Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, responsible for 32% of all deaths, with the annual death toll projected to reach 23.3 million by 2030. The early identification of individuals at high risk of CVD is crucial for the effectiveness of preventive strategies. In the field of deep learning, automated CVD-detection methods have gained traction, with phonocardiogram (PCG) data emerging as a valuable resource. However, deep-learning models rely on large datasets, which are often challenging to obtain. In recent years, data augmentation has become a viable solution to the problem of scarce data. In this paper, we propose a novel data-augmentation technique named PCGmix, specifically engineered for the augmentation of PCG data. The PCGmix algorithm employs a process of segmenting and reassembling PCG recordings, incorporating meticulous interpolation to ensure the preservation of the cardinal diagnostic features pertinent to CVD detection. The empirical assessment of the PCGmix method was utilized on a publicly available database of normal and abnormal heart-sound recordings. To evaluate the impact of data augmentation across a range of dataset sizes, we conducted experiments encompassing both limited and extensive amounts of training data. The experimental results demonstrate that the novel method is superior to the compared state-of-the-art, time-series augmentation. Notably, on limited data, our method achieves comparable accuracy to the no-augmentation approach when trained on 31% to 69% larger datasets. This study suggests that PCGmix can enhance the accuracy of deep-learning models for CVD detection, especially in data-constrained environments.

A rapidly ageing population in the developed world brings a necessity and opportunity for the AI-based ICT solutions. We present a system, developed within the scope of an EU H2020 project IN LIFE, which aims to prolong the age at which individual can still live at home independently while at the same time increasing comfort and safety. In this demo we present the final result of the project, a virtual AI carer monitoring user 24/7 in a form of a smartwatch. The system provides a range of useful services among which fall detection and activity monitoring are highlighted here. The device and accompanying services were tested during the project on 150 elderly users for several months, their feedback was taken into account for potential future improvements.

Information technologies can assist the elderly in their daily lives and thus improve their quality of life. Here, we present a system that was developed within the framework of the EU H2020 project IN LIFE to make the elderly feel more secure. The system consists of a smartwatch, a tablet, and a web portal for carers. The smartwatch has several functionalities; the most important are automatic fall detection, SOS button, communication with carers, location detection in case of emergency, and general activity monitoring. The smartwatch communicates with the server via GSM, therefore it does not depend on wireless internet or Bluetooth connection. The tablet acts as a virtual doorman and also as an interface for the carer, while the portal acts as a central information hub for notifications, scheduling of tasks, and user overview. We present the initial feedback from the users in a pilot study and discuss some modifications that we carried out considering this feedback.

Chronic heart failure represents a global pandemic, currently affecting over 26 million of patients worldwide. It is a major contributor in the death rate of patients with cardiovascular diseases and results in more than 1 million hospitalizations annually in Europe and North America. Methods for chronic heart failure detection can be utilized to act preventive, improve early diagnosis and avoid hospitalizations or even life-threatening situations, thus highly enhance the quality of patient’s life. In this paper, we present a machine-learning method for chronic heart failure detection from heart sounds. The method consists of: filtering, segmentation, feature extraction and machine learning. The method was tested with a leave-one-subject-out evaluation technique on data from 122 subjects, gathered in the study. The method achieved 96% accuracy, outperforming a majority classifier for 15 percentage points. More specifically, it detects (recalls) 87% of the chronic heart failure subjects with a precision of 87%. The study confirmed that advanced machine learning applied on real-life sounds recorded with an unobtrusive digital stethoscope can be used for chronic heart failure detection.

Abstract Refrigeration systems have been a vital component of our lives for more than a century. Apart from storing food, they are used to store sensitive goods such as pharmaceutical products or reactive chemicals. The deterioration of the refrigeration system performance due to aging or malfunction directly affects the quality of stored goods. Therefore, an early detection of deviation in performance is an important task. This paper presents a system that monitors operation of refrigeration devices and alerts the user to possible irregularities in the operation run. The emphasis is on recognition of gradual changes of performance that indicate upcoming hardware problems. The system consists of 2 modules: human‐defined expert rules and machine learning. The machine‐learning module learns to recognize abnormal behaviour of devices automatically. Furthermore, it can distinguish between different abnormal events and allow the user to classify some of the types as normal, so that they not longer raise an alarm. The machine learning was evaluated by comparing its recognition of abnormal events and classification accuracy of such events to the performance of a human operator. The system can in principle be adapted to any electronic device that periodically applies some system for sustaining a predefined quality (e.g., temperature).

In this paper we propose a method for continuous stress monitoring using data provided by a commercial wrist device equipped with common physiological sensors and an accelerometer. The method consists of three machine-learning components: a laboratory stress-detector that detects short-term stress every 2 minutes; an activity recognizer that continuously recognizes user's activity and thus provides context information; and a context-based stress detector that first aggregates the predictions of the laboratory detector, and then exploits the user's context in order to provide the final decision in a 20 minute interval. The method was trained on 21 subjects in a laboratory setting and tested on 5 subjects in a real-life setting. The accuracy on 55 days of real-life data was 92%. The method is currently being implemented as a smartphone application, which will be demonstrated at the conference.

The constant increase of the elderly population and the need to prolong the independent live, is driving the development of telecare systems. Two of the key factors of such systems are their simplicity of usage and minimal obtrusiveness. Therefore, in this paper we propose a practical, intuitive and simple smartwatch-based telecare system. The system has several functionalities, including: automatic fall detection, activity analysis, SOS red button, location information, and reminders. The proposed system can work indoors as well as outdoors, and is completely independent, i.e., it requires only a SIM card to function and does not depend on base stations, internet connection, Bluetooth and similar. The evaluation of the fall detection algorithm showed that it detects all of the fast falls and minimizes the false positives, achieving 85% accuracy.

This paper presents an approach to detecting perceived stress in students using data collected with smartphones. The goal is to develop a machine-learning model that can unobtrusively detect the stress level in students using data from several smartphone sources: accelerometers, audio recorder, GPS, Wi-Fi, call log and light sensor. From these, features were constructed describing the students' deviation from usual behaviour. As ground truth, we used the data obtained from stress level questionnaires with three possible stress levels: "Not stressed", "Slightly stressed" and "Stressed". Several machine learning approaches were tested: a general models for all the students, models for cluster of similar students, and student-specific models. Our findings show that the perceived stress is highly subjective and that only person-specific models are substantially better than the baseline.

Energy demand in a smart grid is directly related to energy consumption, as defined by user needs and comfort experience. This article presents a multi-agent architecture for smart control of space heating and cooling processes, in an attempt to enable flexible ways of monitoring and adjusting energy supply and demand. In this proposed system, control agents are implemented in order to perform temperature set-point delegation for heating and cooling systems in a building, offering a means to observe and learn from both the environment and the occupant. Operation of the proposed algorithms is compared with traditional algorithms utilized for room heating, using a simulated model of a residential building and real data about user behaviour. The results show (i) the performance of machine learning for the occupancy forecasting problem and for the problem of calculating the time to heat or cool a room; and (ii) the performance of the control algorithms, with respect to energy consumption and occupant comfort. The proposed control agents make it possible to significantly improve an occupant comfort with a relatively small increase in energy consumption, compared with simple control strategies that always maintain predefined temperatures. The findings enable the smart grid to anticipate the energy needs of the building.

Activity recognition with a single accelerometer placed on the torso is fairly common task, but distinguishing standing from sitting in this way is very difficult because the torso is oriented the same way during both activities, and the transition between the two is very hard to classify into going down or up. We propose a novel approach based on the Multiple Contexts Ensemble (MCE) algorithm which classifies the activity with an ensemble of classifiers, each of which considers the problem in the context of a single feature. The improvement stems from using multiple viewpoints, based on accelerometer data only, designed specifically to distinguish standing from sitting. This approach improves the accuracy on the two activities by 24 percentage points compared to regular machine learning.

We propose a multi-objective machine learning approach guaranteed to find the Pareto optimal set of hybrid classification models consisting of comprehensible and incomprehensible submodels. The algorithm run-times are below 1 s for typical applications despite the exponential worst-case time complexity. The user chooses the model with the best comprehensibility-accuracy trade-off from the Pareto front which enables a well informed decision or repeats finding new Pareto fronts with modified seeds. For a classification trees as the comprehensible seed, the hybrids include single black-box model, invoked in hybrid leaves. The comprehensibility of such hybrid classifiers is measured with the proportion of examples classified by the regular leaves. We propose one simple and one computationally efficient algorithm for finding the Pareto optimal hybrid trees, starting from an initial classification tree and a black-box classifier. We evaluate the proposed algorithms empirically, comparing them to the baseline solution set, showing that they often provide valuable improvements. Furthermore, we show that the efficient algorithm outperforms the NSGA-II algorithm in terms of quality of the result set and efficiency (for this optimisation problem). Finally we show that the algorithm returns hybrid classifiers that reflect the expert's knowledge on activity recognition problem well.

Computer-aided practice can help improve personnel training for demanding scenarios in terms of time and quality. In this paper, we concentrate on asymmetrical conflicts, such as a unit that deals with hostile crowds robbing a store, with the aim of preventing further criminal activity and at the same time minimizing physical and emotional damage. We propose a surrogate-agent modeling approach based on execution of the following loop: (i) observe a human (the unit leader) playing a set of scenarios in a simulated environment and induce strategic patterns of human play; (ii) use patterns to construct a surrogate agent (digital clone); (iii) test the surrogate under all possible circumstances through data farming; and (iv) evaluate the performance and highlight deficiencies in the agent's responses, thereby enabling human improvements in new attempts. Experiments on two domains indicate that the proposed approach could significantly improve the training procedure and help trainees to properly perceive the cognitive properties of the crowds.

Abstract This paper presents a method for combining domain knowledge and machine learning (CDKML) for classifier generation and online adaptation. The method exploits advantages in domain knowledge and machine learning as complementary information sources. Whereas machine learning may discover patterns in interest domains that are too subtle for humans to detect, domain knowledge may contain information on a domain not present in the available domain dataset. CDKML has three steps. First, prior domain knowledge is enriched with relevant patterns obtained by machine learning to create an initial classifier. Second, genetic algorithms refine the classifier. Third, the classifier is adapted online on the basis of user feedback using the Markov decision process. CDKML was applied in fall detection. Tests showed that the classifiers developed by CDKML have better performance than machine‐learning classifiers generated on a training dataset that does not adequately represent all real‐life cases of the learned concept. The accuracy of the initial classifier was 10 percentage points higher than the best machine‐learning classifier and the refinement added 3 percentage points. The online adaptation improved the accuracy of the refined classifier by an additional 15 percentage points.

Monitoring human energy expenditure is important in many health and sport applications, since the energy expenditure directly reflects the level of physical activity. The actual energy expenditure is unpractical to measure; hence, the field aims at estimating it by measuring the physical activity with accelerometers and other sensors. Current advanced estimators use a context-dependent approach in which a different regression model is invoked for different activities of the user. In this paper, we go a step further and use multiple contexts corresponding to multiple sensors, resulting in an ensemble of models for energy expenditure estimation. This provides a multi-view perspective, which leads to a better estimation of the energy. The proposed method was experimentally evaluated on a comprehensive set of activities where it outperformed the current state-of-the-art.

We present a health-monitoring system based on the multidimensional dynamic time warping approach with semantic attributes for the detection of health problems in the elderly to prolong their autonomous living. The movement of the elderly user is captured with a motion-capture system that consists of body-worn tags, whose coordinates are acquired by sensors located in an apartment. The output time series of the coordinates are modeled with the proposed data-mining approach in order to recognize the specific health problem of an elderly person. This paper is an extension of our previous study, which proposed four data mining approaches to recognition of health problems, falls and activities of elderly from their motion patterns. The most successful of the four approaches is SMDTW (Multidimensional dynamic time-warping approach with semantic attributes), whose version is used and thoroughly analyzed in this paper. SMDTW is the modification of the DTW algorithm to use with the multidimensional time series with semantic attributes. To test the robustness of the SMDTW approach, this study calculates the DTW on the time series of various lengths. The semantic attributes presented here consist of the joint angles that are able to recognize many types of movement, e.g., health problems, falls and activities, in contrast to the more specific approaches with specific medically defined attributes from the literature. The k-nearest-neighbor classifier using SMDTW as a distance measure classifies movement of an elderly person into five different health states: one healthy and four unhealthy. Even though the new approach is more general and can be used to differentiate other types of activities or health problems, it achieves very high classification accuracy of 97.2%, comparable to the more specific approaches.

In practice, an incomplete heuristic search nearly always finds better solutions if it is allowed to search deeper, i.e. expand and heuristically evaluate more nodes in the search tree. On the rare occasions when searching deeper is not beneficial, a curious phenomenon called ‘search pathology’ occurs. In this article, we study the pathology and gain of a deeper search of the minimin algorithm in the 8-puzzle, a domain often used for evaluating single-agent search algorithms. We have analysed the influence of various properties of the search tree and the heuristic evaluation function on the gain and the pathology. In order to investigate a broad range of the properties, the original 8-puzzle was extended with diagonal moves, yielding a larger variety of search trees. It turned out that in the 8-puzzle, a substantial proportion of the solvable positions is pathological under various parameters. More importantly, the search parameters that enable the highest gains are quite consistent in pathological and non-pathological positions alike, thus pointing to potentially successful search strategies.

This paper proposes a system for the early automatic recognition of health problems that manifest themselves in distinctive form of gait. Purpose of the system is to prolong the autonomous living of the elderly at home. When the system identifies a health problem, it automatically notifies a physician and provides an explanation of the automatic diagnosis. The gait of the elderly user is captured using a motion-capture system, which consists of body-worn tags and wall-mounted sensors. The positions of the tags are acquired by the sensors and the resulting time series of position coordinates are analyzed with machine-learning algorithms in order to recognize a specific health problem. Novel semantic features based on medical knowledge for training a machine-learning classifier are proposed in this paper. The classifier classifies the user’s gait into: 1) normal, 2) with hemiplegia, 3) with Parkinson’s disease, 4) with pain in the back and 5) with pain in the leg. The studies of 1) the feasibility of automatic recognition and 2) the impact of tag placement and noise level on the accuracy of the recognition of health problems are presented. The experimental results of the first study (12 tags, no noise) showed that the k-nearest neighbors and neural network algorithms achieved classification accuracies of 100%. The experimental results of the second study showed that classification accuracy of over 99% is achievable using several machine-learning algorithms and 8 or more tags with up to 15 mm standard deviation of noise. The results show that the proposed approach achieves high classification accuracy and can be used as a guide for further studies in the increasingly important area of Ambient Assisted Living. Since the system uses semantic features and an artificial-intelligence approach to interpret the health state, provides a natural explanation of the hypothesis and is embedded in the domestic environment of the elderly person; it is an example of the semantic ambient media for Ambient Assisted Living.

Due to the rapid aging of the population, many technical solutions for the care of the elderly are being developed, often involving fall detection with accelerometers. We present a novel approach to fall detection with location sensors. In our application, a user wears up to four tags on the body whose locations are detected with radio sensors. This makes it possible to recognize the user’s activity, including falling any lying afterwards, and the context in terms of the location in the apartment. We compared fall detection using location sensors, accelerometers and accelerometers combined with the context. A scenario consisting of events difficult to recognize as falls or non- falls was used for the comparison. The accuracy of the methods that utilized the context was almost 40 percentage points higher compared to the methods without the context. The accuracy of pure location-based methods was around 10 percentage points higher than the accuracy of accelerometers combined with the context.

This paper presents an approach to fall detection with accelerometers that exploits posture recognition to identify postures that may be the result of a fall. Posture recognition as a standalone task was also studied. Nine placements of up to four sensors were considered: on the waist, chest, thigh and ankle. The results are compared to the results of a system using ultra wide band location sensors on a scenario consisting of events difficult to recognize as falls or non-falls. Three accelerometers proved sufficient to correctly recognize all the events except one(a slow fall). The location-based system was comparable to two accelerometers, except that it was able to recognize the slow fall because it resulted in lying outside the bed, whose location was known to the system. One accelerometer was able to recognize only the most clear-cut fall. Two accelerometers achieved over 90% accuracy of posture recognition, which was better than the location-based system. Chest and waist accelerometers proved best at both tasks, with the chest accelerometer having a slight advantage in posture recognition.

A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with dynamic time warping and machine learning algorithms in order to identify the specific gait anomaly. We designed medically oriented features for training a machine learning classifier that classifies the user's gait into: i) normal, ii) with hemiplegia, iii) with Parkinson's disease, iv) with pain in the back and v) with pain in the leg. Experimental results show that the proposed tool is usable for discovery of gait anomalies.

Agent-oriented software engineering is promising substantial improvements especially for the design of distributed systems. But currently, there is a substantial gap between the massive number of publications advocating advantages of agents and the relatively small number of successful large applications. We present an agent-based cluster server application together with the analysis of advantages and disadvantages of the agent-based version compared to the non agent-based version of functionally similar cluster server application. Obtained experience is analyzed from the viewpoints of designers, developers and users. Observations confirm several previously published advantages of the agent-based approach and implementation: yet reveal some less familiar disadvantages as well. Overall, we argue that agents provide reasonable benefits already at this stage, while we lack further agent-based research.