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Biomechanics And Motor Control Of Human Movement

Objective
To present the International Parkinson and Movement Disorder Society (MDS) Clinical Diagnostic Criteria for Parkinson9s disease.
Background
Although several diagnostic criteria for Parkinson9s disease have been proposed, none have been officially adopted by an official Parkinson society. Moreover, the commonest-used criteria, the UK brain bank, were created more than 25 years ago. In recognition of the lack of standard criteria, the MDS initiated a task force to design new diagnostic criteria for clinical Parkinson9s disease.
Methods/Results
The MDS-PD Criteria are intended for use in clinical research, but may also be used to guide clinical diagnosis. The benchmark is expert clinical diagnosis; the criteria aim to systematize the diagnostic process, to make it reproducible across centers and applicable by clinicians with less expertise. Although motor abnormalities remain central, there is increasing recognition of non-motor manifestations; these are incorporated into both the current criteria and particularly into separate criteria for prodromal PD. Similar to previous criteria, the MDS-PD Criteria retain motor parkinsonism as the core disease feature, defined as bradykinesia plus rest tremor and/or rigidity. Explicit instructions for defining these cardinal features are included. After documentation of parkinsonism, determination of PD as the cause of parkinsonism relies upon three categories of diagnostic features; absolute exclusion criteria (which rule out PD), red flags (which must be counterbalanced by additional supportive criteria to allow diagnosis of PD), and supportive criteria (positive features that increase confidence of PD diagnosis). Two levels of certainty are delineated: Clinically-established PD (maximizing specificity at the expense of reduced sensitivity), and Probable PD (which balances sensitivity and specificity).
Conclusion
The MDS criteria retain elements proven valuable in previous criteria and omit aspects that are no longer justified, thereby encapsulating diagnosis according to current knowledge. As understanding of PD expands, criteria will need continuous revision to accommodate these advances. Disclosure: Dr. Postuma has received personal compensation for activities with Roche Diagnostics Corporation and Biotie Therapies. Dr. Berg has received research support from Michael J. Fox Foundation, the Bundesministerium fur Bildung und Forschung (BMBF), the German Parkinson Association and Novartis GmbH.

MDS Clinical Diagnostic Criteria for Parkinson's Disease (S19.001)

For the statistical consultant working with social science researchers the estimation of reliability and validity is a task frequently encountered. Measurement issues differ in the social sciences in that they are related to the quantification of abstract, intangible and unobservable constructs. In many instances, then, the meaning of quantities is only inferred. Let us begin by a general description of the paradigm that we are dealing with. Most concepts in the behavioral sciences have meaning within the context of the theory that they are a part of. Each concept, thus, has an operational definition which is governed by the overarching theory. If a concept is involved in the testing of hypothesis to support the theory it has to be measured. So the first decision that the research is faced with is \" how shall the concept be measured? \" That is the type of measure. At a very broad level the type of measure can be observational, self-report, interview, etc. These types ultimately take shape of a more specific form like observation of ongoing activity, observing video-taped events, self-report measures like questionnaires that can be open-ended or close-ended, Likert-type scales, interviews that are structured, semi-structured or unstructured and open-ended or close-ended. Needless to say, each type of measure has specific types of issues that need to be addressed to make the measurement meaningful, accurate, and efficient. Another important feature is the population for which the measure is intended. This decision is not entirely dependent on the theoretical paradigm but more to the immediate research question at hand. 6/14/2016 2 A third point that needs mentioning is the purpose of the scale or measure. What is it that the researcher wants to do with the measure? Is it developed for a specific study or is it developed with the anticipation of extensive use with similar populations? Once some of these decisions are made and a measure is developed, which is a careful and tedious process, the relevant questions to raise are \" how do we know that we are indeed measuring what we want to measure? \" since the construct that we are measuring is abstract, and \" can we be sure that if we repeated the measurement we will get the same result? \". The first question is related to validity and second to reliability. Validity and reliability are two important characteristics of behavioral measure and are referred to as …

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Validity and reliability.

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Basic Biomechanics of the Musculoskeletal System

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classiﬁcation and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redeﬁne representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM ﬁrstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

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Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.

Validation and application of a novel solution for environmental monitoring: A three months study at "Minerva Medica" archaeological site in Rome

Tag recognition: A new methodology for the structural monitoring of cultural heritage

The aim of this work is the evaluation of Distributed Classifier for the detection of gait phases that can be implemented in an active knee orthosis for the recovery of locomotion of pediatric subjects with neurological diseases, such as Cerebral Palsy (CP). The classifier is based on a Hierarchical Weighted Decision applied to the outputs of two or more scalar Hidden Markov Models (HMMs) trained by linear accelerations and angular velocities measured at shank and thigh. The kinematics of the dominant lower limb of ten healthy subjects were acquired by means of linear accelerometers and gyroscopes embedded in two inertial sensors. The actual sequence of gait phases was captured by means of foot switches. The experimental procedure consisted in one walking task, repeated for three times, on a treadmill at the preferred velocity of each subject. We compared the performance, in terms of sensitivity and specificity, of both Scalar Classifiers (SCs) and Distributed Classifiers (DCs) based on all the combinations of sagittal acceleration and sagittal angular velocity of the two body segments. The DC based on the angular velocities showed the highest values of sensitivity and specificity. The SC based on the angular velocity of shank was the better among others SCs, but the values of sensitivity and specificity are lower than 0.95. When we use only one sensor, placed on shank or thigh, the DC based on kinematic variables of shank showed better results, but not higher than 0.95. Consequently, the additional information provided by linear acceleration did not improve the performance and then, the gait-phase detection algorithm, which can be implemented in an active knee orthosis, has to be based on the output of two gyroscopes placed on shank and thigh.

Eduardo Palermo

Papers

Validation and application of a novel solution for environmental monitoring: A three months study at "Minerva Medica" archaeological site in Rome

Tag recognition: A new methodology for the structural monitoring of cultural heritage

A HMM distributed classifier to control robotic knee module of an active orthosis

Additive manufacturing structural redesign of hip prostheses for stress-shielding reduction and improved functionality and safety

Spatial memory training in a citizen science context