Activity Recognition in Smart Homes via Feature-Rich Visual Extraction of Locomotion Traces

The proliferation of sensors in smart homes makes it possible to monitor human activities, routines, and complex behaviors in an unprecedented way. Hence, human activity recognition has gained increasing attention over the last few years as a tool to improve healthcare and well-being in several applications. However, most existing activity recognition systems rely on cameras or wearable sensors, which may be obtrusive and may invade the user's privacy, especially at home. Moreover, extracting expressive features from a stream of data provided by heterogeneous smart-home sensors is still an open challenge. In this paper, we investigate a novel method to detect activities of daily living by exploiting unobtrusive smart-home sensors (i.e., passive infrared position sensors and sensors attached to everyday objects) and vision-based deep learning algorithms, without the use of cameras or wearable sensors. Our method relies on depicting the locomotion traces of the user and visual clues about their interaction with objects on a floor plan map of the home, and utilizes pre-trained deep convolutional neural networks to extract features for recognizing ongoing activity. One additional advantage of our method is its seamless extendibility with additional features based on the available sensor data. Extensive experiments with a real-world dataset and a comparison with state-of-the-art approaches demonstrate the effectiveness of our method

Sensor-Based Locomotion Data Mining for Supporting the Diagnosis of Neurodegenerative Disorders: A Survey

Locomotion characteristics and movement patterns are reliable indicators of neurodegenerative diseases (NDDs). This survey provides a systematic literature review of locomotion data mining systems for supporting NDD diagnosis. We discuss techniques for discovering low-level locomotion indicators, sensor data acquisition and processing methods, and NDD detection algorithms. The survey presents a comprehensive discussion on the main challenges for this active area, including the addressed diseases, locomotion data types, duration of monitoring, employed algorithms, and experimental validation strategies. We also identify prominent open challenges and research directions regarding ethics and privacy issues, technological and usability aspects, and availability of public benchmarks

Explainable AI-powered Graph Neural Networks for HD EMG-Based Gesture Intention Recognition

The ability to recognize fine-grained gestures enables several applications in different domains, including healthcare, robotics, remote control, and human-computer interaction. Traditional gesture recognition systems rely on data acquired from cameras, depth sensors, or smart gloves. More recently, techniques for recognizing gestures based on signals acquired by high-density (HD) EMG electrodes worn on the forearm have been proposed. An advantage of these techniques is that they do not rely on the use of external devices, and they are feasible also to people who underwent amputation. Unfortunately, the extraction of complex features from raw HD EMG signals may introduce delays that deter the real-time requirements of the system. To address this issue, in a preliminary investigation we proposed to use graph neural networks for gesture recognition from raw HD EMG data. In this paper, we extend our previous work by exploiting Explainable AI algorithms to automatically refine the graph topology based on the data in order to improve recognition rates and reduce the computational cost. We performed extensive experiments with a large dataset collected from 20 volunteers regarding the execution of 65 fine-grained gestures, comparing our technique with state-of-the-art methods based on handcrafted features and different machine learning algorithms. Experimental results show that our technique outperforms the state of the art in terms of recognition performance while incurring significantly lower computational cost at run-time

FootApp: An AI-powered system for football match annotation

In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players’ and team actions, as well as the athletic performance of players. Consequently, annotating football events at a fine-grained level is a very expensive and error-prone task. Most existing semi-automatic tools for football match annotation rely on cameras and computer vision. However, those tools fall short in capturing team dynamics and in extracting data of players who are not visible in the camera frame. To address these issues, in this manuscript we present FootApp, an AI-based system for football match annotation. First, our system relies on an advanced and mixed user interface that exploits both vocal and touch interaction. Second, the motor performance of players is captured and processed by applying machine learning algorithms to data collected from inertial sensors worn by players. Artificial intelligence techniques are then used to check the consistency of generated labels, including those regarding the physical activity of players, to automatically recognize annotation errors. Notably, we implemented a full prototype of the proposed system, performing experiments to show its effectiveness in a real-world adoption scenario

Replication and discovery of musculoskeletal QTLs in LG/J and SM/J advanced intercross lines

AR056280 awarded to DAB and AL. AIHC supported by IMS and Elphinstone Scholarship from the University of Aberdeen. GRV supported by Medical Research Scotland (Vac-929-2016).Peer reviewedPublisher PD

Sphingosine-1-Phosphate in the Tumor Microenvironment: A Signaling Hub Regulating Cancer Hallmarks

As a key hub of malignant properties, the cancer microenvironment plays a crucial role intimately connected to tumor properties. Accumulating evidence supports that the lysophospholipid sphingosine-1-phosphate acts as a key signal in the cancer extracellular milieu. In this review, we have a particular focus on glioblastoma, representative of a highly aggressive and deleterious neoplasm in humans. First, we highlight recent advances and emerging concepts for how tumor cells and different recruited normal cells contribute to the sphingosine-1-phosphate enrichment in the cancer microenvironment. Then, we describe and discuss how sphingosine-1-phosphate signaling contributes to favor cancer hallmarks including enhancement of proliferation, stemness, invasion, death resistance, angiogenesis, immune evasion and, possibly, aberrant metabolism. We also discuss the potential of how sphingosine-1-phosphate control mechanisms are coordinated across distinct cancer microenvironments. Further progress in understanding the role of S1P signaling in cancer will depend crucially on increasing knowledge of its participation in the tumor microenvironment

Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues †

Research on the analysis of counselling conversations through natural language processing methods has seen remarkable growth in recent years. However, the potential of this field is still greatly limited by the lack of access to publicly available therapy dialogues, especially those with expert annotations, but it has been alleviated thanks to the recent release of AnnoMI, the first publicly and freely available conversation dataset of 133 faithfully transcribed and expert-annotated demonstrations of high- and low-quality motivational interviewing (MI)-an effective therapy strategy that evokes client motivation for positive change. In this work, we introduce new expert-annotated utterance attributes to AnnoMI and describe the entire data collection process in more detail, including dialogue source selection, transcription, annotation, and post-processing. Based on the expert annotations on key MI aspects, we carry out thorough analyses of AnnoMI with respect to counselling-related properties on the utterance, conversation, and corpus levels. Furthermore, we introduce utterance-level prediction tasks with potential real-world impacts and build baseline models. Finally, we examine the performance of the models on dialogues of different topics and probe the generalisability of the models to unseen topics

Pessimistic Rescaling and Distribution Shift of Boosting Models for Impression-Aware Online Advertising Recommendation

In this paper, we provide an overview of the approach we used as team Gabibboost for the ACM RecSys Challenge 2023, organized by ShareChat and Moj. The challenge focused on predicting user activity in the online advertising setting based on impression data, in particular, predicting whether a user would install an advertised application using a high-dimensional anonymized feature vector. Our proposed solution is based on an ensemble model that combines the strengths of several machine learning sub-models, including CatBoost, LightGBM, HistGradientBoosting, and two hybrid models. Our proposal is able to harness the strengths of our models through a distribution shift postprocessing and fine-Tune the final prediction via a custom build pessimistic rescaling function. The final ensemble model allowed us to rank 1st on the academic leaderboard and 9th overall

Aluminum alloy production for the reinforcement of the CMS conductor

The Compact Muon Solenoid (CMS) is one of the general-purpose detectors to be provided for the Large Hadron Collider (LHC) project at CERN. The design field of the CMS superconducting magnet is 4 T, the magnetic length is 12.5 m and the free bore is 6 m. To reinforce the high-purity (99.998%) Al-stabilized conductor of the magnet against the magnetic loadings experienced during operation at 4.2 K, two continuous sections of Al-alloy (AA) reinforcement are Electron Beam (EB) welded to it. The reinforcements have a section of 24*18 mm and are produced in continuous 2.55 km lengths. The alloy EN AW-6082 has been selected for the reinforcement due to its excellent extrudability, high strength in the precipitation hardened states, high toughness and strength at cryogenic temperature and good EB weldability. Each of the continuous lengths of the reinforcement is extruded billet on billet and press quenched on-line from the extrusion temperature in an industrial extrusion plant. In order to insure the ready EB weldability of the reinforcement onto the pure aluminum of the insert, tight dimensional tolerances and proper surface finish of the reinforcement are required in the as-extruded state. As well, in order to facilitate the winding operation of the conductor, the uniformity of the mechanical properties of the extruded reinforcement, especially at the billet on billet joints, is critical. To achieve these requirements in an industrial environment, substantial effort was made to refine existing production techniques and to monitor critical extrusion parameters during production. This paper summarizes the main results obtained during the establishment of the extrusion line and of the production phase of the reinforcement. (10 refs)

Triptycene-Roofed Quinoxaline Cavitands for the Supramolecular Detection of BTEX in Air

Two novel triptycene quinoxaline cavitands (DiTriptyQxCav and MonoTriptyQxCav) have been designed, synthesized, and applied in the supramolecular detection of benzene, toluene, ethylbenzene, and xylenes (BTEX) in air. The complexation properties of the two cavitands towards aromatics in the solid state are strengthened by the presence of the triptycene moieties at the upper rim of the tetraquinoxaline walls, promoting the confinement of the aromatic hydrocarbons within the cavity. The two cavitands were used as fiber coatings for solid-phase microextraction (SPME) BTEX monitoring in air. The best performances in terms of enrichment factors, selectivity, and LOD (limit of detection) values were obtained by using the DiTriptyQxCav coating. The corresponding SPME fiber was successfully tested under real urban monitoring conditions, outperforming the commercial divinylbenzene\u2013Carboxen\u2013polydimethylsiloxane (DVB\u2013CAR\u2013PDMS) fiber in BTEX adsorption