A wearable system to objectify assessment of motor tasks for supporting parkinson’s disease diagnosis

Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring

Using wearable sensor systems for objective assessment of parkinson's disease

This paper presents a novel wearable sensor system based on the integration of miniaturised IMUs for fine hand movement analysis. The system, named SensHand V1, is composed of full 9-axis inertial sensors, placed on the fingers and wrist, which are managed by a cortex-M3 microcontroller. The acquired data are sent to a data logger through the use of Bluetooth communication. In this paper, the system is used for the objective diagnosis of Parkinson's disease, which is commonly assessed by neurologists through visual examination of motor tasks and semi-quantitative rating scales. Here, these motor tasks are also assessed using the SensHand V1, and then compared with the subjective metrics. Results demonstrate that the system is adequate to support neurologists in diagnostic procedures and allows for an objective evaluation of the disease

Empowering patients in self-management of parkinson's disease through cooperative ICT systems

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved

Epidemiological surveillance of human enteric viruses by monitoring of different environmental matrices.

In the aim of studying possible relations between viruses detected in clinical specimens and the ones found in different environmental matrices, in the period May 2004 to April 2005, the collection of faecal samples from gastroenteritis cases and the monthly monitoring of raw and treated wastewater, river water, seawater and mussels were carried out. The viruses considered for environmental monitoring were adenovirus, rotavirus, enterovirus, norovirus, hepatitis A virus (HAV) and Torque teno virus (TTV): they were searched for with PCR and RT-PCR and confirmed by gene sequencing. Faecal coliforms and somatic coliphages' counts were also determined. The surveillance of case detected 45 positive faecal samples out of 255 (17.6%) while 35 of 56 environmental samples (62.5%) resulted positive for at least one of the considered viruses. The detection of the same viral strain in the faeces of gastroenteritis cases and in water was possible for adenovirus and rotavirus, which were also predominant in environmental matrices; thus they could be considered as a reference for risk assessment

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

Cyclooxygenase-2 inhibitors. 1,5-diarylpyrrol-3-acetic esters with enhanced inhibitory activity toward cyclooxygenase-2 and improved cyclooxygenase-2/cyclooxygenase-1 selectivity.

he important role of cyclooxygenase-2 (COX-2) in the pathogenesis of inflammation and side effect limitations of current COX-2 inhibitor drugs illustrates a need for the design of new compounds based on alternative structural templates. We previously reported a set of substituted 1,5-diarylpyrrole derivatives, along with their inhibitory activity toward COX enzymes. Several compounds proved to be highly selective COX-2 inhibitors and their affinity data were rationalized through docking simulations. In this paper, we describe the synthesis of new 1,5-diarylpyrrole derivatives that were assayed for their in vitro inhibitory effects toward COX isozymes. Among them, the ethyl-2-methyl-5-[4-(methylsulfonyl)phenyl]-1-[3-fluorophenyl]-1H-pyrrol-3- acetate (1d), which was the most potent and COX-2 selective compound, also showed a very interesting in vivo anti-inflammatory and analgesic activity, laying the foundations for developing new lead compounds that could be effective agents in the armamentarium for the management of inflammation and pain

Objective and automatic classification of Parkinson disease with Leap Motion controller

Background: The main objective of this paper is to develop and test the ability of the Leap Motion controller (LMC) to assess the motor dysfunction in patients with Parkinson disease (PwPD) based on the MDS-UPDRSIII exercises. Four exercises (thumb forefinger tapping, hand opening/closing, pronation/supination, postural tremor) were used to evaluate the characteristics described in MDS-UPDRSIII. Clinical ratings according to the MDS/UPDRS-section III items were used as target. For that purpose, 16 participants with PD and 12 healthy people were recruited in Ospedale Cisanello, Pisa, Italy. The participants performed standardized hand movements with camera-based marker. Time and frequency domain features related to velocity, angle, amplitude, and frequency were derived from the LMC data. Results: Different machine learning techniques were used to classify the PD and healthy subjects by comparing the subjective scale given by neurologists against the predicted diagnosis from the machine learning classifiers. Feature selection methods were used to choose the most significant features. Logistic regression (LR), naive Bayes (NB), and support vector machine (SVM) were trained with tenfold cross validation with selected features. The maximum obtained classification accuracy with LR was 70.37%; the average area under the ROC curve (AUC) was 0.831. The obtained classification accuracy with NB was 81.4%, with AUC of 0.811. The obtained classification accuracy with SVM was 74.07%, with AUC of 0.675. Conclusions: Results revealed that the system did not return clinically meaningful data for measuring postural tremor in PwPD. In addition, it showed limited potential to measure the forearm pronation/supination. In contrast, for finger tapping and hand opening/closing, the derived parameters showed statistical and clinical significance. Future studies should continue to validate the LMC as updated versions of the software are developed. The obtained results support the fact that most of the set of selected features contributed significantly to classify the PwPD and healthy subjects

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