Buildings' Biaxial Tilt Assessment Using Inertial Wireless Sensors and a Parallel Training Model.

Applications of MEMS-based sensing technology are beneficial and versatile. If these electronic sensors integrate efficient processing methods, and if supervisory control and data acquisition (SCADA) software is also required, then mass networked real-time monitoring will be limited by cost, revealing a research gap related to the specific processing of signals. Static and dynamic accelerations are very noisy, and small variations of correctly processed static accelerations can be used as measurements and patterns of the biaxial inclination of many structures. This paper presents a biaxial tilt assessment for buildings based on a parallel training model and real-time measurements using inertial sensors, Wi-Fi Xbee, and Internet connectivity. The specific structural inclinations of the four exterior walls and their severity of rectangular buildings in urban areas with differential soil settlements can be supervised simultaneously in a control center. Two algorithms, combined with a new procedure using successive numeric repetitions designed especially for this work, process the gravitational acceleration signals, improving the final result remarkably. Subsequently, the inclination patterns based on biaxial angles are generated computationally, considering differential settlements and seismic events. The two neural models recognize 18 inclination patterns and their severity using an approach in cascade with a parallel training model for the severity classification. Lastly, the algorithms are integrated into monitoring software with 0.1° resolution, and their performance is verified on a small-scale physical model for laboratory tests. The classifiers had a precision, recall, F1-score, and accuracy greater than 95%.

A Computer Method for Pronation-Supination Assessment in Parkinson's Disease Based on Latent Space Representations of Biomechanical Indicators.

One problem in the quantitative assessment of biomechanical impairments in Parkinson's disease patients is the need for scalable and adaptable computing systems. This work presents a computational method that can be used for motor evaluations of pronation-supination hand movements, as described in item 3.6 of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The presented method can quickly adapt to new expert knowledge and includes new features that use a self-supervised training approach. The work uses wearable sensors for biomechanical measurements. We tested a machine-learning model on a dataset of 228 records with 20 indicators from 57 PD patients and eight healthy control subjects. The test dataset's experimental results show that the method's precision rates for the pronation and supination classification task achieved up to 89% accuracy, and the F1-scores were higher than 88% in most categories. The scores present a root mean squared error of 0.28 when compared to expert clinician scores. The paper provides detailed results for pronation-supination hand movement evaluations using a new analysis method when compared to the other methods mentioned in the literature. Furthermore, the proposal consists of a scalable and adaptable model that includes expert knowledge and affectations not covered in the MDS-UPDRS for a more in-depth evaluation.

Computer models evaluating hand tremors in Parkinson's disease patients.

One of the most characteristic signs of Parkinson's disease (PD) is hand tremor. The MDS-UPDRS scale evaluates different aspects of the disease. The tremor score is a part of the MDS-UPDRS scale, which provides instructions for rating it, by observation, with an integer from 0 to 4. Nevertheless, this form of assessment is subjective and dependent on visual acuity, clinical judgment, and even the mood of the individual examiner. On the other hand, in many cases, existing computational models proposed to resolve the disadvantages of the MDS-UPDRS scale may have uncertainty in differentiating a category of a slight Parkinson tremor from voluntary movements. In this study, 554 measurements from Parkinson's patients, and 60 measurements from healthy subjects, were recorded with inertial sensors placed on the back of each hand. Five biomechanical indicators characterised the hand tremor. With these indicators, the three fuzzy inference models proposed can differentiate, in the first instance, the presence of postural or resting tremor from a normal movement of the hand, and if detected, to determine its severity. The fuzzy inference models allowed following the criteria of the MDS-UPDRS scale, providing an evaluation with an accuracy of two decimal digits and which, due to its simplicity, can be implemented in clinical environments. The assessments of three experts validated the computer model.

Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson's disease patients.

Nowadays, the Unified Parkinson Disease Rating Scale supported by the Movement Disorder Society (MDS-UPDRS), is a standardized and widely accepted instrument to rate Parkinson's disease (PD). This work presents a thorough analysis of item 3.6 of the MDS-UPDRS scale which corresponds to the pronation and supination hand movements. The motivation for this work lies in the objective quantification of motor affectations not covered by the MDS-UPDRS scale such as unsteady oscillations and velocity decrements during the motor exploration. Overall, 12 different bio-mechanical features were quantified based on measurements performed by inertial measurement units (IMUs). After a feature selection process, the selected bio-mechanical features were used as inputs for a fuzzy inference model that predicts the stage of development of the disease in each patient. In addition to this model's output, the scores of three different expert examiners and the output of a fuzzy inference model which covers affectations strictly attached the MDS-UPDRS guidelines, were also considered to obtain an integrated computational model. The proposed integrated model was incorporated using the Analytic Hierarchy Process (AHP), which gives the novelty of a combined score that helps expert examiners to give a broader assessment of the disease that covers both affectations mentioned in the MDS-UPDRS guidelines and affectations not covered by it in an objective manner.

Computer model for leg agility quantification and assessment for Parkinson's disease patients.

Parkinson's disease (PD) is a progressive disorder that affects motor regulation. The Unified Parkinson's Disease Rating Scale sponsored by the Movement Disorder Society (MDS-UPDRS) quantifies the illness progression based on clinical observations. The leg agility is an item in this scale, yet only a visual detection of the features is used, leading to subjectivity. Overall, 50 patients (85 measurements) with varying motor impairment severity were asked to perform the leg agility item while wearing inertial sensor units on each ankle. We quantified features based on the MDS-UPDRS and designed a fuzzy inference model to capture clinical knowledge for assessment. The model proposed is capable of capturing all details regardless of the task speed, reducing the inherent uncertainty of the examiner observations obtaining a 92.35% of coincidence with at least one expert. In addition, the continuous scale implemented in this work prevents the inherent "floor/ceil" effect of discrete scales. This model proves the feasibility of quantification and assessment of the leg agility through inertial signals. Moreover, it allows a better follow-up of the PD patient state, due to the repeatability of our computer model and the continuous output, which are not objectively achievable through visual examination. Graphical abstract ᅟ.

Pronation and supination analysis based on biomechanical signals from Parkinson's disease patients.

In this work, a fuzzy inference model to evaluate hands pronation/supination exercises during the MDS-UPDRS motor examination is proposed to analyze different extracted features from the bio-mechanical signals acquired from patients with Parkinson's disease (PD) in different stages of severity. Expert examiners perform visual assessments to evaluate several aspects of the disease. Some previous work on this subject does not contemplate the MDS-UPDRS guidelines. The method proposed in this work quantifies the biomechanical features examiners evaluate. The extracted characteristics are used as inputs of a fuzzy inference model to perform an assessment strictly attached to the MDS-UPDRS. The acquired signals are processed by techniques of digital signal processing and statistical analysis. The experiments were performed in collaboration with clinicians and patients from different PD associations and institutions. In total, 210 different measurements of patients with PD, plus 20 different measurements of healthy control subjects were performed. With objective values rated by several feature extraction procedures there is the possibility to track down the disease evolution in a patient, and to detect subtle changes in the patient's condition.

Fuzzy inference model evaluating turn for Parkinson's disease patients.

Parkinson's disease is a chronic illness that affects motor skills. The Unified Parkinson's Disease Rating Scale sponsored by the Movement Disorder Society (MDS-UPDRS) quantifies the current state of the disease based on clinician's observations. In this scale, turning is part of the gait assessment, yet specific guidelines on which features to observe and rate are still unclear. What is more, only visual impairment detection is used as the main subjective rating tool. In this respect, four biomechanical features are extracted from sensors worn on the lower limbs in this work. Afterwards, a turning assessment score is computed by means of a fuzzy inference model constructed based on the examiners knowledge. Overall, 46 patients with varying motor impairment severity underwent a full MDS-UPDRS motor examination and were monitored using a measurement system that includes inertial sensors on each ankle. Turning rating scores computed are reasonably consistent with examiners opinions. Nevertheless, the model proposed herein will always output the same score given the same inputs; whereas the subjective nature of examiners observations translates into uncertainty and variability in the rating scores. Furthermore, the continuous scale implemented in this work prevents the floor/ceiling effect inherent of discrete scales.