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

Diagnosing Mathematics Ability of Technology Students: Misconceptions in Algebra

Catering to a long-standing need in the country, the technology stream was introduced to the G.C.E. (A/L) in Sri Lanka in 2015 with one compulsory subject Science for Technology formed by combining six Science subjects including Mathematics. There is no argument that a sound Mathematics background is essential to produce a good technology graduate. Not only do technologists need Mathematics knowledge in technological applications, but also the logical, analytical, and critical thinking developed through the learning of mathematics is essential for them in solving problems. Hence, technology faculties around the country observe that the command in mathematics of their new entrants needs improvement. As a diagnosis and to uplift their mathematics achievement, this study aims to explore one aspect of their mathematics knowledge: common mistakes and misconceptions. This paper reports on the extent to which algebraic mistakes are made by students entering Technology Faculties. The data for this study comes from a three-week online intensive mathematics course that students follow, prior to commencing their degree program. Students ask to respond to ten questions designed to capture errors in algebraic manipulations. The analysis of data shows a lack of understanding of the intricacies of division by zero consequently resulting in cancellation errors, erroneous manipulations of algebraic expressions, and improper use of parenthesis and priority of exponents in the order of operations. Another mistake is extending the distributive property of multiplication over addition erroneously to distributing multiplication over multiplication. More importantly, the data reveals a training these students have received in school that is mathematically less precise and therefore highlights the need to make students unlearn these erroneous habits that is ingrained in them for many years. Further, these results urge instructors to incorporate purposeful remedial actions into their early mathematics courses to better prepare them for their future technology.   Full paper submission of ICIET 202

Fixed-point MAP decoding of channel codes

This paper describes the fixed-point model of the maximum a posteriori (MAP) decoding algorithm of turbo and low-density parity-check (LDPC) codes, the most advanced channel codes adopted by modern communication systems for forward error correction (FEC). Fixed-point models of the decoding algorithms are developed in a unified framework based on the use of the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. This approach aims at bridging the gap toward the design of a universal, multistandard decoder of channel codes, capable of supporting the two classes of codes and having reduced requirements in terms of silicon area and power consumption and so suitable to mobile applications. The developed models allow the identification of key parameters such as dynamic range and number of bits, whose impact on the error correction performance of the algorithm is of pivotal importance for the definition of the architectural tradeoffs between complexity and performance. This is done by taking the turbo and LDPC codes of two recent communication standards such asWiMAX and 3GPP-LTE as a reference benchmark for a mobile scenario and by analyzing their performance over additive white Gaussian noise (AWGN) channel for different values of the fixed-point parameters

La disciplina del patto di famiglia e la sua compatibilita con l'impresa familiare

In un tessuto sociale ed economico come il nostro, caratterizzato per lo più da piccole e medie imprese, è emerso soprattutto negli ultimi anni come una buona successione imprenditoriale richieda una programmazione nel tempo, partendo cioè da “quando ancora non serve”, cosicchè la stessa non si prospetti come un fenomeno improvviso, ma sia invece correttamente gestito, in modo tale che l’ efficacia del passaggio generazionale perduri anche negli anni futuri. Il patto di famiglia, introdotto dalla l. 14 febbraio 2006, n° 55, rappresenta lo strumento in grado di anticipare l’insorgere dei conflitti disinnescandone le cause, un mezzo capace di spianare la strada alle soluzioni organizzative più consone all’impresa di volta in volta oggetto del trasferimento, e con il quale l’imprenditore può “uscire di scena” e lasciare il posto al suo successore senza che questo provochi dissidi o litigi all’interno della compagine familiare, e più specificatamente nell’esercizio dell’attività

Tubulin-VDAC Interaction: Molecular Basis for Mitochondrial Dysfunction in Chemotherapy-Induced Peripheral Neuropathy

Tubulin is a well-established target of microtubule-targeting agents (MTAs), a widely used class of chemotherapeutic drugs. Yet, aside from their powerful anti-cancer efficiency, MTAs induce a dose-limiting and debilitating peripheral neurotoxicity. Despite intensive efforts in the development of neuroprotective agents, there are currently no approved therapies to effectively manage chemotherapy-induced peripheral neuropathy (CIPN). Over the last decade, attempts to unravel the pathomechanisms underlying the development of CIPN led to the observation that mitochondrial dysfunctions stand as a common feature associated with axonal degeneration. Concomitantly, mitochondria emerged as crucial players in the anti-cancer efficiency of MTAs. The findings that free dimeric tubulin could be associated with mitochondrial membranes and interact directly with the voltage-dependent anion channels (VDACs) located in the mitochondrial outer membrane strongly suggested the existence of an interplay between both subcellular compartments. The biological relevance of the interaction between tubulin and VDAC came from subsequent in vitro studies, which found dimeric tubulin to be a potent modulator of VDAC and ultimately of mitochondrial membrane permeability to respiratory substrates. Therefore, one of the hypothetic mechanisms of CIPN implies that MTAs, by binding directly to the tubulin associated with VDAC, interferes with mitochondrial function in the peripheral nervous system. We review here the foundations of this hypothesis and discuss them in light of the current knowledge. A focus is set on the molecular mechanisms behind MTA interference with dimeric tubulin and VDAC interaction, the potential relevance of tubulin isotypes and availability as a free dimer in the specific context of MTA-induced CIPN. We further highlight the emerging interest for VDAC and its interacting partners as a promising therapeutic target in neurodegeneration

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

Upper limb motor pre-clinical assessment in Parkinson's disease using machine learning

Abstract Introduction Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. For example, idiopathic hyposmia (IH), which is a reduced olfactory sensitivity, is typical in >95% of PD patients and is a preclinical marker for the pathology. Methods In this work, a wearable inertial device, named SensHand V1, was used to acquire motion data from the upper limbs during the performance of six tasks selected by MDS-UPDRS III. Three groups of people were enrolled, including 30 healthy subjects, 30 IH people, and 30 PD patients. Forty-eight parameters per side were computed by spatiotemporal and frequency data analysis. A feature array was selected as the most significant to discriminate among the different classes both in two-group and three-group classification. Multiple analyses were performed comparing three supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes, on three different datasets. Results Excellent results were obtained for healthy vs. patients classification (F-Measure 0.95 for RF and 0.97 for SVM), and good results were achieved by including subjects with hyposmia as a separate group (0.79 accuracy, 0.80 precision with RF) within a three-group classification. Overall, RF classifiers were the best approach for this application. Conclusion The system is suitable to support an objective PD diagnosis. Further, combining motion analysis with a validated olfactory screening test, a two-step non-invasive, low-cost procedure can be defined to appropriately analyze people at risk for PD development, helping clinicians to identify also subtle changes in motor performance that characterize PD onset

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