1,720,975 research outputs found
The Body-Machine Interface: A new perspective on an old theme
No full textBody-machine interfaces establish a way to interact with a variety of devices, allowing their users to extend the limits of their performance. Recent advances in this field, ranging from computer interfaces to bionic limbs, have had important consequences for people with movement disorders. The authors provide an overview of the basic concepts underlying the body-machine interface with special emphasis on their use for rehabilitation and for operating assistive devices. They outline the steps involved in building such an interface and highlight the critical role of body-machine interfaces in addressing theoretical issues in motor control as well as their utility in movement rehabilitation
Synergic practice with a body-machine interface: implications for individual and collective motor learning
No full textObjective. Body-machine interfaces (BoMIs) translate human body movements into commands for controlling external devices, such as computer cursors. This process allows researchers to study the development and refinement of inverse models, which generate motor commands necessary for achieving desired movements. Traditionally, motor learning has focused on solo practice, but recent research has shifted towards exploring dyadic tasks, where two individuals practice together. Within dyadic tasks, synergic practice—where partners collaborate toward a shared goal—has shown promise in enhancing performance and reducing stress. However, the impact of contributions of each partner within synergic practice on individual and collective learning remains underexplored. This study aims to (i) investigate how different levels of contribution during synergic practice affect both individual and collective motor learning, and (ii) assess the impact of these contribution levels on individual performance when returning to solo practice. Approach. Forty naïve participants underwent individual practice, dyadic synergic practice, and a final round of individual practice using a BoMI to control a cursor. Participants were classified as high or low contributors based on their participation in the cursor trajectory during dyadic practice. We analyzed how these contribution levels influenced performance, motor strategies, and internal models during and after the dyadic phase. Main results. During dyadic practice, high contributors maintained motor strategies similar to their initial solo performance, while low contributors showed significant deviations. After returning to solo practice, high contributors retained better task performance, whereas low contributors initially regressed but quickly improved with additional practice, eventually matching high contributors’ performance levels. Significance. This understanding holds practical implications for optimizing dyadic practice. Our study sheds light on the influence of synergic practice on subsequent individual motor performance, contributing to a clearer understanding of its advantages and limitations for optimal implementation
Building an adaptive interface via unsupervised tracking of latent manifolds
No full textIn human–machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human–machine joint performance. Importantly, the proposed approach avoids interrupting the operation of the device and it neither relies on information about the state of the task, nor on the existence of a stable neural or movement manifold, allowing it to be applied in the earliest stages of interface operation, when the formation of new neural strategies is still on-going. In order to more directly test the performance of our algorithm, we defined the autoencoder latent space as the control space of a body–machine interface. After an initial offline parameter tuning, we evaluated the performance of the adaptive interface versus that of a static decoder in approximating the evolving low-dimensional manifold of users simultaneously learning to perform reaching movements within the latent space. Results show that the adaptive approach increased the representational efficiency of the interface decoder. Concurrently, it significantly improved users’ task-related performance, indicating that the development of a more accurate internal model is encouraged by the online co-adaptation process
Kinematic networks. A distributed model for representing and regularizing motor redundancy
No full textLinear and Non-linear Dimensionality-Reduction Techniques on Full Hand Kinematics
No full textThe purpose of this study was to find a parsimonious representation of hand kinematics data that could facilitate prosthetic hand control. Principal Component Analysis (PCA) and a non-linear Autoencoder Network (nAEN) were compared in their effectiveness at capturing the essential characteristics of a wide spectrum of hand gestures and actions. Performance of the two methods was compared on (a) the ability to accurately reconstruct hand kinematic data from a latent manifold of reduced dimension, (b) variance distribution across latent dimensions, and (c) the separability of hand movements in compressed and reconstructed representations derived using a linear classifier. The nAEN exhibited higher performance than PCA in its ability to more accurately reconstruct hand kinematic data from a latent manifold of reduced dimension. Whereas, for two dimensions in the latent manifold, PCA was able to account for 78% of input data variance, nAEN accounted for 94%. In addition, the nAEN latent manifold was spanned by coordinates with more uniform share of signal variance compared to PCA. Lastly, the nAEN was able to produce a manifold of more separable movements than PCA, as different tasks, when reconstructed, were more distinguishable by a linear classifier, SoftMax regression. It is concluded that non-linear dimensionality reduction may offer a more effective platform than linear methods to control prosthetic hands
Recovery of distal arm movements in spinal cord injured patients with a body-machine interface: a proof-of-concept study
No full textBackground: The recovery of upper limb mobility and functions is essential for people with cervical spinal cord injuries (cSCI) to maximize independence in daily activities and ensure a successful return to normality. The rehabilitative path should include a thorough neuromotor evaluation and personalized treatments aimed at recovering motor functions. Body-machine interfaces (BoMI) have been proven to be capable of harnessing residual joint motions to control objects like computer cursors and virtual or physical wheelchairs and to promote motor recovery. However, their therapeutic application has still been limited to shoulder movements. Here, we expanded the use of BoMI to promote the whole arm’s mobility, with a special focus on elbow movements. We also developed an instrumented evaluation test and a set of kinematic indicators for assessing residual abilities and recovery. Methods: Five inpatient cSCI subjects (four acute, one chronic) participated in a BoMI treatment complementary to their standard rehabilitative routine. The subjects wore a BoMI with sensors placed on both proximal and distal arm districts and practiced for 5 weeks. The BoMI was programmed to promote symmetry between right and left arms use and the forearms’ mobility while playing games. To evaluate the effectiveness of the treatment, the subjects’ kinematics were recorded while performing an evaluation test that involved functional bilateral arms movements, before, at the end, and three months after training. Results: At the end of the training, all subjects learned to efficiently use the interface despite being compelled by it to engage their most impaired movements. The subjects completed the training with bilateral symmetry in body recruitment, already present at the end of the familiarization, and they increased the forearm activity. The instrumental evaluation confirmed this. The elbow motion’s angular amplitude improved for all subjects, and other kinematic parameters showed a trend towards the normality range. Conclusion: The outcomes are preliminary evidence supporting the efficacy of the proposed BoMI as a rehabilitation tool to be considered for clinical practice. It also suggests an instrumental evaluation protocol and a set of indicators to assess and evaluate motor impairment and recovery in cSCI
Static Versus Dynamic Decoding Algorithms in a Non-Invasive Body-Machine Interface
No full textIn this study, we consider a non-invasive body-machine interface that captures body motions still available to people with spinal cord injury (SCI) and maps them into a set of signals for controlling a computer user interface while engaging in a sustained level of mobility and exercise. We compare the effectiveness of two decoding algorithms that transform a high-dimensional body-signal vector into a lower dimensional control vector on six subjects with high-level SCI and eight controls. One algorithm is based on a static map from current body signals to the current value of the control vector set through principal component analysis (PCA), the other on dynamic mapping a segment of body signals to the value and the temporal derivatives of the control vector set through a Kalman filter. SCI and control participants performed straighter and smoother cursor movements with the Kalman algorithm during center-out reaching, but their movements were faster and more precise when using PCA. All participants were able to use the BMI's continuous, two-dimensional control to type on a virtual keyboard and play pong, and performance with both algorithms was comparable. However, seven of eight control participants preferred PCA as their method of virtual wheelchair control. The unsupervised PCA algorithm was easier to train and seemed sufficient to achieve a higher degree of learnability and perceived ease of use
Testing the Ability to Represent and Control a Contact Force
No full textWhile the concept of force is solidly grounded in Newtonian mechanics, it is not known if it is also represented in a consistent way by our brains as they control interactions of the hand with external objects. For example, a force of 10Â N applied against different springs will cause different amounts of displacement. Are we able to represent 10 N in a way that is independent of the effects of applying such force to different objects? Here, we developed a simple method to address this question by engaging subjects in a task whose success depends critically upon the ability to exert a fixed force against different simulated springs. Our preliminary findings indicate that while this task is difficult, subjects learn after some training to exert the same force against different springs and in different directions
A body-machine interface for training selective pelvis movements in stroke survivors: A pilot study
No full textThe body-machine interfaces (BMIs) map the subjects' movements into the low dimensional control space of external devices to reach assistive and/or rehabilitative goals. This work is a first proof of concept of this kind of BMI as tool for rehabilitation after stroke. We designed an exercise to improve the control of selective movements of the pelvis in stroke survivors, increasing the ability to decouple the motion in the sagittal and frontal planes and decreasing compensatory adjustments at the shoulder girdle. A Kinect sensor recorded the movements of the subjects. Subjects played different games by controlling the vertical and horizontal motion of a cursor on a screen with respectively the lateral tilt and the ante/retroversion of their pelvis. We monitored also the degrees of freedom not directly involved in cursor control, thus subjects could complete the task only with a correct posture. Our preliminary results highlight significant improvement not only in cursor control, but also in the Trunk Impairment Scale (TIS) and in the Five Times Sit to Stand Test (5xSST)
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