Preliminary evaluation of SensHand V1 in assessing motor skills performance in Parkinson Disease

Nowadays, the increasing old population 65+ as well as the pace imposed by work activities lead to a high number of people that have particular injuries for limbs. In addition to persistent or temporary disabilities related to accidental injuries we must take into account that part of the population suffers from motor deficits of the hands due to stroke or diseases of various clinical nature. The most recurrent technological solutions to measure the rehabilitation or skill motor performance of the hand are glove-based devices, able to faithfully capture the movements of the hand and fingers. This paper presents a system for hand motion analysis based on 9-axis complete inertial modules and dedicated microcontroller which are fixed on fingers and forearm. The technological solution presented is able to track the patients' hand motions in real-time and then to send data through wireless communication reducing the clutter and the disadvantages of a glove equipped with sensors through a different technological structure. The device proposed has been tested in the study of Parkinson's disease

Combining olfactory test and motion analysis sensors in Parkinson's disease preclinical diagnosis: A pilot study

Objectives: Preclinical diagnosis of Parkinson's disease (PD) is nowadays a topic of interest as the neuropathological process could begin years before the appearance of motor symptoms. Several symptoms, among them hyposmia, could precede motor features in PD. In the preclinical phase of PD, a subclinical reduction in motor skills is highly likely. In this pilot study, we investigate a step-by-step method to achieve preclinical PD diagnosis. Material and methods: We used the IOIT (Italian Olfactory Identification Test) to screen a population of healthy subjects. We identified 20 subjects with idiopathic hyposmia. Hyposmic subjects underwent an evaluation of motor skills, at baseline and after 1 year, using motion analysis sensors previously created by us. Results: One subject showed significant worsening in motor measurements. In this subject, we further conducted a dopaminergic challenge test monitored with the same sensors and, finally, he underwent [123I]-FP/CIT (DaTscan) SPECT brain imaging. The results show that he is probably affected by preclinical PD. Conclusions: Our pilot study suggests that the combined use of an olfactory test and motor sensors for motion analysis could be useful for a screening of healthy subjects to identify those at a high risk of developing PD

Biomechanical parameter assessment for classification of Parkinson's disease on clinical scale

The primary goal of this study was to investigate computerized assessment methods to classify motor dysfunctioning of patients with Parkinsonâ\u80\u99s disease on the clinical scale. In this proposed system, machine learningâ\u80\u93based computerized assessment methods were introduced to assess the motor performance of patients with Parkinsonâ\u80\u99s disease. Biomechanical parameters were acquired from six exercises through wearable inertial sensors: SensFoot V2 and SensHand V1. All patients were evaluated via neurologist by means of the clinical scale. The average rating was calculated from all exercise ratings given by clinicians to estimate overall rating for each patient. Patients were divided in two groups: slightâ\u80\u93mild patients with Parkinsonâ\u80\u99s disease and moderateâ\u80\u93severe patients with Parkinsonâ\u80\u99s disease according to average rating (â\u80\u9c0: slight and mildâ\u80\u9d and â\u80\u9c1: moderate and severeâ\u80\u9d). Feature selection methods were used for the selection of significant features. Selected features were trained in support vector machine, logistic regression, and neural network to classify the two groups of patients. The highest classification accuracy obtained by support vector machine classifier was 79.66%, with 0.8790 area under the curve. A 76.2% classification accuracy was obtained with 0.7832 area under the curve through logistic regression. A 83.10% classification accuracy was obtained by neural network classifier, with 0.889 area under the curve. Strong distinguishability of the models between the two groups directs the high possibility of motor impairment classification through biomechanical parameters in patients with Parkinsonâ\u80\u99s disease based on the clinical scale

Preliminary design issues for inertial rings in Ambient Assisted Living applications

A wearable 9dof inertial system able to measure hand posture and movement is presented. The design issues for the deployment of measurement instrumentation based on no-invasive ring-shaped inertial units and of a wireless sensor network by them composed are described. Compromises between the physical and functional proprieties of a wearable device and the requirements for the hardware development are discussed with attention to an handsome design concept aesthetically effective. Techniques of power saving based on an optimized firmware programming are mentioned to realize a performing battery powered system featured by an exhaustive operation time. The printed circuit board (PCB) design rules, the choice of the components and materials, the fusion of inertial data with optical sensors outcomes are also discussed. Previous experience in the field of wearable systems are mentioned in the presentation of the results that emphasize the functional and application potential of a 9dof inertial system integrated in a ring-shaped device. � 2015 IEEE

The VISTA datasets, a combination of inertial sensors and depth cameras data for activity recognition

This paper makes the VISTA database, composed of inertial and visual data, publicly available for gesture and activity recognition. The inertial data were acquired with the SensHand, which can capture the movement of wrist, thumb, index and middle fingers, while the RGB-D visual data were acquired simultaneously from two different points of view, front and side. The VISTA database was acquired in two experimental phases: in the former, the participants have been asked to perform 10 different actions; in the latter, they had to execute five scenes of daily living, which corresponded to a combination of the actions of the selected actions. In both phase, Pepper interacted with participants. The two camera point of views mimic the different point of view of pepper. Overall, the dataset includes 7682 action instances for the training phase and 3361 action instances for the testing phase. It can be seen as a framework for future studies on artificial intelligence techniques for activity recognition, including inertial-only data, visual-only data, or a sensor fusion approach

Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review

IntroductionVarious neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer.MethodsHere, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement.ResultsA majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon.DiscussionWe conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation

Automated Tracking of Hand Hygiene Stages

The European Centre for Disease Prevention and Control (ECDC) estimates that 2.5 millioncases of Hospital Acquired Infections (HAIs) occur each year in the European Union. Handhygiene is regarded as one of the most important preventive measures for HAIs. If it is implemented properly, hand hygiene can reduce the risk of cross-transmission of an infection in the healthcare environment. Good hand hygiene is not only important for healthcare settings. Therecent ongoing coronavirus pandemic has highlighted the importance of hand hygiene practices in our daily lives, with governments and health authorities around the world promoting goodhand hygiene practices. The WHO has published guidelines of hand hygiene stages to promotegood hand washing practices. A significant amount of existing research has focused on theproblem of tracking hands to enable hand gesture recognition. In this work, gesture trackingdevices and image processing are explored in the context of the hand washing environment.Hand washing videos of professional healthcare workers were carefully observed and analyzedin order to recognize hand features associated with hand hygiene stages that could be extractedautomatically. Selected hand features such as palm shape (flat or curved); palm orientation(palms facing or not); hand trajectory (linear or circular movement) were then extracted andtracked with the help of a 3D gesture tracking device - the Leap Motion Controller. These fea-tures were further coupled together to detect the execution of a required WHO - hand hygienestage,Rub hands palm to palm, with the help of the Leap sensor in real time. In certain conditions, the Leap Motion Controller enables a clear distinction to be made between the left andright hands. However, whenever the two hands came into contact with each other, sensor data from the Leap, such as palm position and palm orientation was lost for one of the two hands.Hand occlusion was found to be a major drawback with the application of the device to this usecase. Therefore, RGB digital cameras were selected for further processing and tracking of the hands. An image processing technique, using a skin detection algorithm, was applied to extractinstantaneous hand positions for further processing, to enable various hand hygiene poses to be detected. Contour and centroid detection algorithms were further applied to track the handtrajectory in hand hygiene video recordings. In addition, feature detection algorithms wereapplied to a hand hygiene pose to extract the useful hand features. The video recordings did not suffer from occlusion as is the case for the Leap sensor, but the segmentation of one handfrom another was identified as a major challenge with images because the contour detectionresulted in a continuous mass when the two hands were in contact. For future work, the datafrom gesture trackers, such as the Leap Motion Controller and cameras (with image processing)could be combined to make a robust hand hygiene gesture classification system