The KIMORE dataset: KInematic assessment of MOvement and clinical scores for remote monitoring of physical REhabilitation

The paper proposes a free dataset, available at the following link1, named KIMORE, regarding different rehabilitation exercises collected by a RGB-D sensor. Three data inputs including RGB, Depth videos and skeleton joint positions were recorded during five physical exercises, specific for low back pain and accurately selected by physicians. For each exercise, the dataset also provides a set of features, specifically defined by the physicians, and relevant to describe its scope. These features, validated with respect to a stereophotogrammetric system, can be analyzed to compute a score for the subject's performance. The dataset also contains an evaluation of the same performance provided by the clinicians, through a clinical questionnaire. The impact of KIMORE has been analyzed by comparing the output obtained by an example of rule and template-based approaches and the clinical score. The dataset presented is intended to be used as a benchmark for human movement assessment in a rehabilitation scenario in order to test the effectiveness and the reliability of different computational approaches. Unlike other existing datasets, the KIMORE merges a large heterogeneous population of 78 subjects, divided into 2 groups with 44 healthy subjects and 34 with motor dysfunctions. It provides the most clinically-relevant features and the clinical score for each exercise

An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept

none8This work proposes a real-time monitoring tool aimed to support clinicians for remote assessing exercise performances during home-based rehabilitation. The study relies on clinician indications to define kinematic features, that describe five motor tasks (i.e., the lateral tilt of the trunk, lifting of the arms, trunk rotation, pelvis rotation, squatting) usually adopted in the rehabilitation program for axial disorders. These features are extracted by the Kinect v2 skeleton tracking system and elaborated to return disaggregated scores, representing a measure of subjects performance. A bell-shaped function is used to rank the patient performances and to provide the scores. The proposed rehabilitation tool has been tested on 28 healthy subjects and on 29 patients suffering from different neurological and orthopedic diseases. The reliability of the study has been performed through a cross-sectional controlled design methodology, comparing algorithm scores with respect to blinded judgment provided by clinicians through filling a specific questionnaire. The use of task-specific features and the comparison between the clinical evaluation and the score provided by the instrumental approach constitute the novelty of the study. The proposed methodology is reliable for measuring subject's performance and able to discriminate between the pathological and healthy condition.Capecci, Marianna; Ceravolo, Maria Gabriella; Ferracuti, Francesco; Grugnetti, Martina; Iarlori, Sabrina; Longhi, Sauro; Romeo, Luca; Verdini, FedericaCapecci, Marianna; Ceravolo, Maria Gabriella; Ferracuti, Francesco; Grugnetti, Martina; Iarlori, Sabrina; Longhi, Sauro; Romeo, Luca; Verdini, Federic

Black box modelling of a latent heat thermal energy storage system coupled with heat pipes

This paper presents black box models to represent a LHTESS (Latent Heat Thermal Energy Storage System) coupled with heat pipes, aimed at increasing the storage performance and at decreasing the time of charging/discharging. The presented storage system is part of a micro solar CHP plant and the developed model is intended to be used in the simulation tool of the overall system, thus it has to be accurate but also fast computing. Black box data driven models are considered, trained by means of numerical data obtained from a white box detailed model of the LHTESS and heat pipes system. A year round simulation of the system during its normal operation within the micro solar CHP plant is used as dataset. Then the black box models are trained and finally validated on these data. Results show the need for a black box model that can take into account the different seasonal performance of the LHTESS. In this analysis the best fit was achieved by means of Random Forest models with an accuracy higher than 90%.This study is a part of the Innova MicroSolar Project, funded in the framework of the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 723596). Prof. Cabeza would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREA is certified agent TECNIO in the category of technology developers from the Government of Catalonia. Dr. Alvaro de Gracia has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 712949

Parietal resting-state EEG alpha source connectivity is associated with subcortical white matter lesions in HIV-positive people

Objective Parietal resting-state electroencephalographic (rsEEG) alpha (8–10 Hz) source connectivity is abnormal in HIV-positive persons. Here we tested whether this abnormality may be associated with subcortical white matter vascular lesions in the cerebral hemispheres. Methods Clinical, rsEEG, and magnetic resonance imaging (MRI) datasets in 38 HIV-positive persons and clinical and rsEEG datasets in 13 healthy controls were analyzed. Radiologists visually evaluated the subcortical white matter hyperintensities from T2-weighted FLAIR MRIs (i.e., Fazekas scale). In parallel, neurophysiologists estimated the eLORETA rsEEG source lagged linear connectivity from parietal cortical regions of interest. Results Compared to the HIV participants with no/negligible subcortical white matter hyperintensities, the HIV participants with mild/moderate subcortical white matter hyperintensities showed lower parietal interhemispheric rsEEG alpha lagged linear connectivity. This effect was also observed in HIV-positive persons with unimpaired cognition. This rsEEG marker allowed good discrimination (area under the receiver operating characteristic curve > 0.80) between the HIV-positive individuals with different amounts of subcortical white matter hyperintensities. Conclusions The parietal rsEEG alpha source connectivity is associated with subcortical white matter vascular lesions in HIV-positive persons, even without neurocognitive disorders. Significance Those MRI-rsEEG markers may be used to screen HIV-positive persons at risk of neurocognitive disorders

Modeling and Diagnosis of Complex Systems Dynamics by Data-Driven Approaches

I sistemi complessi possono essere rinvenuti in quasi tutti i campi della scienza contemporanea, e possono avere diversa natura: finanziaria, fisica, biologica, informativa, sociale, ecc. I sistemi complessi consistono di un gran numero di componenti che interagiscono non linearmente tra loro e che mostrano un comportamento collettivo non derivante semplicemente dal comportamento delle parti individuali. Sebbene tali sistemi godano di numerose proprietà, le più importanti sono: dimensionalità, incertezza, non linearità e accoppiamento tra i componenti. Le procedure per ottenere modelli analitici di determinati sistemi sono solitamente classificate in modellazione fisica e identificazione. Queste procedure possono essere difficilmente implementabili se applicate a sistemi complessi perché le loro caratteristiche peculiari rendono difficile la modellazione. Poiché un modello matematico è una descrizione del comportamento di un sistema, una modellazione accurata per i sistemi complessi è molto difficile da ottenere in pratica. Per di più, a volte, potrebbe addirittura essere impossibile descrivere il sistema attraverso equazioni analitiche. Alla luce di quanto emerso, la presente trattazione si propone di affrontare due problemi riguardanti la modellazione e la diagnosi dei sistemi complessi: il primo riguarda specificamente la modellazione di un sistema complesso, nel caso in cui il modello analitico non sia ottenibile; il secondo si riferisce alla diagnosi del comportamento del sistema. Quest’ultima attività dovrebbe rilevare se il sistema complesso è normale o se sta avvenendo un cambiamento dovuto a eventi anomali, nonché le cause probabili di tali eventi. La modellazione dei sistemi complessi viene affrontata sviluppando metodi data-driven, che sono capaci di apprendere le dinamiche del sistema complesso direttamente dai dati forniti da sensori installati sul sistema, al fine di monitorarne le variabili fisiche. La diagnosi dei sistemi complessi viene invece affrontata sviluppando metodi di apprendimento automatico in modo da classificare le probabili cause di scostamento da eventi normali del sistema. Nella trattazione ampia attenzione è posta al problema di modellazione e diagnosi di sistemi complessi con riferimento a sistemi reali, presentando diverse applicazioni pratiche e casi di studio. Il primo caso di studio riguarda la modellazione e diagnosi di difetti e guasti di motori elettrici in uno scenario di controllo qualità mentre il secondo si riferisce ad un sistema complesso indusxiii triale, quale quello di una cartiera. Nel terzo caso viene affrontata la questione di stimare la vita utile rimasta di un motore turbofan e l’ultimo tratta il problema di modellare segnali elettroencefalografici attraverso algoritmi basati sui dati. Dato che il problema di modellazione e diagnosi è affrontato attraverso procedure basate sui dati, gli algoritmi sviluppati possono essere applicati ad un’ampia classe di macchine elettriche rotanti e sistemi complessi industriali, e non solo a quelli riportati

An Integrated Simulation Module for Cyber-Physical Automation Systems

The integration of Wireless Sensors Networks (WSNs) into Cyber Physical Systems (CPSs) is an important research problem to solve in order to increase the performances, safety, reliability and usability of wireless automation systems. Due to the complexity of real CPSs, emulators and simulators are often used to replace the real control devices and physical connections during the development stage. The most widespread simulators are free, open source, expandable, flexible and fully integrated into mathematical modeling tools; however, the connection at a physical level and the direct interaction with the real process via the WSN are only marginally tackled; moreover, the simulated wireless sensor motes are not able to generate the analogue output typically required for control purposes. A new simulation module for the control of a wireless cyber-physical system is proposed in this paper. The module integrates the COntiki OS JAva Simulator (COOJA), a cross-level wireless sensor network simulator, and the LabVIEW system design software from National Instruments. The proposed software module has been called “GILOO” (Graphical Integration of Labview and cOOja). It allows one to develop and to debug control strategies over the WSN both using virtual or real hardware modules, such as the National Instruments Real-Time Module platform, the CompactRio, the Supervisory Control And Data Acquisition (SCADA), etc. To test the proposed solution, we decided to integrate it with one of the most popular simulators, i.e., the Contiki OS, and wireless motes, i.e., the Sky mote. As a further contribution, the Contiki Sky DAC driver and a new “Advanced Sky GUI” have been proposed and tested in the COOJA Simulator in order to provide the possibility to develop control over the WSN. To test the performances of the proposed GILOO software module, several experimental tests have been made, and interesting preliminary results are reported. The GILOO module has been applied to a smart home mock-up where a networked control has been developed for the LED lighting system

Fixed-size LS-SVM LPV System Identification for Large Datasets

In this paper, we propose an efficient method for handling large datasets in linear parameter-varying (LPV) model identification. The method is based on least-squares support vector machine (LS-SVM) identification in the primal space. To make the identification computationally feasible, even for very large datasets, we propose estimating a finite-dimensional feature map. To achieve this, we propose a two-step method to reduce the computational effort. First, we define the training set as a fixed-size subsample of the entire dataset, considering collision entropy for subset selection. The second step involves approximating the feature map through the eigenvalue decomposition of the kernel matrices. This paper considers both autoregressive with exogenous input (ARX) and state-space (SS) model forms. By comparing the problem formulation in the primal and dual spaces in terms of accuracy and computational complexity, the main advantage of the proposed technique is the reduction in space and time complexity during the training stage, making it preferable for handling very large datasets. To validate our proposed primal approach, we apply it to estimate LPV models using provided inputs, outputs, and scheduling signals for two nonlinear benchmarks: the parallel Wiener-Hammerstein system and the Silverbox system. The performances of our proposed approach are compared with the dual LS-SVM approach and the kernel principal component regression

RGBD camera monitoring system for Alzheimer’s disease assessment using Recurrent Neural Networks with Parametric Bias action recognition

The present paper proposes a computer vision system to diagnose the stage of illness in patients a ected by Alzheimer's disease. In the context of Ambient Assisted Living (AAL), the system monitors people in home environment during daily personal care activities. The aim is to evaluate the dementia stage, observing actions listed in the Direct Assessment of Funcional Status (DAFS) index and detecting anomalies during the performance, in order to assign a score explaining if the action is correct or not. In this work brushing teeth and grooming hair by a hairbrush are analysed. The technology consists of the application of a Recurrent Neural Network with Parametric Bias (RNNPB) that is able to learn movements connected with a speci c action and recognize human activities by parametric bias that work like mirror neurons. This study has been conducted using Microsoft Kinect to collect data about the actions observed and oversee the user tracking and gesture recognition. Experiments prove that the proposed computer vision system can learn and recognize complex human activities and evaluates DAFS score

Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines

Condition-based monitoring of rotating machines requires robust features for accurate fault diagnosis, which is indeed directly linked to the quality of the features extracted from the signals. This is especially true for vibration data, whose quasi-stationary nature implies that the quality of frequency domain extracted features depends on the Signal-to-Noise Ratio (SNR) condition, operating condition variations and data segmentation. This paper presents a novel Statistical Spectral Analysis, which leads to highly robust fault diagnosis with poor SNR conditions, different time-window segmentation and different operating conditions. The amplitudes of spectral contents of the quasi-stationary time vibration signals are sorted and transformed into statistical spectral images. The sort operation leads to the knowledge of the Empirical Cumulative Distribution Function (ECDF) of the amplitudes of each frequency band. The ECDF provides a robust statistical information of the distribution of the amplitude under different SNR and operating conditions. Statistical metrics have been adopted for fault classification, by using the ECDFs obtained from the spectral images as fault features. By applying simple statistical metrics, it is possible to achieve fault diagnosis without classifier training, saving both time and computational costs. The proposed algorithm has been tested using a vibration data benchmark: comparison with state-of-the-art fault diagnosis algorithms shows promising results