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The biodiversity of our planet is under threat, with approximately one million species expected to become extinct within decades. The reason: negative human actions, which include hunting, overfishing, pollution, and the conversion of land for urbanisation and agricultural purposes. Despite significant investment from charities and governments for activities that benefit nature, global wildlife populations continue to decline. Local wildlife guardians have historically played a critical role in global conservation efforts and have shown their ability to achieve sustainability at various levels. In 2021, COP26 recognised their contributions and pledged USD 1.7 billion per year; however this is a fraction of the global biodiversity budget available (between USD 124 billion and USD 143 billion annually) given they protect 80% of the planets biodiversity. This paper proposes a radical new solution based on "Interspecies Money", where animals own their own money. Creating a digital twin for each species allows animals to dispense funds to their guardians for the services they provide. For example, a rhinoceros may release a payment to its guardian each time it is detected in a camera trap as long as it remains alive and well. To test the efficacy of this approach, 27 camera traps were deployed over a 400 km 2 area in Welgevonden Game Reserve in Limpopo Province in South Africa. The motion-triggered camera traps were operational for ten months and, using deep learning, we managed to capture images of 12 distinct animal species. For each species, a makeshift bank account was set up and credited with GBP 100. Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount—mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian. The trial demonstrated that it is possible to achieve high animal detection accuracy across the 12 species with a sensitivity of 96.38%, specificity of 99.62%, precision of 87.14%, F1 score of 90.33%, and an accuracy of 99.31%. The successful detections facilitated the transfer of GBP 185.20 between animals and their associated guardians. [ABSTRACT FROM AUTHOR]

Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Declines in bird populations can result in reduced ecosystem services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time-consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: (a) cameras generate many images, making it difficult to process and analyse the data in a timely manner; and (b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classification of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cameras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics, thereby removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring. [ABSTRACT FROM AUTHOR]

Background: Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths.Methods: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤ 7.20-acidosis, n = 18; pH > 7.20 and pH < 7.25-foetal deterioration, n = 4; or clinical decision without evidence of pathological outcome measures, n = 24). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures.Results: The findings show that deep learning classification achieves sensitivity = 94%, specificity = 91%, Area under the curve = 99%, F-score = 100%, and mean square error = 1%.Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies. [ABSTRACT FROM AUTHOR]

The growing trend of obesity and overweight worldwide has reached epidemic proportions with one third of the global population now considered obese. This is having a significant medical impact on children and adults who are at risk of developing osteoarthritis, coronary heart disease and stroke, type 2 diabetes, cancers, respiratory problems, and non-alcoholic fatty liver disease. In an attempt to redress the issue, physical activity is being promoted as a fundamental component for maintaining a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of their public health measures. However, current techniques and protocols, including those used in laboratory settings, have been criticised. The main concern is that it is not feasible to use multiple pieces of measurement hardware, such as VO2 masks and heart rate monitors, to monitor children in free-living environments due to weight and encumbrance constraints. This has prompted research in the use of wearable sensing and machine learning technology to produce classifications for specific physical activity events. This paper builds on this approach and presents a supervised machine learning method that utilises data obtained from accelerometer sensors worn by children in free-living environments. Our results show that when using an artificial neural network algorithm it is possible to obtain an overall accuracy of 96% using four features from the initial dataset, with sensitivity and specificity values equal to 95% and 99% respectively. Expanding the dataset with interpolated cases, it was possible to improve on these results with 98.8% for accuracy, and 99% for sensitivity and specificity when four features were used. [ABSTRACT FROM AUTHOR]

Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilised, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven different artificial neural networks were then used to identify these records. The results illustrate that the combination of the Levenberg–Marquardt trained Feed-Forward Neural Network, Radial Basis Function Neural Network and the Random Neural Network classifiers performed the best, with 91% for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate. [ABSTRACT FROM AUTHOR]

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders. [ABSTRACT FROM AUTHOR]

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier. [ABSTRACT FROM AUTHOR]

A healthy lifestyle has the ability not only to give you more energy and help you look and feel better, but it also has the ability to help you live longer and prevent disease, such as obesity and pressure ulcers. This is particularly important for the elderly population, as a healthier lifestyle would enable independent living to occur for a longer period of time. However, providing a direct link between increasing physical activity and positive health outcomes is a problem. The effect of leading an increasing sedentary lifestyle is also not evident straightaway. Effects of this behavior often occur over years and decades, as opposed to days or months. Therefore, there is very little willingness to change, if instant results are not seen. There is a need to provide a mechanism that is able to monitor an individual and provide a visual indication of his or her behavior. It is envisioned that the area of human digital memories is capable of providing such a system. This article explores how sedentary behavior and journey information can be collected, from different environments, so that an illustration of a user's habits can be seen and changes can occur. A successful prototype has also been developed that evaluates the applicability of the approach. [ABSTRACT FROM AUTHOR]

Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as distance and occlusion, coupled with increased vegetation density, present the most significant effect on DP and CC. Overall, the results indicate a significantly higher detection probability (DP), 81.1%, and correct classification (CC) of 76.6% for the analyst approach when compared to ML which detected 41.1% and classified 47.5% of wildlife within CT images. However, both methods presented similar probabilities for daylight CT images, 69.4% (ML) and 71.8% (analysts), and dusk CT images, 17.6% (ML) and 16.2% (analysts), when detecting humans. Given that users carefully follow provided recommendations, we expect DP and CC to increase. In turn, the ML approach to CT image processing would be an excellent provision to support time-sensitive threat monitoring for biodiversity conservation. [ABSTRACT FROM AUTHOR]

Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a machine learning (ML) approach to model normal working operations and detect anomalies. The approach extracts key features from signals representing a known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system where green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure. [ABSTRACT FROM AUTHOR]

Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies. [ABSTRACT FROM AUTHOR]

The rising demand for health and social care, and around the clock monitoring services, is increasing and are unsustainable under current care provisions. Consequently, a safe and independent living environment is hard to achieve; yet the detection of sudden or worsening changes in a patient's condition is vital for early intervention. The use of smart technologies in primary care delivery is increasing significantly. However, substantial research gaps remain in non-invasive and cost effective monitoring technologies. The inability to learn the unique characteristics of patients and their conditions seriously limits the effectiveness of most current solutions. The smart metering infrastructure provides new possibilities for a variety of applications that are unachievable using the traditional energy grid. By 2020, UK energy suppliers will install 50 million smart meters, therefore, providing access to a highly accurate sensing network. Each smart meter records the electrical load for a given property at 30 minute intervals. This granular data captures detailed habits and routines through the occupant's interactions with electrical devices, enabling the detection and identification of alterations in behaviour. The research presented in this paper explores how this data could be used to achieve a safe living environment for people living with progressive neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

In 2016, there were 317 serious water pollution incidents in the UK, with 78,000 locations where businesses discharge controlled quantities of pollutants into rivers; therefore, continuous monitoring is vital. Since 1998, the environment agency has taken over 50 million water samples for water quality monitoring. The Internet of Things has grown phenomenally in recent years, reaching all aspects of our lives, many of these connected devices use wireless sensor networks to relay data to internet-connected nodes, where data can be processed, analyzed and consumed. However, Underwater wireless communications rely mainly on alternative communication methods such as optical and acoustic, with radio frequencies being an under-exploited method. This research presents real world results conducted in the Leeds and Liverpool Canal for the novel use of the 433 MHz radio frequency combined with a bowtie antenna in underwater communications in raw water, achieving distances of 7 m at 1.2 kbps and 5 m at 25 kbps. [ABSTRACT FROM AUTHOR]

This paper discusses the use of machine learning techniques for the classification of medical data, specifically for guiding disease modifying therapies for Sickle Cell. Extensive research has indicated that machine learning approaches generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. The aim of this paper is to present findings for several classes of learning algorithm for medically related problems. The initial case study addressed in this paper involves classifying the dosage of medication required for the treatment of patients with Sickle Cell Disease. We use different machine learning architectures in order to investigate the accuracy and performance within the case study. The main purpose of applying classification approach is to enable healthcare organisations to provide accurate amount of medication. The results obtained from a range of models during our experiments have shown that of the proposed models, recurrent networks produced inferior results in comparison to conventional feedforward neural networks and the Random Forest model. For our dataset, it was found that the Random Forest Classifier produced the highest levels of performance overall. [ABSTRACT FROM AUTHOR]

Monitoring animals' behavior living in wild or semi-wild environments is a very interesting subject for biologists who work with them. The difficulty and cost of implanting electronic devices in this kind of animals suggest that these devices must be robust and have low power consumption to increase their battery life as much as possible. Designing a custom smart device that can detect multiple animal behaviors and that meets the mentioned restrictions presents a major challenge that is addressed in this work. We propose an edge-computing solution, which embeds an ANN in a microcontroller that collects data from an IMU sensor to detect three different horse gaits. All the computation is performed in the microcontroller to reduce the amount of data transmitted via wireless radio, since sending information is one of the most power-consuming tasks in this type of devices. Multiples ANNs were implemented and deployed in different microcontroller architectures in order to find the best balance between energy consumption and computing performance. The results show that the embedded networks obtain up to 97.96% ± 1.42% accuracy, achieving an energy efficiency of 450 Mops/s/watt. [ABSTRACT FROM AUTHOR]

Averting today's loss of biodiversity and ecosystem services can be achieved through conservation efforts, especially of keystone species. Giraffes (Giraffa camelopardalis) play an important role in sustaining Africa's ecosystems, but are 'vulnerable' according to the IUCN Red List since 2016. Monitoring an animal's behavior in the wild helps to develop and assess their conservation management. One mechanism for remote tracking of wildlife behavior is to attach accelerometers to animals to record their body movement. We tested two different commercially available high-resolution accelerometers, e-obs and Africa Wildlife Tracking (AWT), attached to the top of the heads of three captive giraffes and analyzed the accuracy of automatic behavior classifications, focused on the Random Forests algorithm. For both accelerometers, behaviors of lower variety in head and neck movements could be better predicted (i.e., feeding above eye level, mean prediction accuracy e-obs/AWT: 97.6%/99.7%; drinking: 96.7%/97.0%) than those with a higher variety of body postures (such as standing: 90.7–91.0%/75.2–76.7%; rumination: 89.6–91.6%/53.5–86.5%). Nonetheless both devices come with limitations and especially the AWT needs technological adaptations before applying it on animals in the wild. Nevertheless, looking at the prediction results, both are promising accelerometers for behavioral classification of giraffes. Therefore, these devices when applied to free-ranging animals, in combination with GPS tracking, can contribute greatly to the conservation of giraffes. [ABSTRACT FROM AUTHOR]

Use of technology in health care has risen exponentially in recent years alongside the increased demand for healthcare services. In a climate of rising healthcare costs innovations in the use of technology may provide a cost effective means of interacting with clients. Programmes such as Skype enable sound and video calls between devices. Remote live interactions using Skype could improve the quality of care by increasing the level of accessibility, reach and convenience between health professionals and their clients. In Speech and Language Therapy (SLT) there may be additional benefits to care being provided in the clients' natural environment via videoconferencing as the surroundings are more meaningful to them. Speech and Language Therapists (SLTs) in private practice are implementing its use, despite limited evidence of its effectiveness. We aimed to address this issue and provide data on its use within clinical practice in an NHS setting. In a prospective pilot study, seven SLTs and their multiple clients were provided with tablet computer devices to enable them to have 'virtual sessions' via Skype instead of 'face to face' consultations. We incorporated an evaluation into the pilot and assessed client and therapists perceptions of using Skype instead of face to face contact. Information on resource savings, e.g. time to travel to clinic, travel costs, was collected with a questionnaire app on the device that sent this anonymised data to a remote server at the university. Clients and therapists were also interviewed at the end of the pilot. Implementing the service required governance and clinical practice within the Trust to be accommodated. There were few technical difficulties encountered throughout the pilot, and any encountered were addressed quickly. The majority of therapists and clients were overwhelmingly positive about the use of Skype and cited multiple benefits relating to convenience, time saved, reduced costs of travel and greater flexibility of access to the service and clients. These included SLTs offering multiple shorter 'virtual' sessions in the home, instead of a longer session in school, negating the need to take the child out of lessons and greater engagement in therapy by clients. The presentation will discuss some practical issues with delivery therapy 'virtually' and highlight the benefits to SLTs and their specific clients. [ABSTRACT FROM AUTHOR]