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

Real Time Hand Movement Trajectory Tracking for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language

Real time hand movement trajectory tracking based on machine learning approaches may assist the early identification of dementia in ageing Deaf individuals who are users of British Sign Language (BSL), since there are few clinicians with appropriate communication skills, and a shortage of sign language interpreters. Unlike other computer vision systems used in dementia stage assessment such as RGB-D video with the aid of depth camera, activities of daily living (ADL) monitored by information and communication technologies (ICT) facilities, or X-Ray, computed tomography (CT), and magnetic resonance imaging (MRI) images fed to machine learning algorithms, the system developed here focuses on analysing the sign language space envelope(sign trajectories/depth/speed) and facial expression of deaf individuals, using normal 2D videos. In this work, we are interested in providing a more accurate segmentation of objects of interest in relation to the background, so that accurate real-time hand trajectories (path of the trajectory and speed) can be achieved. The paper presents and evaluates two types of hand movement trajectory models. In the first model, the hand sign trajectory is tracked by implementing skin colour segmentation. In the second model, the hand sign trajectory is tracked using Part Afinity Fields based on the OpenPose Skeleton Model [1, 2]. Comparisons of results between the two different models demonstrate that the second model provides enhanced improvements in terms of tracking accuracy and robustness of tracking. The pattern differences in facial and trajectory motion data achieved from the presented models will be beneficial not only for screening of deaf individuals for dementia, but also for assessment of other acquired neurological impairments associated with motor changes, for example, stroke and Parkinsons disease

Monitoring and analysis of movement in subjects with cognitive and motor diseases by Machine Learning methods

Questo studio propone metodologie di Machine Learning per valutare indici clinici utili al monitoraggio di malattie che comportano disturbi cognitivi e motori. Tali indici, che derivano dall’analisi del movimento umano, permettono di supportare sia i pazienti che il personale medico nei periodi di riabilitazione a casa. Il lavoro è stato condotto nel contesto di una crescente necessità di sistemi di tele-assistenza, dovuta al progressivo invecchiamento della popolazione e alla richiesta di riduzione dei costi di assistenza. Il sistema di monitoraggio si avvale del sensore Microsoft Kinect, che consente di tracciare le coordinate di posizione ed orientazione dei giunti dello skeleton, mappato sul corpo del soggetto in esame. L’analisi di queste informazioni è stata possibile attraverso lo sviluppo di tecniche di Machine Learning. Questo sistema a basso costo permette all’equipe medica di monitorare da remoto il paziente a casa, evitando problemi relativi ad un eventuale trasferimento in clinica o all’influenza di un valutatore esterno durante lo svolgimento del test. Lo studio e la sperimentazione sono stati supportati dalla collaborazione di medici e fisioterapisti della clinica di Neuro-riabilitazione dell’ospedale di Torrette di Ancona. La valutazione degli esercizi è stata ottenuta attribuendo un punteggio quantitativo a ciascun fattore di controllo, individuato sulla base delle specifiche cliniche definite dagli specialisti. Sono stati inoltre inclusi un feedback visivo o uditivo per correggere eventuali posture errate dei pazienti e un report per i fisioterapisti riguardo lo svolgimento degli esercizi proposti. Inoltre, lo studio analizza come sia possibile ottenere la stessa valutazione con diverse tecniche di Machine Learning. I risultati dimostrano come le metodologie proposte consentono di ottenere una valutazione, ben correlata con quella elaborata dai clinici, della bontà dello svolgimento degli esercizi riabilitativi e cognitivi.This study proposes Machine Learning methodologies to evaluate clinical indexes available for the monitoring of cognitive and motor diseases. These indexes, that are correlated to human motion analysis, allow to support both patients and medical staff during the rehabilitation period at home. This study has been conducted in the context of a future need of tele-assistance systems, due to the growing aging of the population and the requirement of reducing costs. The monitoring system is equipped with Microsoft Kinect sensor, allowing to track position and orientation coordinates of skeleton joints mapped on the subject’s body. The analysis of this information has been possible by Machine Learning techniques. This instrument allows medical staff to monitor remotely patients at home, avoiding problems related to the transport in a clinical center or the influence of a supervisor during the test performance. Exercises evaluation has been obtained assigning a quantitative score to each control factor identified on the base of clinical specifications defined by clinicians. The study and exercises acquisitions have been conducted with the collaboration of doctors and physiotherapists of Neuro-rehabilitation clinic of Torrette hospital in Ancona. A visual or audio feedback to correct wrong postures of patients and a report for physiotherapists about exercise performances are included. Moreover, this study analyses how is possible to obtain an assessment of the correctness of daily living activities for cognitive impairments and of rehabilitation exercises performance well correlated with those given by clinicians

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

Electric motor defects diagnosis based on kernel density estimation and Kullback-Leibler divergence in quality control scenario

The present paper deals with the defect detection and diagnosis of induction motor, based on motor current signature analysis in a quality control scenario. In order to develop a monitoring system and improve the reliability of induction motors, Clarke-Concordia transformation and kernel density estimation are employed to estimate the probability density function of data related to healthy and faulty motors. Kullback-Leibler divergence identifies the dissimilarity between two probability distributions and it is used as an index for the automatic defects identification. Kernel density estimation is improved by fast Gaussian transform. Since these techniques achieve a remarkable computational cost reduction respect the standard kernel density estimation, the developed monitoring procedure became applicable on line, as a Quality Control method for the end of production line test. Several simulations and experimentations are carried out in order to verify the proposed methodology effectiveness: broken rotor bars and connectors are simulated, while experimentations are carried out on real motors at the end of production line. Results show that the proposed data-driven diagnosis procedure is able to detect and diagnose different induction motor faults and defects, improving the reliability of induction machines in quality control scenario. © 2015 Elsevier Ltd

A novel computer vision based e-rehabilitation system: From gaming to therapy support

We propose a novel e-rehabilitation system based on a commercial RGB-D device. Differently from exergaming approaches, clinical objectives scores of each specific body part involved in the exercise are computed. Subjects performances are sent to the physiotherapists in order to support and improve decisions and therapies. © 2016 IEEE

Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities

Electric motor fault detection and diagnosis by kernel density estimation and kullback-Leibler divergence based on stator current measurements

This paper deals with the problem of fault detection and diagnosis of induction motor based on motor current signature analysis. Principal component analysis is used to reduce the three-phase current space to a 2-D space. Kernel density estimation (KDE) is adopted to evaluate the probability density functions of each healthy and faulty motor, which can be used as features in order to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two probability distributions, and it allows automatic fault identification. The aim is also to improve computational performance in order to apply online a monitoring system. KDE is improved by fast Gaussian transform and a points reduction procedure. Since these techniques achieve a remarkable computational cost reduction with respect to the standard KDE, the algorithm can be used online. Experiments are carried out using two alternate current motors: an asynchronous induction machine and a single-phase motor. The faults considered to test the developed algorithm are cracked rotor, out-of-tolerance geometry rotor, and backlash. Tests are carried out at different load and voltage levels to show the proposed method performance

An inertial and QR code landmarks-based navigation system for impaired wheelchair users

Personal mobility is a key factor in independent living for elderly people and people with motor disabilities, thus indoor navigation systems are of utmost concern in Ambient Assisted Living (AAL) applications. Driving an electric powered wheelchair in domestic environments becomes difficult for people with arms or hands impairments. Moreover, people affected by tetraplegia are completely unable to operate a joystick, and must rely on input interfaces, such as eye tracking and “sip and puff”, which require tedious and repetitive tasks to be operated. Smart powered wheelchairs with autonomous navigation intelligence and their integration within AAL homes, may enhance independence and improve both the security and the perceived quality of life. Self-navigating systems combine different measurements provided by both absolute and relative sensors to improve localization accuracy. In this work, a low-cost localization system for autonomous wheelchairs, which takes advantage of Quick Response (QR) code landmarks information, is proposed. QR code is a low-cost pattern with fast readability and large storage capacity with respect to other landmarks solutions. The proposed wheelchair is equipped with an Inertial Measurement Unit (IMU) and a video camera: the inertial information, provided by the IMU, is fused with that provided by QR code recognition, thus reducing the error propagation caused by a Dead Reckoning (DR) approach. Autonomy and intelligence of the wheelchair is drastically increased by integrating within its navigation system both the knowledge about self localization and the environment (e.g. room identification). QR code landmarks are a suitable solution to store this information. This approach has been implemented and experimentally tested in an indoor scenario, demonstrating its feasibility and its good and reliable long-term performances