Thank you very much for downloading biomechanics and motor control of human movement. Maybe you have knowledge that, people have search hundreds times for their favorite books like this biomechanics and motor control of human movement, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious virus inside their laptop.

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 …

/pdf/validity-and-reliability-3ymfyx75ou.pdf

Validity and reliability.

You could look for impressive publication by the title of Basic Biomechanics Of The Musculoskeletal System by panamabustickets.com Studio Presently, you can conveniently to review every publication by online and download without spending great deals time for seeing book stores. Your best publication's title is here! You can discover your book to aid you obtain originality about the book you check out. Locate them in zip, txt, word, rar, kindle, ppt, and pdf report. basic biomechanics mccc basic biomechanics “it is important when learning about how the body moves (kinesiology) to also learn about the forces placed on the body that cause the movement.” lippert, p93

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.

/pdf/learning-to-monitor-machine-health-with-convolutional-bi-2vok1ch88q.pdf

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

This paper presents a tactile telepresence system employed for the localization of stiff inclusions embedded in a soft matrix. The system delivers a neuromorphic spike-based haptic feedback, encoding object stiffness, to the human fingertip. For the evaluation of the developed system, in this study a customized silicon phantom was fabricated inserting 12 inclusions made of 4 different polymers (3 replicas for each material). Such inclusions, all of them having the same shape, were encapsulated in a softer silicon matrix in randomized positions. Two main blocks composed the experimental setup. The first sub-setup included an optical sensor for tracking human hand movements and a piezoelectric disk, inserted into a glove at the level of the index fingertip, to deliver tactile feedback. The second sub-setup was a 3-axis cartesian motorized sensing platform which explored the silicon phantom through a spherical indenter mechanically linked to a load cell. The movements of the platform were based on the acquired hand gestures of the user. The normal force exerted during the active sliding was converted into temporal patterns of spikes through a neuronal model, and delivered to the fingertip via the vibrotactile glove. Inclusions were detected through modulation in the aforementioned patterns generated during the experimental trials. Results suggest that the presented system allows the recognition of the stiffness variation between the encapsulated inclusions and the surrounding matrix. As expected, stiffer inclusions were more frequently discriminated than softer ones, with about 70% of stiffer inclusions being identified in the proposed task. Future works will address the investigation of a larger set of materials in order to evaluate a finer distribution of stiffness values.

Neuromorphic haptic glove and platform with gestural control for tactile sensory feedback in medical telepresence applications

Development of wireless sensor network for museum environmental monitoring

Monitoring spatio-temporal parameters of gait, such as Stride Length, Gait Time and Gait Speed, through inertial measurement units is a research topic of great interest in clinics. Despite the high interest in the field, the development of a high accuracy wearable solution for spatio-temporal parameters assessment in a clinical scenario, suitable for both normal and pathological gait, is still an open challenge. In this study we proposed a novel solution to estimate spatio-temporal parameters through Inertial Measurement Units and an optimized strap-down approach for drift reduction. For the accuracy evaluation three healthy subjects were enrolled. Each subject was equipped with two inertial sensors placed on feet. Subjects were asked to repeat seven walking trials, through the calibration volume of an optoelectronic system, used as a reference. As a clinical application, we used the solution to assess fatigue effects on healthy and pathological subjects considering the 6-Minute Walking Test. Results showed high accuracy in the estimation of spatio-temporal parameters for each subject. The mean relative error of the Stride Length estimation among subjects was 1.1±0.7% and 0.8±0.5% for the left and right side. The clinical application was conducted on a cohort of ten patients with Systemic Sclerosis and ten age-matched healthy adults. Statistical differences were observed between populations in the Stride Length and in the Gait Speed. Moreover, all subjects experienced fatigue effect reporting lower values of spatio-temporal parameters in the last minute, compared to the first one. The excellent results in the accuracy evaluation encourage application of this methodology in normal and in pathological gait monitoring where a low error in the estimation of spatial gait parameters is required.

Accuracy Evaluation and Clinical Application of an Optimized Solution for Measuring Spatio-Temporal Gait Parameters

Experimental validation of a sensor to segment calibration procedure for MIMU based gait analysis

In this paper we propose a new protocol based on Virtual Reality and a low-cost haptic device for evaluating motion performance during perturbed 3D reaching tasks. The protocol presented herein was designed to assess how different force amplitudes and different reaching directions influence motor performance of healthy subjects. We developed a novel gaming scenario using Unity 3D and the Novint Falcon, a low-cost haptic joystick. The protocol consisted of six 3D point-to-point reaching tasks, which were performed by means of the Falcon while six different force fields were applied. Five subjects were enrolled in the study. During each task, subjects were asked to reach 80 targets. The trajectories of the end-effector, during each task, were recorded to calculate the following kinematic indices: duration of movement, length ratio, lateral deviation, aiming angle, speed metric and normalized jerk. The coefficient of variation was calculated to study the intra-subject variability to establish which indices better assessed the accuracy and the smoothness of the trajectories. Subsequently, two-way repeated measurement ANOVA tests were performed for all indices in order to ascertain effects of the 6 levels of force and the 8 directions of the reaching task. Length ratio and speed metric have proven the highest intra-subject repeatability as accuracy and smoothness indices, respectively. Statistical analysis demonstrated that all the accuracy indices are not sensitive to amplitude variation of the applied force field, nor to different target directions. Conversely, the smoothness indices showed statistical differences in both forces and directions. In particular, the speed metric is sensitive to the applied force, and the normalized jerk depends on the target directions.

Eduardo Palermo

Papers

Neuromorphic haptic glove and platform with gestural control for tactile sensory feedback in medical telepresence applications

Development of wireless sensor network for museum environmental monitoring

Accuracy Evaluation and Clinical Application of an Optimized Solution for Measuring Spatio-Temporal Gait Parameters

Experimental validation of a sensor to segment calibration procedure for MIMU based gait analysis

Performance evaluation of 3D reaching tasks using a low-cost haptic device and virtual reality