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The musculoskeletal model used in this study. Biceps, brachialis and triceps lateral are represented in yellow, red and green, respectively. The wrapping object used to model the bony surface is represented in blue (color figure online)
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The tradeoff between speed and accuracy of human movements has been exploited from many different perspectives, such as experimental psychology, workspace design, human–machine interface. This tradeoff is formalized by Fitts’ law, which states a linear relationship between the duration and the difficulty of the movement. The bigger is the required...
Citations
... In the context of HCI, Shannon's information theory is applied to the concept of pointing, where Fitts' law models the relationship between movement time and index of difficulty. Fitts also introduced the Index of Performance, later termed throughput by [6], to measure the effective speed of data transmission in engineering, calculated as the amount of transmitted information divided by the time taken, typically measured in bits per second. This application of information theory helps analyze throughput in HCI tasks, extending beyond Fitts' law to consider input communication in noisy channels and sequential processes like text entry. ...
... Now putting x = 1 in Eq. (3), implies Eq. (4).Eq. (4) with x → y implies Eq. (5):It follows by Eq. (5) that Eq. (6) holds:It can be verified that Eq.(6) implies Eq.(7). ) = 0. Consequently, f(y) = cy q+1 . Letting f(1) = 1. ...
Licensee System Analytics. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0). 1|Introduction A supplementary portion of the research carried out in [1] is provided by the current work. The authors believe that the task is now complete and can be understood by using both analytical expressions and illustrative data to explain the newly developed study outcomes. The current paper is a substantial extension of an accepted paper [1]. The main contributions of [1] are: I. Providing the full detailed proofs of the limit theorem as well as the full proofs of Rényi Generalized Entropies RGEs extended properties and finding the discrete time domain PV-updates. A Mageed, I. (2024). On the shoulders of the three giants information theory, semi-group theory, and uncertain reasoning with information-theoretic applications to human computer interaction. Uncertainty discourse and applications, 1(2), 258-270. Abstract This paper provides a first-time ever unification of information theory, semi-group theory with the theory of uncertain reasoning, through functional perspective. Fundamentally, the threshold theorems for the Inference Functional (IF) were devised. Furthermore, numerical experiments are illustrated. Some information-theoretic applications to Human Computer Interaction (HCI) are provided. The paper ends with concluding remarks, open problems, and future research pathways.
... More recently, by using a neurocomputational model of the spinal cord, which includes connections with the motor cortex, the cerebellum, and the proprioceptive feedback pro- Fig. 8 Stroke segmentation of the word "Unfolding". Circles indicate the segmentation points, i.e., the points delimiting the strokes vided by tendons and Golgi organs, we have shown that movements aimed at reaching a spatial target are learned by a trial-and-error process, driven by two goals: reaching the desired position and minimizing the metabolic energy consumption to perform the movement [22]. We have also shown that when subjects are required to produce a handwritten sample by following a trajectory that is different from the one they normally draw, the trajectory is made of a larger number of strokes than the usual one [32]. ...
Reconstructing the trajectory from the static image of handwritten ink traces is useful in many practical applications envisaging handwriting analysis and recognition from offline data, as it allows the use of methods, algorithms, and tools that deal with online data, achieving better results than those achieved on offline data. In this work, the trajectory recovery is addressed by combining a general graph traversal algorithm with knowledge about the processes involved in human learning of motor skills to perform voluntary and complex movements. The effectiveness of the proposed approach has been quantitatively and extensively evaluated on large and publicly available datasets, containing English and French multi-stroke words and isolated characters. The experimental results show that our approach outperforms the existing ones in terms of Root Mean Square Error and Dynamic Time Warping distance between the recovered trajectories and the actual ones. Furthermore, an “off-the-shelf” online recognition system provided with the trajectory recovered from offline samples showed an overall reduction of 6.8% with respect to the recognition rate achieved by the system when provided with online data; the reduction, however, drops to 2.4% once preprocessing errors are not taken into account.
... 7). Due to their low computational cost, transfer functions are well suited to investigate the interplay within large networks of neuron populations; however, transfer functions cannot adequately represent the spiking activity of single α-motoneurons (e.g., Raphael et al., 2010;Li S. et al., 2015;Teka et al., 2017;Parziale et al., 2020). The second approach employs biophysical neuron models, which explicitly model the membrane dynamics of individual neurons in response to synaptic inputs (Section 3.1.1). ...
... By implementing both mechanical and neural interactions between the simulated muscles, these approaches are suited to improving our understanding of strategies the central neural system could use to control the neuromuscular system. To investigate the spinal circuity in more detail, modellers could also consider the influences of Renshawcells, sensory pathways involving more than one interneuron (e.g., Stienen et al., 2007;Raphael et al., 2010;Buhrmann and Di Paolo, 2014;Parziale et al., 2020) and γ-motoneurons, which innervate muscle spindles (Li S. et al., 2015). These models can help researchers to understand how certain connectivity rules and their modulation enable the central nervous system to perform numerous different tasks with one anatomical kind of neuronal network. ...
Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson’s disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.
... The execution of a learned movement, i.e. the realization of a motor program, results from the interaction between brain areas, spinal cord networks, muscles and the proprioceptive receptors [21,27]. In a nutshell, to initiate the movement, the brain sends commands to recruit the muscles and to set the forces they have to exert on the bones they are connected to, while, during execution, the spinal cord modulates such commands depending on the information received by the proprioceptive receptors in order to keep the execution as close as possible to the learned one. ...
Experimental studies led by Lashley and Raibert in the early phase of human movement science highlighted the phenomenon of motor equivalence, according to which complex movements are represented in the brain abstractly, in a way that is independent of the effector used for the execution of the movement. This abstract representation is known as motor program and it defines the temporal sequence of target points the effector has to move towards to accomplish the desired movement. We present and compare two algorithms for the extraction of motor programs from handwriting samples. One algorithm considers that lognormal velocity profiles are an invariant characteristic of reaching movements and it identifies the position of the target points by analysing the velocity profile of samples. The other algorithm seeks target points by identifying the trajectory points corresponding to maximum curvature variations because experimental studies have shown that the activity of the primary motor cortex encodes the direction of the movement. We have compared the performance of the two algorithms in terms of the number of virtual target points extracted by handwriting samples generated by 32 subjects with their dominant and non-dominant hands. The results have shown that the two algorithms show a similar performance over 55% of samples but the extraction of motor programs by analysing the curvature variations is more robust to noise and unmodeled motor variability.KeywordsMotor equivalenceMotor program extractionHandwriting representation
... Such data can also determine the impact of orthotics, muscle training routines, and other treatments. Dynamic electromyography allows you to link muscle movement to a singular purpose with pinpoint accuracy (Parziale, 2020) [18] . Various factors might cause the usual, complicated walking pattern to be disturbed. ...
Introduction: Surface electromyography (sEMG) equipment technological advancements are opening more and more new options for the use of this technology in many medical areas and rehabilitation, including physiotherapy on land and in the water environment. Objective: The purpose of this study is to investigate the use of electromyography (EMG) wearable sensors in hydro-rehabilitation and to analyze the use of surface electromyography in the rehabilitation of various diseases on land and in the water environment Methods: Studies were searched for on the Pub-Med, Science direct, and Google Scholar databases using the following descriptors: "sEMG rehabilitation", "sEMG physiotherapy", "surface electromyography physiotherapy", "sEMG hydrotherapy", resulting in 57,200 citations in total. After reviewing for inclusion criteria-methodological quality assessment using the Physiotherapy Evidence Database (PEDro) scale and consistency with the theme of systematic review-152 studies remained in the analysis. Results: A total of 153 articles were found in 3 databases searched, only 30 were included and classified with good methodological quality by Pedro because they were related to surface electromyography, physiotherapy on land and hydrotherapy. Conclusion: The role of EMG sensors to measure muscle activity is extremely important for the physiotherapist to monitor and evaluate the clinical process and the effectiveness of hydro-rehabilitation as a treatment method. More research is needed in order to find a functional and reliable way of use.
... The analysis of handwriting dynamics might provide a cheap and noninvasive method for evaluating the disease progression [18], since handwriting requires a precise and properly coordinated control of the body [32], which, in turn, envisages the cognitive and motor functions that are impaired by the insurgence of the diseases. Cheap and widely available graphic tablets have then been introduced to administer various handwriting and/or drawing tests and to record kinematic and dynamic information of the performed movements, and several studies on the neural processes involved in motor learning and execution, as well as the deterioration of motor processes involved in writing and drawing in patients, have highlighted the most distinctive features of patient's movements [2,19,28,35,36,[38][39][40]. Based on these findings, machine learning methods combining a variety of tasks, features, and classifiers to discriminate between healthy subjects and AD patients have been proposed in the literature [3,7,9,10,26,27,41]. ...
The analysis of handwriting and drawing has been adopted since the early studies to help diagnose neurodegenerative diseases, such as Alzheimer’s and Parkinson’s. Departing from the current state-of-the-art methods that approach the problem of discriminating between healthy subjects and patients by using two- or multi-class classifiers, we propose to adopt one-class classifier models, as they require only data by healthy subjects to build the classifier, thus avoiding to collect patient data, as requested by competing techniques. In this framework, we evaluated the performance of three models of one-class classifiers, namely the Negative Selection Algorithm, the Isolation Forest and the One-Class Support Vector Machine, on the DARWIN dataset, which includes 174 subjects performing 25 handwriting/drawing tasks. The comparison with the state-of-the-art shows that the methods achieve state-of-the-art performance, and therefore may represent a viable alternative to the dominant approach.KeywordsAlzheimer’s disease diagnosisHandwriting analysisOne-class classifiersNegative selection algorithmIsolation forestOne-class support vector machine
... To overcome these problems, other approaches have investigated the neural processes involved in motor learning and execution, as well as the deterioration of motor processes involved in writing and drawing in subjects affected by NDs, highlighting the most distinctive features of patient's movements [5,23,33,41,42,[44][45][46]. The findings provided by these methods is that handwriting analysis might provide a cheap, quick, and non-invasive method for both diagnosing the diseases and evaluating its progression, and therefore may represent a viable solution to develop a test that can be administered routinely by family doctor or even by trained caregivers, thus allowing a continuous monitoring along the years, avoiding overloading hospitals and specialized medical personnel and reducing the cost for both diagnosis and treatment. ...
We present a method for discriminating between healthy subjects and Alzheimer’s diseases patients from on-line handwriting. Departing from the current state of the art methods, that adopts machine learning methods and tools for building the classifier, we propose to apply the Negative Selection Algorithm. The major advantage of the proposed method in comparison with others machine learning techniques is that it requires only data by healthy subjects to build the classifier, thus avoiding to collect patient data, as requested by competing techniques. Experiments results involving data produced by 175 subjects show that the proposed method achieves state-of-the-art performance.KeywordsImmunocomputingAlzheimer’s disease diagnosisHandwriting analysis
... They conclude that "In all human motor skills, there is, therefore, an advantage to be gained by those who are most economical in their movements" A biologically inspired approach for recovering the trajectory of off-line handwriting (Sparrow and Newell (1998)). More recently, by using a neurocomputational model of the spinal cord, which includes connections with the motor cortex, the cerebellum and the proprioceptive feedback provided by tendons and Golgi organs, we have showed that movements aimed at reaching a spatial target are learned by a trial-and-error process, driven by two goals: reaching the desired position and minimizing the metabolic energy consumption to perform the movement (Parziale et al (2020)). We have also shown that when subjects are required to produce an handwritten sample by following a trajectory that is different from the one they normally draw, the trajectory is made of a larger number of strokes than the usual one (Senatore and Marcelli (2019)). ...
Background: Reconstructing the trajectory from the static image of handwritten ink traces is useful in many practical applications envisaging handwriting analysis and recognition from off-line data, as it allows to use methods, algorithms and tools that deal with on-line data, achieving better results than those achieved on off-line data.
Methods: In this work we addressed the trajectory recovery by proposing an approach inspired by the processes involved in human learning of motor skills to perform voluntary and complex movements. As humans learn motor skills by a trial-and-error process driven by the performance and the consumption of metabolic energy, our approach generates a trajectory and estimates the consumption of metabolic energy needed to execute it, and in case it is deemed too energy demanding, a new one is generated and its energy consumption is evaluated. Eventually the one corresponding to the minimum energy consumption among the extant ones is selected as the actual one.
Results and Conclusions: The effectiveness of the proposed approach has been quantitatively and extensively evaluated on a large and publicly available dataset, containing multi-stroke words. The experimental results show that our approach outperforms the existing ones in terms of Root Mean Square Error and Dynamic Time Warping distance between the recovered trajectories and the actual ones. Furthermore, an on-line recognition system provided with the trajectory recovered from off-line samples showed an overall reduction of about 1% with respect to the recognition rate achieved by the system when provided with on-line data.
... As we all know, "static" is the biggest attribute of an image. In an image, the discriminative features of one area point are very likely to be applicable to other areas [18,19]. erefore, it appears to calculate the mean or maximum value of the features in a certain area of the image and use it to represent the features of the area. ...
... 18: e angle changes of the shoulder and elbow joints at the end of the back swing. ...
At present, in sports training for volleyball, it still mainly depends on the personal experience of the coach. Training costs are high, and the quality is difficult to maintain stable. Even with the introduction of training assistance software, it is often necessary to manually enter complex data, and the research samples are mostly single individuals. Serving is one of the basic and important technical movements of volleyball, and its standardization is of great significance to the stable performance of the scene. This article proposes an analysis of the volleyball player’s arm trajectory based on the background of human posture recognition and analysis, based on the neural network model. The changes in the angles of the shoulders, elbows, and wrist when serving the ball reflect the different trajectories of the arm. Experiments show that the height of the throwing arm from the ground accounts for 98% of the height. The horizontal angle of the throwing arm at the moment the ball leaves the hand is positively correlated with the throwing time and height, and the reasonable trajectory has an impact on the stability of the throwing ball. The closer the trajectory of the tossing arm is to the vertical, the more stable the tossing is.
... In addition, we plan to use the data gathered by HaReS to train machine learning methods that can automatically predict whether the user is improving or not by reviewing their history, or to state whether a user is prone to suffer any hand motor or cognitive disease such as Parkinson's. We plan to also involve computational models [58][59][60] to produce EMG signals that would enable to provide an automatic analysis to assist therapists to understand the sEMG signals provided by HaReS. ...
Featured Application
The system described in this work is intended to be applied to hand motor skill rehabilitation and recovery.
Abstract
In this work, we introduce HaReS, a hand rehabilitation system. Our proposal integrates a series of exercises, jointly developed with a foundation for those with motor and cognitive injuries, that are aimed at improving the skills of patients and the adherence to the rehabilitation plan. Our system takes advantage of a low-cost hand-tracking device to provide a quantitative analysis of the performance of the patient. It also integrates a low-cost surface electromyography (sEMG) sensor in order to provide insight about which muscles are being activated while completing the exercises. It is also modular and can be deployed on a social robot. We tested our proposal in two different facilities for rehabilitation with high success. The therapists and patients felt more motivation while using HaReS, which improved the adherence to the rehabilitation plan. In addition, the therapists were able to provide services to more patients than when they used their traditional methodology.
