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  1. Article ; Online: A portable system to measure knee extensor spasticity after spinal cord injury.

    De Santis, Dalia / Perez, Monica A

    Journal of neuroengineering and rehabilitation

    2024  Volume 21, Issue 1, Page(s) 50

    Abstract: Background: The pendulum test is a quantitative method used to assess knee extensor spasticity in humans with spinal cord injury (SCI). Yet, the clinical implementation of this method remains limited. The goal of our study was to develop an objective ... ...

    Abstract Background: The pendulum test is a quantitative method used to assess knee extensor spasticity in humans with spinal cord injury (SCI). Yet, the clinical implementation of this method remains limited. The goal of our study was to develop an objective and portable system to assess knee extensor spasticity during the pendulum test using inertial measurement units (IMU).
    Methods: Spasticity was quantified by measuring the first swing angle (FSA) using a 3-dimensional optical tracking system (with external markers over the iliotibial band, lateral knee epicondyle, and lateral malleolus) and two wireless IMUs (positioned over the iliotibial band and mid-part of the lower leg) as well as a clinical exam (Modified Ashworth Scale, MAS).
    Results: Measurements were taken on separate days to assess test-retest reliability and device agreement in humans with and without SCI. We found no differences between FSA values obtained with the optical tracking system and the IMU-based system in control subjects and individuals with SCI. FSA values from the IMU-based system showed excellent agreement with the optical tracking system in individuals with SCI (ICC > 0.98) and good agreement in controls (ICC > 0.82), excellent test-retest reliability across days in SCI (ICC = 0.93) and good in controls (ICC = 0.87). Notably, FSA values measured by both systems showed a strong association with MAS scores (
    Conclusions: These findings suggest that our new portable IMU-based system provides a robust and flexible alternative to a camera-based optical tracking system to quantify knee extensor spasticity following SCI.
    MeSH term(s) Humans ; Reproducibility of Results ; Lower Extremity ; Muscle Spasticity/etiology ; Muscle Spasticity/complications ; Knee ; Spinal Cord Injuries/complications
    Language English
    Publishing date 2024-04-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 2164377-5
    ISSN 1743-0003 ; 1743-0003
    ISSN (online) 1743-0003
    ISSN 1743-0003
    DOI 10.1186/s12984-024-01326-9
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  2. Article: A Framework for Optimizing Co-adaptation in Body-Machine Interfaces.

    De Santis, Dalia

    Frontiers in neurorobotics

    2021  Volume 15, Page(s) 662181

    Abstract: The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific ... ...

    Abstract The operation of a human-machine interface is increasingly often referred to as a two-learners problem, where both the human and the interface independently adapt their behavior based on shared information to improve joint performance over a specific task. Drawing inspiration from the field of body-machine interfaces, we take a different perspective and propose a framework for studying co-adaptation in scenarios where the evolution of the interface is dependent on the users' behavior and that do not require task goals to be explicitly defined. Our mathematical description of co-adaptation is built upon the assumption that the interface and the user agents co-adapt toward maximizing the interaction efficiency rather than optimizing task performance. This work describes a mathematical framework for body-machine interfaces where a naïve user interacts with an adaptive interface. The interface, modeled as a linear map from a space with high dimension (the user input) to a lower dimensional feedback, acts as an adaptive "tool" whose goal is to minimize transmission loss following an unsupervised learning procedure and has no knowledge of the task being performed by the user. The user is modeled as a non-stationary multivariate Gaussian generative process that produces a sequence of actions that is either statistically independent or correlated. Dependent data is used to model the output of an action selection module concerned with achieving some unknown goal dictated by the task. The framework assumes that in parallel to this explicit objective, the user is implicitly learning a suitable but not necessarily optimal way to interact with the interface. Implicit learning is modeled as use-dependent learning modulated by a reward-based mechanism acting on the generative distribution. Through simulation, the work quantifies how the system evolves as a function of the learning time scales when a user learns to operate a static vs. an adaptive interface. We show that this novel framework can be directly exploited to readily simulate a variety of interaction scenarios, to facilitate the exploration of the parameters that lead to optimal learning dynamics of the joint system, and to provide an empirical proof for the superiority of human-machine co-adaptation over user adaptation.
    Language English
    Publishing date 2021-04-21
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2453002-5
    ISSN 1662-5218
    ISSN 1662-5218
    DOI 10.3389/fnbot.2021.662181
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  3. Article ; Online: The Effect of Feedback Modality When Learning a Novel Wrist Sensorimotor Transformation Through a Body-Machine Interface.

    Albanese, Giulia A / Zenzeri, Jacopo / De Santis, Dalia

    IEEE ... International Conference on Rehabilitation Robotics : [proceedings

    2023  Volume 2023, Page(s) 1–6

    Abstract: Body-Machine Interfaces (BoMIs) are promising assistive and rehabilitative tools for helping individuals with impaired motor abilities regain independence. When operating a BoMI, the user has to learn a novel sensorimotor transformation between the ... ...

    Abstract Body-Machine Interfaces (BoMIs) are promising assistive and rehabilitative tools for helping individuals with impaired motor abilities regain independence. When operating a BoMI, the user has to learn a novel sensorimotor transformation between the movement of certain body parts and the output of the device. In this study, we investigated how different feedback modalities impacted learning to operate a BoMI. Forty-seven able-bodied participants learned to control the velocity of a 1D cursor using the 3D rotation of their dominant wrist to reach as many targets as possible in a given amount of time. The map was designed to maximize cursor speed for movements around a predefined axis of wrist rotation. We compared the user's performance and control efficiency under three feedback modalities: i) visual feedback of the cursor position, ii) proprioceptive feedback of the cursor position delivered by a wrist manipulandum, iii) both i) and ii). We found that visual feedback led to a greater number of targets reached than proprioceptive feedback alone. Conversely, proprioceptive feedback yielded greater alignment between the axis of rotation of the wrist and the optimal axis represented by the map. These results suggest that proprioceptive feedback may be preferable over visual feedback when information about intrinsic task components, i.e. joint configurations, is of interest as in rehabilitative interventions aiming to promote more effective learning strategies.
    MeSH term(s) Humans ; Wrist ; Feedback ; Learning ; Movement ; Wrist Joint ; Proprioception ; Psychomotor Performance
    Language English
    Publishing date 2023-11-09
    Publishing country United States
    Document type Journal Article
    ISSN 1945-7901
    ISSN (online) 1945-7901
    DOI 10.1109/ICORR58425.2023.10304784
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  4. Article ; Online: Guiding functional reorganization of motor redundancy using a body-machine interface.

    De Santis, Dalia / Mussa-Ivaldi, Ferdinando A

    Journal of neuroengineering and rehabilitation

    2020  Volume 17, Issue 1, Page(s) 61

    Abstract: Background: Body-machine interfaces map movements onto commands to external devices. Redundant motion signals derived from inertial sensors are mapped onto lower-dimensional device commands. Then, the device users face two problems, a) the structural ... ...

    Abstract Background: Body-machine interfaces map movements onto commands to external devices. Redundant motion signals derived from inertial sensors are mapped onto lower-dimensional device commands. Then, the device users face two problems, a) the structural problem of understanding the operation of the interface and b) the performance problem of controlling the external device with high efficiency. We hypothesize that these problems, while being distinct are connected in that aligning the space of body movements with the space encoded by the interface, i.e. solving the structural problem, facilitates redundancy resolution towards increasing efficiency, i.e. solving the performance problem.
    Methods: Twenty unimpaired volunteers practiced controlling the movement of a computer cursor by moving their arms. Eight signals from four inertial sensors were mapped onto the two cursor's coordinates on a screen. The mapping matrix was initialized by asking each user to perform free-form spontaneous upper-limb motions and deriving the two main principal components of the motion signals. Participants engaged in a reaching task for 18 min, followed by a tracking task. One group of 10 participants practiced with the same mapping throughout the experiment, while the other 10 with an adaptive mapping that was iteratively updated by recalculating the principal components based on ongoing movements.
    Results: Participants quickly reduced reaching time while also learning to distribute most movement variance over two dimensions. Participants with the fixed mapping distributed movement variance over a subspace that did not match the potent subspace defined by the interface map. In contrast, participant with the adaptive map reduced the difference between the two subspaces, resulting in a smaller amount of arm motions distributed over the null space of the interface map. This, in turn, enhanced movement efficiency without impairing generalization from reaching to tracking.
    Conclusions: Aligning the potent subspace encoded by the interface map to the user's movement subspace guides redundancy resolution towards increasing movement efficiency, with implications for controlling assistive devices. In contrast, in the pursuit of rehabilitative goals, results would suggest that the interface must change to drive the statistics of user's motions away from the established pattern and toward the engagement of movements to be recovered.
    Trial registration: ClinicalTrials.gov, NCT01608438, Registered 16 April 2012.
    MeSH term(s) Adult ; Female ; Humans ; Learning/physiology ; Male ; Middle Aged ; Movement/physiology ; Self-Help Devices ; User-Computer Interface ; Young Adult
    Language English
    Publishing date 2020-05-11
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1743-0003
    ISSN (online) 1743-0003
    DOI 10.1186/s12984-020-00681-7
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  5. Article ; Online: Building an adaptive interface via unsupervised tracking of latent manifolds.

    Rizzoglio, Fabio / Casadio, Maura / De Santis, Dalia / Mussa-Ivaldi, Ferdinando A

    Neural networks : the official journal of the International Neural Network Society

    2021  Volume 137, Page(s) 174–187

    Abstract: In 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 ...

    Abstract In 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.
    MeSH term(s) Brain-Computer Interfaces ; Calibration ; Computer Security/standards ; Humans ; Unsupervised Machine Learning
    Language English
    Publishing date 2021-01-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 740542-x
    ISSN 1879-2782 ; 0893-6080
    ISSN (online) 1879-2782
    ISSN 0893-6080
    DOI 10.1016/j.neunet.2021.01.009
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  6. Article ; Online: An Exploratory Multi-Session Study of Learning High-Dimensional Body-Machine Interfacing for Assistive Robot Control.

    Lee, Jongmin M / Gebrekristos, Temesgen / De Santis, Dalia / Nejati-Javaremi, Mahdieh / Gopinath, Deepak / Parikh, Biraj / Mussa-Ivaldi, Ferdinando A / Argall, Brenna D

    IEEE ... International Conference on Rehabilitation Robotics : [proceedings

    2023  Volume 2023, Page(s) 1–6

    Abstract: Individuals who suffer from severe paralysis often lose the capacity to perform fundamental body movements and everyday activities. Empowering these individuals with the ability to operate robotic arms, in high degrees-of-freedom (DoFs), can help to ... ...

    Abstract Individuals who suffer from severe paralysis often lose the capacity to perform fundamental body movements and everyday activities. Empowering these individuals with the ability to operate robotic arms, in high degrees-of-freedom (DoFs), can help to maximize both functional utility and independence. However, robot teleoperation in high DoFs currently lacks accessibility due to the challenge in capturing high-dimensional control signals from the human, especially in the face of motor impairments. Body-machine interfacing is a viable option that offers the necessary high-dimensional motion capture, and it moreover is noninvasive, affordable, and promotes movement and motor recovery. Nevertheless, to what extent body-machine interfacing is able to scale to high-DoF robot control, and whether it is feasible for humans to learn, remains an open question. In this exploratory multi-session study, we demonstrate the feasibility of human learning to operate a body-machine interface to control a complex, assistive robotic arm. We use a sensor net of four inertial measurement unit sensors, bilaterally placed on the scapulae and humeri. Ten uninjured participants are familiarized, trained, and evaluated in reaching and Activities of Daily Living tasks, using the body- machine interface. Our results suggest the manner of control space mapping (joint-space control versus task-space control), from interface to robot, plays a critical role in the evolution of human learning. Though joint-space control shows to be more intuitive initially, task-space control is found to have a greater capacity for longer-term improvement and learning.
    MeSH term(s) Humans ; Activities of Daily Living ; User-Computer Interface ; Robotics ; Movement ; Learning
    Language English
    Publishing date 2023-11-07
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1945-7901
    ISSN (online) 1945-7901
    DOI 10.1109/ICORR58425.2023.10304745
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  7. Article ; Online: A hybrid Body-Machine Interface integrating signals from muscles and motions.

    Rizzoglio, Fabio / Pierella, Camilla / De Santis, Dalia / Mussa-Ivaldi, Ferdinando / Casadio, Maura

    Journal of neural engineering

    2020  Volume 17, Issue 4, Page(s) 46004

    Abstract: Objective: Body-Machine Interfaces (BoMIs) establish a way to operate a variety of devices, allowing their users to extend the limits of their motor abilities by exploiting the redundancy of muscles and motions that remain available after spinal cord ... ...

    Abstract Objective: Body-Machine Interfaces (BoMIs) establish a way to operate a variety of devices, allowing their users to extend the limits of their motor abilities by exploiting the redundancy of muscles and motions that remain available after spinal cord injury or stroke. Here, we considered the integration of two types of signals, motion signals derived from inertial measurement units (IMUs) and muscle activities recorded with electromyography (EMG), both contributing to the operation of the BoMI.
    Approach: A direct combination of IMU and EMG signals might result in inefficient control due to the differences in their nature. Accordingly, we used a nonlinear-regression-based approach to predict IMU from EMG signals, after which the predicted and actual IMU signals were combined into a hybrid control signal. The goal of this approach was to provide users with the possibility to switch seamlessly between movement and EMG control, using the BoMI as a tool for promoting the engagement of selected muscles. We tested the interface in three control modalities, EMG-only, IMU-only and hybrid, in a cohort of 15 unimpaired participants. Participants practiced reaching movements by guiding a computer cursor over a set of targets.
    Main results: We found that the proposed hybrid control led to comparable performance to IMU-based control and significantly outperformed the EMG-only control. Results also indicated that hybrid cursor control was predominantly influenced by EMG signals.
    Significance: We concluded that combining EMG with IMU signals could be an efficient way to target muscle activations while overcoming the limitations of an EMG-only control.
    MeSH term(s) Electromyography ; Humans ; Motion ; Movement ; Muscles ; Spinal Cord Injuries
    Language English
    Publishing date 2020-07-13
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2170901-4
    ISSN 1741-2552 ; 1741-2560
    ISSN (online) 1741-2552
    ISSN 1741-2560
    DOI 10.1088/1741-2552/ab9b6c
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    Zs.A 6071: Show issues Location:
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  8. Article ; Online: A semi-automatic registration protocol to match ex-vivo high-field 7T MR images and histological slices in surgical samples from patients with drug-resistant epilepsy.

    Aquino, Domenico / Garbelli, Rita / Rossini, Laura / De Santis, Dalia / Spreafico, Roberto / d'Orio, Piergiorgio / Tassi, Laura / Padelli, Francesco

    Journal of neuroscience methods

    2021  Volume 367, Page(s) 109439

    Abstract: Background: MRI is a fundamental tool to detect brain structural anomalies and improvement in this technique has the potential to visualize subtle abnormalities currently undetected. Correlation between pre-operative MRI and histopathology is required ... ...

    Abstract Background: MRI is a fundamental tool to detect brain structural anomalies and improvement in this technique has the potential to visualize subtle abnormalities currently undetected. Correlation between pre-operative MRI and histopathology is required to validate the neurobiological basis of MRI abnormalities. However, precise MRI-histology matching is very challenging with the surgical samples. We previously developed a coregistration protocol to match the in-vivo MRI with ex-vivo MRI obtained from surgical specimens. Now, we complete the process to successfully align ex-vivo MRI data with the proper digitalized histological sections in an automatic way.
    New method: The implemented pipeline is composed by the following steps: a) image pre-processing made of MRI and histology volumes conversion and masking; b) gross rigid body alignment between MRI volume and histology virtual slides; c) rigid alignment between each MRI section and histology slice and estimate of the correlation coefficient for each step to select the MRI slice that best matches histology; d) final linear registration of the selected slices.
    Results: This method is fully automatic, except for the first masking step, fast and reliable in comparison to the manual one, as assessed using a Bland-Altman plot.
    Comparison with existing methods: The visual assessment usually employed for choosing the best fitting ex-vivo MRI slice for each stained section takes hours and requires practice. Goubran et al. (2015) proposed an iterative registration protocol but its aim and methods were different from ours. No others similar methods are reported in the literature.
    Conclusions: This protocol completes our previous pipeline. The ultimate goal will be to apply the entire process to finely investigate the relationship between clinical MRI data and histopathological features in patients with drug-resistant epilepsy.
    MeSH term(s) Drug Resistant Epilepsy/diagnostic imaging ; Drug Resistant Epilepsy/surgery ; Histological Techniques/methods ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging/methods
    Language English
    Publishing date 2021-12-13
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 282721-9
    ISSN 1872-678X ; 0165-0270
    ISSN (online) 1872-678X
    ISSN 0165-0270
    DOI 10.1016/j.jneumeth.2021.109439
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  9. Article ; Online: Transferring knowledge during dyadic interaction: The role of the expert in the learning process.

    Mireles, Edwin Johnatan Avila / De Santis, Dalia / Morasso, Pietro / Zenzeri, Jacopo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2017  Volume 2016, Page(s) 2149–2152

    Abstract: Physical interaction between man and machines is increasing the interest of the research as well as the industrial community. It is known that physical coupling between active persons can be beneficial and increase the performance of the dyad compared to ...

    Abstract Physical interaction between man and machines is increasing the interest of the research as well as the industrial community. It is known that physical coupling between active persons can be beneficial and increase the performance of the dyad compared to an individual. However, the factors that may result in performance benefits are still poorly understood. The aim of this work is to investigate how the different initial skill levels of the interacting partners influence the learning of a stabilization task. Twelve subjects, divided in two groups, trained in couples in a joint stabilization task. In the first group the couples were composed of two naive, while in the second a naive was trained together with an expert. Results show that training with an expert results in the greatest performance in the joint task. However, this benefit is not transferred to the individual when performing the same task bimanually.
    MeSH term(s) Humans ; Interpersonal Relations ; Learning ; Models, Theoretical
    Language English
    Publishing date 2017-03-08
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC.2016.7591154
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  10. Article ; Online: Distinctive electro-clinical, neuroimaging and histopathological features of temporal encephaloceles associated to epilepsy.

    Di Giacomo, Roberta / Burini, Alessandra / Visani, Elisa / Doniselli, Fabio Martino / Cuccarini, Valeria / Garbelli, Rita / Marucci, Gianluca / De Santis, Dalia / Didato, Giuseppe / Deleo, Francesco / Pastori, Chiara / Stabile, Andrea / Villani, Flavio / Rizzi, Michele / Girardi, Luca / de Curtis, Marco

    Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology

    2023  Volume 44, Issue 12, Page(s) 4451–4463

    Abstract: Objective: Encephaloceles (ENCs) may cause clinical complications, including drug-resistant epilepsy that can be cured with epilepsy surgery.: Methods: We describe clinical, diagnostic, and neuropathological findings of 12 patients with temporal ENC ... ...

    Abstract Objective: Encephaloceles (ENCs) may cause clinical complications, including drug-resistant epilepsy that can be cured with epilepsy surgery.
    Methods: We describe clinical, diagnostic, and neuropathological findings of 12 patients with temporal ENC and epilepsy evaluated for surgery and compare them with a control group of 26 temporal lobe epilepsy (TLE) patients.
    Results: Six patients had unilateral and 6 bilateral temporal ENCs. Compared to TLEs, ENCs showed i) later epilepsy onset, ii) higher prevalence of psychiatric comorbidities, iii) no history of febrile convulsions, and iv) ictal semiology differences. Seven patients had MRI signs of gliosis, and 9 of intracranial hypertension. Interictal EEG analysis in ENCs demonstrated significant differences with controls: prominent activity in the beta/gamma frequency bands in frontal regions, interictal short sequences of low-voltage fast activity, and less frequent and more localized interictal epileptiform discharges. Ictal EEG patterns analyzed in 9 ENCs showed delayed and slower contralateral spread compared to TLEs. All ENCs that underwent surgery (7 lobectomies and 1 lesionectomy) are in Engel class I. Neuropathological examination revealed 4 patterns: herniated brain fragments, focal layer I distortion, white matter septa extending into the cortex, and altered gyral profile.
    Conclusions and significance: The described peculiarities might help clinicians to suspect the presence of largely underdiagnosed ENCs.
    MeSH term(s) Humans ; Electroencephalography/methods ; Encephalocele/complications ; Encephalocele/diagnostic imaging ; Epilepsy/diagnostic imaging ; Epilepsy/etiology ; Epilepsy, Temporal Lobe/diagnostic imaging ; Epilepsy, Temporal Lobe/surgery ; Neuroimaging ; Magnetic Resonance Imaging/methods
    Language English
    Publishing date 2023-07-17
    Publishing country Italy
    Document type Journal Article
    ZDB-ID 2016546-8
    ISSN 1590-3478 ; 1590-1874
    ISSN (online) 1590-3478
    ISSN 1590-1874
    DOI 10.1007/s10072-023-06939-x
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