Analysis of postures for handwriting on touch screens without using tools

The act of handwriting affected the evolutionary development of humans and still impacts the motor cognition of individuals. However, the ubiquitous use of digital technologies has drastically decreased the number of times we really need to pick a pen up and write on paper. Nonetheless, the positive cognitive impact of handwriting is widely recognized, and a possible way to merge the benefits of handwriting and digital writing is to use suitable tools to write over touchscreens or graphics tablets. In this manuscript, we focus on the possibility of using the hand itself as a writing tool. A novel hand posture named FingerPen is introduced, and can be seen as a grasp performed by the hand on the index finger. A comparison with the most common posture that people tend to assume (i.e. index finger-only exploitation) is carried out by means of a biomechanical model. A conducted user study shows that the FingerPen is appreciated by users and leads to accurate writing traits.

Estimation of Tibiofemoral and Patellofemoral Joint Forces during Squatting and Kneeling

This study examined the differences of knee joint forces between lowering to, or rising from squat, and typical final postures of squatting and kneeling. A biomechanical model of the lower limb was configured considering large knee flexion angles, multiple floor contact points, and the soft tissue contact between the thigh and calf. Inverse dynamics were used to determine muscle and compressive joint forces in the tibiofemoral and patellofemoral joints. Data were obtained from a group of 13 male subjects by means of 3D motion capturing, two force plates, a pressure-sensitive pad, and electromyography. During lowering into the kneeling/squatting positions and rising from them, the model exhibited the anticipated high maximum forces of 2.6 ± 0.39 body weight (BW) and 3.4 ± 0.56 BW in the tibiofemoral and patellofemoral joints. Upon attainment of the static terminal squatting and kneeling positions, the forces fell considerably, remaining within a range of between 0.5 and 0.7 BW for the tibiofemoral joint and 0.9 to 1.1 BW for the patellofemoral joint. The differences of the knee joint forces between the final postures of squatting and kneeling remained on average below 0.25 BW and were significant only for the tibiofemoral joint force.

From decision to action: Detailed modelling of frog tadpoles reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming movements in response to sensory signals

How does the brain process sensory stimuli, and decide whether to initiate locomotor behaviour? To investigate this question we develop two whole body computer models of a tadpole. The “Central Nervous System” (CNS) model uses evidence from whole-cell recording to define 2000 neurons in 12 classes to study how sensory signals from the skin initiate and stop swimming. In response to skin stimulation, it generates realistic sensory pathway spiking and shows how hindbrain sensory memory populations on each side can compete to initiate reticulospinal neuron firing and start swimming. The 3-D “Virtual Tadpole” (VT) biomechanical model with realistic muscle innervation, body flexion, body-water interaction, and movement is then used to evaluate if motor nerve outputs from the CNS model can produce swimming-like movements in a volume of “water”. We find that the whole tadpole VT model generates reliable and realistic swimming. Combining these two models opens new perspectives for experiments.

Finger stability in precision grips

Stable precision grips using the fingertips are a cornerstone of human hand dexterity. Occasionally, however, our fingers become unstable and snap into a hyper-extended posture. This is because multi-link mechanisms, like our fingers, can buckle under tip forces. Suppressing this instability is crucial for hand dexterity, but how the neuromuscular system does so is unknown. Here we show that finger stability is due to the stiffness from muscle contraction and likely not feedback control. We recorded maximal force application with the index finger and found that most buckling events lasted less than 50ms, too fast for sensorimotor feedback to act. However, a biomechanical model of the finger predicted that muscle-induced stiffness is also insufficient for stability at maximal force unless we add springs to stiffen the joints. We tested this prediction in 39 volunteers. Upon adding stiffness, maximal force increased by 34±3%, and muscle electromyography readings were 21±3% higher for the finger flexors (mean±standard error). Hence, people refrain from applying truly maximal force unless an external stabilizing stiffness allows their muscles to apply higher force without losing stability. Muscle recordings and mathematical modeling show that the splint offloads the demand for muscle co-contraction and this reduced co-contraction with the splint underlies the increase in force. But more stiffness is not always better. Stiff fingers would interfere the ability to passively adapt to complex object geometries and precisely regulate force. Thus, our results show how hand function arises from neurally tuned muscle stiffness that balances finger stability with compliance.

Equine biomechanical models for three-dimensional kinematics analysis: literature review/ Modelos biomecânicos equinos para análise de cinemática tridimensional: revisão de literatura

Three-dimensional analysis in horses has been widely used in the past years due to technological advancement. With the objective of conducting a literature review of the applicability of existing evidence in horses of a biomechanical model focusing on three-dimensional kinematics and its production in Veterinary Medicine, we searched in the databases: ScienceDirect, SciELO and PubMed. To access them, using as key-words: "Three-dimensional kinematic model in equines", "equine kinematic analysis", "biomechanics of equine locomotion", "equine kinematic model". Selection criteria were papers published between: paper published between 1990 and 2020, in English, with free electronic access and in which characteristics of a three-dimensional kinematic model in horses were mentioned. Most studies were experimental, and population included both healthy horses and pathological ones. Three-dimensional kinematic model was used mainly to understand the analysed movement and using as model the full body. There is scientific evidence on the use of biomechanical models for three-dimensional kinematic analysis in horses published in the period studied, used by professionals in veterinary medicine. The objectives of using the model were specific to the type of movement or pathology of the horse and consistent with the characteristics of the studies.

Detection of Subclinical Keratoconus Using a Novel Combined Tomographic and Biomechanical Model Based on an Automated Decision Tree

The aim of this study was to develop a predictive model for subclinical keratoconus (SKC) based on decision tree (DT) algorithms. A total of 194 eyes (including 105 normal eyes and 89 SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. DT models were generated in the training database using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms. The discriminating rules of the CART model selected variables of the Belin/Ambrósio deviation (BAD-D), stiffness parameter at first applanation (SPA1), back eccentricity (Becc), and maximum pachymetric progression index in order, while the CHAID model selected BAD-D, deformation amplitude ratio, SPA1, and Becc. The CART model allowed discrimination between normal and SKC eyes with 92.2% accuracy, which was higher than that of the CHAID model (88.3%), BAD-D (82.0%), Corvis biomechanical index (CBI, 77.3%), and tomographic and biomechanical index (TBI, 78.1%). The discriminating performance of the CART model was validated with 92.4% accuracy, while the CHAID model was validated with 86.4% accuracy in the validation database. Thus, the CART model using tomography and biomechanical imaging was an excellent model for SKC screening and provided easy-to-understand discriminating rules.