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

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

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

Signal based fault detection and diagnosis for rotating electrical machines: Issues and solutions

Complex systems are found in almost all field of contemporary science and are associated with a wide variety of financial, physical, biological, information and social systems. Complex systems modelling could be addressed by signal based procedures, which are able to learn the complex system dynamics from data provided by sensors, which are installed on the system in order to monitor its physical variables. In this chapter the aim of diagnosis is to detect if the electrical machine is healthy or a change is occurring due to abnormal events and, in addition, the probable causes of the abnormal events. Diagnosis is addressed by developing machine learning procedures in order to classify the probable causes of deviations from system normal events. This chapter presents two Fault Detection and Diagnosis solutions for rotating electrical machines by signal based approaches. The first one uses a current signature analysis technique based on Kernel Density Estimation and Kullback-Liebler divergence. The second one presents a vibration signature analysis technique based on Multi-Scale Principal Component Analysis. Several simulations and experimentations on real electric motors are carried out in order to verify the effectiveness of the proposed solutions. The results show that the proposed signal based diagnosis procedures are able to detect and diagnose different electric motor faults and defects, improving the reliability of electrical machines. Fault Detection and Diagnosis algorithms could be used not only with the fault diagnosis purpose but also in a Quality Control scenario. In fact, they can be integrated in test benches at the end or in the middle of the production line in order to test the machines quality. When the electric motors reach the test benches, the sensors acquire measurements and the Fault Detection and Diagnosis procedures detect if the motor is healthy or faulty, in this last case further inspections can diagnose the fault. © Springer International Publishing Switzerland 2015