carried out to evaluate the usefulness of automated tools of weight bearing and gait analysis following foot surgery to support the clinician to monitor lameness in cattle. For this purpose, the effect of involvement of different anatomical structures and the use of different surgery methods on gait parameters of post-operative recovery was assessed. The study consisted of 2 experiments and included cattle with unilateral foot pathologies located in the digital region which needed 1 (experiment 1; n = 30) or 2 (experiment 2; n = 4) surgical interventions. The surgical techniques were debridement, joint lavage, partial resection of bones, tendons or synovial structures, total resection of the sesamoid bone and digit amputation. Two accelerometers (400 Hz; kinematic outcome = stance phase duration; kinetic outcome = foot load and toe-off), a 4-scale weighing platform (difference of mean weight distribution across the limbs; Δ_weight) and a subjective locomotion score were used to evaluate gait parameters every 3 to 4 days after surgery. A repeated measures ANOVA was used in experiment 1 and a receiver operator characteristic analysis was used to determine the optimal cutoff values in experiment 2. Results showed that the differences across limbs for the pedogram variables of stance phases and peaks of foot load and toe-off, Δ_weight and the locomotion score were highest if joints or sesamoid bones were involved, suggesting that these cattle were more severely lame compared to cattle with more superficial foot pathologies. There was a significantly lower degree of lameness after surgical debridement and after digit amputation compared to partial and total resection of anatomical structures of the foot. The use of accelerometers and a 4-scale weighing platform represent promising objective tools for post-operative monitoring of lameness and can support the clinician in gait assessment to improve the long-term success of surgical interventions in the area of the foot.

Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production

Consumption of animal products such as meat, milk, and eggs in first-world countries has leveled off, but it is rising precipitously in developing countries. Agriculture will have to increase its output to meet demand, opening the door to increased automation and technological innovation; intensified, sustainable farming; and precision livestock farming (PLF) applications. Early indicators of medical problems, which use sensors to alert cattle farmers early concerning individual animals that need special care, are proliferating. Wearable technologies dominate the market. In less-value-per-animal systems like sheep, goat, pig, poultry, and fish, one sensor, like a camera or robot per herd/flock/school, rather than one sensor per animal, will become common. PLF sensors generate huge amounts of data, and many actors benefit from PLF data. No standards currently exist for sharing sensor-generated data, limiting the use of commercial sensors. Technologies providing accurate data can enhance a well-managed farm. Development of methods to turn the data into actionable solutions is critical.

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.

Validation of a pedometer algorithm as a tool for evaluation of locomotor behaviour in dairy Mediterranean buffalo

This research communication validates an algorithm to monitor natural occurrence of locomotor behaviours in dairy Mediterranean buffalo based on the output of a 3-dimensional accelerometer (RumiWatch®, pedometer). Several characteristics of the locomotor behaviour were detected with a very high (up-right, lying and standing time) or high degree of correlation (walking time and number of strides) and a low mean difference with the video recording. The proportion of correctly detected events exceeded 99 % for the following variables: stand up and lie down events, as well as number of lying, standing or walking bouts. The mean relative measurement error was less than 10 % for the variables: lying, standing, up-right times and number of strides as compared with gold standard. This new algorithm may represent the base for a future early and real-time disease warning system aiming to gain higher health standard in these ruminants.