Different neuromuscular control mechanisms regulate static and dynamic balance: A center-of-pressure analysis in young adults.

The analysis of the center of pressure (CoP) trajectory, derived from force platforms, is a widely accepted measure to investigate postural balance control. The CoP trajectory could be analyzed as a physiological time-series through a general stochastic modeling framework (i.e., Stabilogram Diffusion Analysis (SDA)). Critical point divides short-term from long-term regions and diffusion coefficients reflect the level of stochastic activity of the CoP. Sample Entropy (SampEn) allows quantifying the CoP complexity in terms of regularity. Thus, this study aimed to understand whether SDA and SampEn could discriminate the neuromuscular control mechanisms underpinning static and dynamic postural tasks. Static balance control and its relationship with dynamic balance control were investigated through the CoP velocity (Mean Velocity) and the area of the 95th percentile ellipse (Area95). Balance was assessed in 15 subjects (age: 23.13 ± 0.99 years; M = 9) over a force platform under two conditions: static (ST) and dynamic, both in anterior-posterior (DAP) and medio-lateral (DML) directions. During the DAP and DML, subjects stood on an unstable board positioned over a force platform. Short-term SDA diffusion coefficients and critical points were lower in ST than in DAP and DML (p < 0.05). SampEn values resulted greater in ST than in DAP and DML (p < 0.001). As expected, lower values of Area95 (p < 0.001) and Mean Velocity (p < 0.001) were detected in the easiest condition, the ST, compared to DAP and DML. No significant correlations between static and dynamic balance performances were detected. Moreover, differences in the diffusion coefficients were detected comparing DAP and DML (p < 0.05). In the anterior-posterior direction, the critical point occurred at relatively small intervals in DML compared to DAP (p < 0.001) and ST (p < 0.001). In the medio-lateral direction, the critical point differed only between DAP and DML (p < 0.05). Overall, SDA analysis pointed out a less tightly regulated neuromuscular control system in the dynamic tasks, with closed-loop corrective feedback mechanisms called into play at different time intervals in the three conditions. SampEn results reflected more attention and, thus, less automatic control mechanisms in the dynamic conditions, particularly in the medio-lateral task. The different neuromuscular control mechanisms that emerged in the static and dynamic balance tasks encourage using both static and dynamic tests for a more comprehensive balance performance assessment.

Improving Robotic Hand Prosthesis Control With Eye Tracking and Computer Vision: A Multimodal Approach Based on the Visuomotor Behavior of Grasping.

The complexity and dexterity of the human hand make the development of natural and robust control of hand prostheses challenging. Although a large number of control approaches were developed and investigated in the last decades, limited robustness in real-life conditions often prevented their application in clinical settings and in commercial products. In this paper, we investigate a multimodal approach that exploits the use of eye-hand coordination to improve the control of myoelectric hand prostheses. The analyzed data are from the publicly available MeganePro Dataset 1, that includes multimodal data from transradial amputees and able-bodied subjects while grasping numerous household objects with ten grasp types. A continuous grasp-type classification based on surface electromyography served as both intent detector and classifier. At the same time, the information provided by eye-hand coordination parameters, gaze data and object recognition in first-person videos allowed to identify the object a person aims to grasp. The results show that the inclusion of visual information significantly increases the average offline classification accuracy by up to 15.61 ± 4.22% for the transradial amputees and of up to 7.37 ± 3.52% for the able-bodied subjects, allowing trans-radial amputees to reach average classification accuracy comparable to intact subjects and suggesting that the robustness of hand prosthesis control based on grasp-type recognition can be significantly improved with the inclusion of visual information extracted by leveraging natural eye-hand coordination behavior and without placing additional cognitive burden on the user.

Publisher Correction: Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Gaze, behavioral, and clinical data for phantom limbs after hand amputation from 15 amputees and 29 controls.

Despite recent advances in prosthetics, many upper limb amputees still use prostheses with some reluctance. They often do not feel able to incorporate the artificial hand into their bodily self. Furthermore, prosthesis fitting is not usually tailored to accommodate the characteristics of an individual's phantom limb sensations. These are experienced by almost all persons with an acquired amputation and comprise the motor and postural properties of the lost limb. This article presents and validates a multimodal dataset including an extensive qualitative and quantitative assessment of phantom limb sensations in 15 transradial amputees, surface electromyography and accelerometry data of the forearm, and measurements of gaze behavior during exercises requiring pointing or repositioning of the forearm and the phantom hand. The data also include acquisitions from 29 able-bodied participants, matched for gender and age. Special emphasis was given to tracking the visuo-motor coupling between eye-hand/eye-phantom during these exercises.

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics.

A hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person's life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics.

On the Visuomotor Behavior of Amputees and Able-Bodied People During Grasping.

Visual attention is often predictive for future actions in humans. In manipulation tasks, the eyes tend to fixate an object of interest even before the reach-to-grasp is initiated. Some recent studies have proposed to exploit this anticipatory gaze behavior to improve the control of dexterous upper limb prostheses. This requires a detailed understanding of visuomotor coordination to determine in which temporal window gaze may provide helpful information. In this paper, we verify and quantify the gaze and motor behavior of 14 transradial amputees who were asked to grasp and manipulate common household objects with their missing limb. For comparison, we also include data from 30 able-bodied subjects who executed the same protocol with their right arm. The dataset contains gaze, first person video, angular velocities of the head, and electromyography and accelerometry of the forearm. To analyze the large amount of video, we developed a procedure based on recent deep learning methods to automatically detect and segment all objects of interest. This allowed us to accurately determine the pixel distances between the gaze point, the target object, and the limb in each individual frame. Our analysis shows a clear coordination between the eyes and the limb in the reach-to-grasp phase, confirming that both intact and amputated subjects precede the grasp with their eyes by more than 500 ms. Furthermore, we note that the gaze behavior of amputees was remarkably similar to that of the able-bodied control group, despite their inability to physically manipulate the objects.

Analyzing the Trade-Off Between Training Session Time and Performance in Myoelectric Hand Gesture Recognition During Upper Limb Movement.

Although remarkable improvements have been made, the natural control of hand prostheses in everyday life is still challenging. Changes in limb position can considerably affect the robustness of pattern recognition-based myoelectric control systems, even if various strategies were proposed to mitigate this effect. In this paper, we investigate the possibility of selecting a set of training movements that is robust to limb position change, performing a trade-off between training time and accuracy. Four able-bodied subjects were recorded while following a training protocol for myoelectric hand prostheses control. The protocol is composed of 210 combinations of arm positions, forearm orientations, wrist orientations and hand grasps. To the best of our knowledge, it is among the most complete including changes in limb positions. A training reduction paradigm was used to select subsets of training movements from a group of subjects that were tested on the left-out subject's data. The results show that a reduced training set (30 to 50 movements) allows a substantial reduction of the training time while maintaining reasonable performance, and that the trade-off between performance and training time appears to depend on the chosen classifier. Although further improvements can be made, the results show that properly selected training sets can be a viable strategy to reduce the training time while maximizing the performance of the classifier against variations in limb position.

A quantitative taxonomy of human hand grasps.

BACKGROUND: A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified. METHODS: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic. RESULTS: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies. CONCLUSIONS: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields.

Moderate treadmill run worsened static but not dynamic postural stability of healthy individuals.

PURPOSE: Running has been demonstrated to be one of the most relevant exercise in altering static postural stability, while limiting attention has been paid to its effects on dynamic postural stability. The aim of the present study was to investigate if 25 min of moderate running on a treadmill altered static and dynamic postural stability in healthy subjects. METHODS: Eight female and six male participants (age 27.7 ± 8.3 years, height 170.9 ± 12.2 cm, weight 63.9 ± 15.6 kg) took part in the study. Before and after the run static postural stability was evaluated on a stabilometric platform (10 trials of 30 s each), while dynamic postural stability was assessed on an instrumented unstable platform (2 trials of 30 s each). RESULTS: After the treadmill run the area of the confident ellipse (from 67.97 ± 34.56 to 93.08 ± 50.00 mm2), sway path velocity (from 6.92 ± 1.85 to 7.83 ± 2.57 mm/s), sway area velocity (from 6.88 ± 3.27 to 9.54 ± 5.36 mm2/s), and medio-lateral maximal oscillation (from 9.48 ± 2.80 to 11.44 ± 3.64 mm) significantly increased. Stabilogram diffusion analysis showed no statistically significant difference in the diffusion coefficients, both short and long term. No statistically significant differences were reported in all the parameters of the dynamic postural stability test. CONCLUSION: The contrasting results of the static and dynamic postural stability tests raise the question of which are the more selective tests to assess the acute effect of physical exercise on postural stability among healthy individuals. The proper interaction of both static and dynamic postural evaluations could represent the next challenge in the postural stability assessment.

Head-mounted eye gaze tracking devices: An overview of modern devices and recent advances.

An increasing number of wearable devices performing eye gaze tracking have been released in recent years. Such devices can lead to unprecedented opportunities in many applications. However, staying updated regarding the continuous advances and gathering the technical features that allow to choose the best device for a specific application is not trivial. The last eye gaze tracker overview was written more than 10 years ago, while more recent devices are substantially improved both in hardware and software. Thus, an overview of current eye gaze trackers is needed. This review fills the gap by providing an overview of the current level of advancement for both techniques and devices, leading finally to the analysis of 20 essential features in six head-mounted eye gaze trackers commercially available. The analyzed characteristics represent a useful selection providing an overview of the technology currently implemented. The results show that many technical advances were made in this field since the last survey. Current wearable devices allow to capture and exploit visual information unobtrusively and in real time, leading to new applications in wearable technologies that can also be used to improve rehabilitation and enable a more active living for impaired persons.

Comparison of six electromyography acquisition setups on hand movement classification tasks.

Hand prostheses controlled by surface electromyography are promising due to the non-invasive approach and the control capabilities offered by machine learning. Nevertheless, dexterous prostheses are still scarcely spread due to control difficulties, low robustness and often prohibitive costs. Several sEMG acquisition setups are now available, ranging in terms of costs between a few hundred and several thousand dollars. The objective of this paper is the relative comparison of six acquisition setups on an identical hand movement classification task, in order to help the researchers to choose the proper acquisition setup for their requirements. The acquisition setups are based on four different sEMG electrodes (including Otto Bock, Delsys Trigno, Cometa Wave + Dormo ECG and two Thalmic Myo armbands) and they were used to record more than 50 hand movements from intact subjects with a standardized acquisition protocol. The relative performance of the six sEMG acquisition setups is compared on 41 identical hand movements with a standardized feature extraction and data analysis pipeline aimed at performing hand movement classification. Comparable classification results are obtained with three acquisition setups including the Delsys Trigno, the Cometa Wave and the affordable setup composed of two Myo armbands. The results suggest that practical sEMG tests can be performed even when costs are relevant (e.g. in small laboratories, developing countries or use by children). All the presented datasets can be used for offline tests and their quality can easily be compared as the data sets are publicly available.

Megane Pro: Myo-electricity, visual and gaze tracking data acquisitions to improve hand prosthetics.

During the past 60 years scientific research proposed many techniques to control robotic hand prostheses with surface electromyography (sEMG). Few of them have been implemented in commercial systems also due to limited robustness that may be improved with multimodal data. This paper presents the first acquisition setup, acquisition protocol and dataset including sEMG, eye tracking and computer vision to study robotic hand control. A data analysis on healthy controls gives a first idea of the capabilities and constraints of the acquisition procedure that will be applied to amputees in a next step. Different data sources are not fused together in the analysis. Nevertheless, the results support the use of the proposed multimodal data acquisition approach for prosthesis control. The sEMG movement classification results confirm that it is possible to classify several grasps with sEMG alone. sEMG can detect the grasp type and also small differences in the grasped object (accuracy: 95%). The simultaneous recording of eye tracking and scene camera data shows that these sensors allow performing object detection for grasp selection and that several neurocognitive parameters need to be taken into account for this. In conclusion, this work on intact subjects presents an innovative acquisition setup and protocol. The first results in terms of data analysis are promising and set the basis for future work on amputees, aiming to improve the robustness of prostheses with multimodal data.

Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data.

Control methods based on sEMG obtained promising results for hand prosthetics. Control system robustness is still often inadequate and does not allow the amputees to perform a large number of movements useful for everyday life. Only few studies analyzed the repeatability of sEMG classification of hand grasps. The main goals of this paper are to explore repeatability in sEMG data and to release a repeatability database with the recorded experiments. The data are recorded from 10 intact subjects repeating 7 grasps 12 times, twice a day for 5 days. The data are publicly available on the Ninapro web page. The analysis for the repeatability is based on the comparison of movement classification accuracy in several data acquisitions and for different subjects. The analysis is performed using mean absolute value and waveform length features and a Random Forest classifier. The accuracy obtained by training and testing on acquisitions at different times is on average 27.03% lower than training and testing on the same acquisition. The results obtained by training and testing on different acquisitions suggest that previous acquisitions can be used to train the classification algorithms. The inter-subject variability is remarkable, suggesting that specific characteristics of the subjects can affect repeatibility and sEMG classification accuracy. In conclusion, the results of this paper can contribute to develop more robust control systems for hand prostheses, while the presented data allows researchers to test repeatability in further analyses.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.