122,157 research outputs found
Computer-assisted orthopedic surgery
No full textComputer-assisted orthopedic surgery (CAOS) represents one of the most effective means of treatment in clinical orthopedics and, in general, in the treatment of different kinds of musculoskeletal diseases. In fact, the CAOS approach aims at optimizing the surgical process by enhancing the available information with quantitative data, measurements, and estimations during the execution of procedures, so as to enhance the overall surgery-related accuracy, improve the clinical outcomes, and reduce the invasiveness of the surgery itself. In order to achieve this goal, CAOS exploits and integrates a large number of technologies and methodologies, including robotics, tracking devices, clinical images, and modeling. A “biomechanically enhanced” surgery, based on CAOS solutions, may indeed obtain optimal outcomes in a “patient-specific” perspective. This chapter discusses the most relevant details about CAOS systems in terms of general workflow, designs, technologies, methodologies, and applications, with concise hints on the latest advances made by the integration of several concepts borrowed from the musculoskeletal biomechanics within the CAOS workflow
Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout
Full text linkObserving, classifying and assessing human movements is important in many applied fields, including human-computer interface, clinical assessment, activity monitoring and sports performance. The redundancy of options in planning and implementing motor programmes, the inter- and intra-individual variability in movement execution, and the time-continuous, high-dimensional nature of motion data make segmenting sequential movements into a smaller set of discrete classes of actions non-trivial. We aimed to develop and validate a method for the automatic classification of four popular functional fitness drills, which are commonly performed in current circuit training routines. Five inertial measurement units were located on the upper and lower limb, and on the trunk of fourteen participants. Positions were chosen by keeping into account the dynamics of the movement and the positions where commercially-available smart technologies are typically secured. Accelerations and angular velocities were acquired continuously from the units and used to train and test different supervised learning models, including k-Nearest Neighbors (kNN) and support-vector machine (SVM) algorithms. The use of different kernel functions, as well as different strategies to segment continuous inertial data were explored. Classification performance was assessed from both the training dataset (k-fold cross-validation), and a test dataset (leave-one-subject-out validation). Classification from different subsets of the measurement units was also evaluated (1-sensor and 2-sensor data). SVM with a cubic kernel and fed with data from 600 ms windows with a 10% overlap gave the best classification performances, yielding to an overall accuracy of 97.8%. This approach did not misclassify any functional fitness movement for another, but confused relatively frequently (2.8–18.9%) a fitness movement phase with the transition between subsequent repetitions of the same task or different drills. Among 1-sensor configurations, the upper arm achieved the best classification performance (96.4% accuracy), whereas combining the upper arm and the thigh sensors obtained the highest level of accuracy (97.6%) from 2-sensors movement tracking. We found that supervised learning can successfully classify complex sequential movements such as those of functional fitness workouts. Our approach, which could exploit technologies currently available in the consumer market, demonstrated exciting potential for future on-field applications including unstructured training
intra- operative validation of a novel method dedicated to quantify pivot-shift phenomenon
No full textValidation of a numerical model for the mechanical behavior of a continuous positive airway pressure mask
Full text linkFinite Element models (FEM) are developed for the analysis of the contact pressures exerted by a Continuous Positive Airway Pressure (CPAP) mask applied to a dummy head. This is seen as a preliminary step in the analysis of the mechanical effects of CPAP masks applied to human faces, such as recently employed for the care of COVID-19 patients, or other purposes. These mechanical effects can range from negligible, in the case of correct positioning, sufficiently light tension in the headgear, correct mask design, etc., to the possible development of device-related pressure ulcers and/or dentofacial deformations, especially in children. The results of Finite Element analyses are compared, for their validation, with experimental ones. The numerical analysis tool appears able to predict, at an acceptable cost, both the intensity and the area distribution of the contact pressures, as well as the force-displacement relationship occurring in the headgear. This might help the design and the production of more effective and tolerable CPAP masks
Characterization Method for Bending Sensor Applied for Smart Glove
No full textNowadays accurate measurements of hand movements of workers in sectors such as manufacturing, aerospace, and healthcare are requested for different purposes, such as health analysis or human-robot interactions. In Industry 4.0, analyzing how workers interact with tools, machinery, and processes by hand it is important to achieve optimal performance, quality control, safety, and ergonomics. Several techniques - motion capture and wearable sensors - are available with different levels of accuracy and applicability. In particular, smart gloves, and in specific bend sensors, represent a viable solution to measure comfort level, but commercial solutions lack accuracy and affordability. For this reason, new sensors applied to wearable devices and therefore suitable characterization methods are needed. In this paper, a novel platform that emulates the finger movements is proposed to evaluate the sensors used to measure the rotation of two or more finger joints. For example, one bend sensor is used to measure more than one finger joint. The platform integrates a dummy little and index finger of average dimensions. With respect to previous works, the proposed method was used to test commercial bend sensors bent in two points, corresponding to two finger joints. The experimental results confirmed the sensor characteristics, especially regarding the linearity (the maximum error is less than 2.5%) and the repeatability (the maximum error is less than 3.8%). Finally, a relationship between the resistance and the curvature was found when the sensor was bent in two points, obtaining the same characteristics in terms of linearity and repeatability
Quantifying the pivot shift test: A systematic review
No full textThis study aims to identify and summarize the evidence on the biomechanical parameters and the corresponding technologies which have been used to quantify the pivot shift test during the clinical and functional assessment of anterior cruciate ligament (ACL) injury and surgical reconstruction.
METHODS:
Search strategy Internet search of indexed scientific articles on the PubMed database, Web of Science and references on published manuscripts. No year restriction was used. Selection criteria Articles included were written only in English and related to search terms: "pivot shift" AND (OR "ACL"). The reviewers independently selected only those studies that included at least one quantitative parameter for the analysis of the pivot shift test, including both in vitro and in vivo analyses performed on human joint. Those studies that analysed only clinical grading were excluded from the analysis. Analysis After evaluating the methodological quality of the articles, the parameters found were summarized.
RESULTS:
Six hundred and eight studies met the inclusion criteria, and finally, 68 unique studies were available for the systematic review. Quantitative results were heterogeneous. The pivot shift test has been quantified by means of 25 parameters, but most of the studies focused on anterior-posterior translations, internal-external rotation and acceleration in anterior-posterior direction.
CONCLUSION:
Several methodologies have been identified and developed to quantify pivot shift test. However, clinical professionals are still lacking a 'gold standard' method for the quantification of knee joint dynamic laxity. A widespread adoption of a standardized pivot shift manoeuvre and measurement method to allow objective comparison of the results of ACL reconstructions is therefore desirable. Further development of measurement methods is indeed required to achieve this goal in a routine clinical scenario
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