96 results found
Borrego G, Morán AL, Meza V, et al., 2021, Key factors that influence the UX of a dual-player game for the cognitive stimulation and motor rehabilitation of older adults, Universal Access in the Information Society, Vol: 20, Pages: 767-783, ISSN: 1615-5289
In this work, the results of usability and user experience (UX) evaluation of a serious video game for the cognitive stimulation and motor rehabilitation of the upper limb of the elderly are presented. The serious game includes features that allow (1) performing cooperative therapy exercises between two patients, (2) remote session configuration therapy, and (3) monitoring/analyzing the sessions’ results by the therapist. An evaluation of the game with 16 older adults provides evidence about how the tool is perceived by participants, who embraced it as stimulating, useful, usable and even fun, and which impacts in therapy compliance and acceptability by the elderly. In addition, through an in depth analysis of the participants’ performance and observed emotions, as well as their self-report, we determined which engagement attributes are present in the game. Finally, we also found evidence that suggests that the participants’ skill level and the game difficulty level rather than just a good performance on the game are key factors that influence their enjoyment and frustration.
Sharma C, Singh H, Orihuela-Espina F, et al., 2021, Visual gaze patterns reveal surgeons' ability to identify risk of bile duct injury during laparoscopic cholecystectomy, HPB, Vol: 23, Pages: 715-722, ISSN: 1365-182X
BACKGROUND: Bile duct injury is a serious surgical complication of laparoscopic cholecystectomy. The aim of this study was to identify distinct visual gaze patterns associated with the prompt detection of bile duct injury risk during laparoscopic cholecystectomy. METHODS: Twenty-nine participants viewed a laparoscopic cholecystectomy that led to a serious bile duct injury ('BDI video') and an uneventful procedure ('control video') and reported when an error was perceived that could result in bile duct injury. Outcome parameters include fixation sequences on anatomical structures and eye tracking metrics. Surgeons were stratified into two groups based on performance and compared. RESULTS: The 'early detector' group displayed reduced common bile duct dwell time in the first half of the BDI video, as well as increased cystic duct dwell time and Calot's triangle glances count during Calot's triangle dissection in the control video. Machine learning based classification of fixation sequences demonstrated clear separability between early and late detector groups. CONCLUSION: There are discernible differences in gaze patterns associated with early recognition of impending bile duct injury. The results could be transitioned into real time and used as an intraoperative early warning system and in an educational setting to improve surgical safety and performance.
Wu K-C, Sunwoo J, Sheriff F, et al., 2021, Validation of diffuse correlation spectroscopy measures of critical closing pressure against transcranial Doppler ultrasound in stroke patients, Journal of Biomedical Optics, Vol: 26, Pages: 036008-1-036008-14, ISSN: 1083-3668
SIGNIFICANCE: Intracranial pressure (ICP), variability in perfusion, and resulting ischemia are leading causes of secondary brain injury in patients treated in the neurointensive care unit. Continuous, accurate monitoring of cerebral blood flow (CBF) and ICP guide intervention and ultimately reduce morbidity and mortality. Currently, only invasive tools are used to monitor patients at high risk for intracranial hypertension. AIM: Diffuse correlation spectroscopy (DCS), a noninvasive near-infrared optical technique, is emerging as a possible method for continuous monitoring of CBF and critical closing pressure (CrCP or zero-flow pressure), a parameter directly related to ICP. APPROACH: We optimized DCS hardware and algorithms for the quantification of CrCP. Toward its clinical translation, we validated the DCS estimates of cerebral blood flow index (CBFi) and CrCP in ischemic stroke patients with respect to simultaneously acquired transcranial Doppler ultrasound (TCD) cerebral blood flow velocity (CBFV) and CrCP. RESULTS: We found CrCP derived from DCS and TCD were highly linearly correlated (ipsilateral R2 = 0.77, p = 9 × 10 - 7; contralateral R2 = 0.83, p = 7 × 10 - 8). We found weaker correlations between CBFi and CBFV (ipsilateral R2 = 0.25, p = 0.03; contralateral R2 = 0.48, p = 1 × 10 - 3) probably due to the different vasculature measured. CONCLUSION: Our results suggest DCS is a valid alternative to TCD for continuous monitoring of CrCP.
Rivas JJ, Lara MDC, Castrejon L, et al., 2021, Multi-label and multimodal classifier for affective states recognition in virtual rehabilitation, IEEE Transactions on Affective Computing, Pages: 1-1, ISSN: 1949-3045
Computational systems that process multiple affective states may benefit from explicitly considering the interaction between the states to enhance their recognition performance. This work proposes the combination of a multi-label classifier, Circular Classifier Chain (CCC), with a multimodal classifier, Fusion using a Semi-Naive Bayesian classifier (FSNBC), to include explicitly the dependencies between multiple affective states during the automatic recognition process. This combination of classifiers is applied to a virtual rehabilitation context of post-stroke patients. We collected data from post-stroke patients, which include finger pressure, hand movements, and facial expressions during ten longitudinal sessions. Videos of the sessions were labelled by clinicians to recognize four states: tiredness, anxiety, pain, and engagement. Each state was modelled by the FSNBC receiving the information of finger pressure, hand movements, and facial expressions. The four FSNBCs were linked in the CCC to exploit the dependency relationships between the states. The convergence of CCC was reached by 5 iterations at most for all the patients. Results (ROC AUC)) of CCC with the FSNBC are over 0.940±0.045 ( mean±std.deviation ) for the four states. Relationships of mutual exclusion between engagement and all the other states and co-occurrences between pain and anxiety were detected and discussed.
Hernandez-Franco J, Orihuela-Espina F, Palafox L, et al., 2021, Remote central effects of botulinum toxin type A as adjuvant to intense occupational therapy in the early stage of stroke: A Type II fMRI randomised controlled trial, TOXINS Conference on Basic Science and Clinical Aspects of Botulinum and other Neurotoxins, Publisher: Elsevier, Pages: S33-S33, ISSN: 0041-0101
Introduction: Improvements in motor function following interventions incorporating botulinum toxin type A (BTX-A) remain controversial, with existing studies yielding contrasting results.1-3 The mechanisms underlying BTX-A remote central effects are still under investigation. It is hypothesized that the toxin administration strategy may play a role in producing such differing outcomes. We tested a strategy based on modulating muscle synergies.Aim: The aim of the study was to investigate the clinical and remote central effects of an occupational therapy intervention combined with adjunctive BTX-A compared to the same occupational therapy without the adjuvant application of the toxin.Methods: A two-group, parallel, pre-post, randomized controlled trial was performed. The clinical effects of occupational therapy when performed following BTX-A injections to disinhibit finger flexors (n=5) was compared to those of an equal dose of occupational therapy alone (n=6). Motor dexterity and function were assessed using the Fugl-Meyer Scale, Motor Index, Arm Activity Measure, 9-Hole Peg Test, and Box and Block Test, and differences were analysed using ANCOVA. Brain activity was examined using functional magnetic resonance imaging (fMRI), and between-group differences were analysed using contrast statistical parametric mapping.Results: Both groups started in statistically similar conditions. Both treatments provided significant clinical improvements compared to baseline. The total differences in change score on the Fugl-Meyer Scale and Motor Index were larger, though not significantly, in the toxin-treated group than in the control group (Figure). When the toxin is administered, activity in the brain is more localised and appears more in the right hemisphere in subjects in the toxin-treated group and more in the left in those in the control group.Conclusions: Functional improvements were observed in the toxin-treated group, but the effect size compared to the control group was to
Ávila-Sansores S-M, Rodríguez-Gómez G, Tachtsidis I, et al., 2020, Interpolated functional manifold for functional near-infrared spectroscopy analysis at group level, Neurophotonics, Vol: 7, Pages: 045009-045009, ISSN: 2329-423X
Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist,but groupwise explorative analysis with classical solutions is often cumbersome. Manifoldbased solutions excel at data exploration, but there are infinite surfaces crossing the observationscloud of points.Aim: We aim to provide a systematic choice of surface for a manifold-based analysis of connectivity at group level with small surface interpolation error.Approach: This research introduces interpolated functional manifold (IFM). IFM builds a manifold from reconstructed changes in concentrations of oxygenated ΔcHbO2 and reduced ΔcHbRhemoglobin species by means of radial basis functions (RBF). We evaluate the root mean squareerror (RMSE) associated to four families of RBF. We validated our model against psychophysiological interactions (PPI) analysis using the Jaccard index (JI). We demonstrate the usability inan experimental dataset of surgical neuroergonomics.Results: Lowest interpolation RMSE was 1.26e − 4 1.32e − 8 for ΔcHbO2 [A.U.] and4.30e − 7 2.50e − 13 [A.U.] for ΔcHbR. Agreement with classical group analysis was JI ¼0.89 0.01 for ΔcHbO2. Agreement with PPI analysis was JI ¼ 0.83 0.07 for ΔcHbO2 andJI ¼ 0.77 0.06 for ΔcHbR. IFM successfully decoded group differences [ANOVA: ΔcHbO2:Fð2;117Þ ¼ 3.07; p < 0.05; ΔcHbR: Fð2;117Þ ¼ 3.35; p < 0.05].Conclusions: IFM provides a pragmatic solution to the problem of choosing the manifold associated to a cloud of points, facilitating the use of manifold-based solutions for the group analysisof fNIRS datasets.
Casillas-Figueroa R, Morán AL, Meza-Kubo V, et al., 2020, ReminiScentia: shaping olfactory interaction in a personal space for multisensory stimulation therapy, Personal and Ubiquitous Computing, ISSN: 1617-4909
Recently, multimodal interfaces are incorporating smell as an additional means of interaction. Devices called olfactory displays have been designed to improve applications with various objectives, such as notifying or alerting through scents, increasing immersion in virtual or augmented reality applications, or learning and enhancement of mental functions. Based on the potential of olfactory memory to evoke memories and emotions to benefit health and well-being, we propose ReminiScentia as an olfactory display to generate and deliver scents. This work presents an evaluation of the effectiveness of ReminiScentia in evoking brain responses similar to those generated by manually delivered scents. To achieve this, we monitored the hemodynamic responses during manual and ReminiScentia olfactory stimulation over the prefrontal cortex (PFC) by using a functional near-infrared spectroscopy (fNIRS) device in 33 healthy subjects. Among the results, it was found that when ReminiScentia was used to deliver the olfactory stimuli, there is no statistically significant difference in the magnitude of concentration changes of OxyHb in the PFC between manual deliver and ReminiScentia (Wilcoxon: p > 0.05). The effectiveness of the use of ReminiScentia has allowed us not only its application for the evocation of memories in a multisensory therapy but also to propose an olfactory interaction design space where olfactory stimuli are used to provide feedback or instructions in multisensory stimulation activities that could support the training of higher mental functions such as memory and learning in patients with cognitive disabilities.
Rivas JJ, Orihuela-Espina F, Palafox L, et al., 2020, Unobtrusive inference of affective states in virtual rehabilitation from upper limb motions: a feasibility study, IEEE Transactions on Affective Computing, Vol: 11, Pages: 470-481, ISSN: 1949-3045
Virtual rehabilitation environments may afford greater patient personalization if they could harness the patient's affective state. Four states: anxiety, pain, engagement and tiredness (either physical or psychological), were hypothesized to be inferable from observable metrics of hand location and gripping strength -relevant for rehabilitation-. Contributions are; (a) multiresolution classifier built from Semi-Naïve Bayesian classifiers, and (b) establishing predictive relations for the considered states from the motor proxies capitalizing on the proposed classifier with recognition levels sufficient for exploitation. 3D hand locations and gripping strength streams were recorded from 5 post-stroke patients whilst undergoing motor rehabilitation therapy administered through virtual rehabilitation along 10 sessions over 4 weeks. Features from the streams characterized the motor dynamics, while spontaneous manifestations of the states were labelled from concomitant videos by experts for supervised classification. The new classifier was compared against baseline support vector machine (SVM) and random forest (RF) with all three exhibiting comparable performances. Inference of the aforementioned states departing from chosen motor surrogates appears feasible, expediting increased personalization of virtual motor neurorehabilitation therapies.
Sunwoo J, Nair V, Steele T, et al., 2020, Assessment of cerebral autoregulation in extremely low gestational age newborns using diffuse correlation spectroscopy
We use diffuse correlation spectroscopy to safely quantify cerebral blood flow response to spontaneous fluctuations in autonomic and respiratory activities to help characterize the elevated risk of intraventricular hemorrhage in extremely premature newborns.
Joel Rivas J, Orihuela-Espina F, Enrique Sucar L, et al., 2019, Automatic recognition of multiple affective states in virtual rehabilitation by exploiting the dependency relationships, 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Publisher: IEEE, Pages: 655-661, ISSN: 2156-8103
The automatic recognition of multiple affective states can be enhanced if the underpinning computational models explicitly consider the interactions between the states. This work proposes a computational model that incorporates the dependencies between four states (tiredness, anxiety, pain, and engagement)known to appear in virtual rehabilitation sessions of post-stroke patients, to improve the automatic recognition of the patients' states. A dataset of five stroke patients which includes their fingers' pressure (PRE), hand movements (MOV)and facial expressions (FAE)during ten sessions of virtual rehabilitation was used. Our computational proposal uses the Semi-Naive Bayesian classifier (SNBC)as base classifier in a multiresolution approach to create a multimodal model with the three sensors (PRE, MOV, and FAE)with late fusion using SNBC (FSNB classifier). There is a FSNB classifier for each state, and they are linked in a circular classifier chain (CCC)to exploit the dependency relationships between the states. Results of CCC are over 90% of ROC AUC for the four states. Relationships of mutual exclusion between engagement and all the other states and some co-occurrences between pain and anxiety for the five patients were detected. Virtual rehabilitation platforms that incorporate the automatic recognition of multiple patient's states could leverage intelligent and empathic interactions to promote adherence to rehabilitation exercises.
Modi HN, Singh H, Fiorentino F, et al., 2019, Association of residents' neural signatures with stress resilience during surgery, JAMA Surgery, Vol: 154, ISSN: 2168-6254
Importance: Intraoperative stressors may compound cognitive load, prompting performance decline and threatening patient safety. However, not all surgeons cope equally well with stress, and the disparity between performance stability and decline under high cognitive demand may be characterized by differences in activation within brain areas associated with attention and concentration such as the prefrontal cortex (PFC). Objective: To compare PFC activation between surgeons demonstrating stable performance under temporal stress with those exhibiting stress-related performance decline. Design, Setting, and Participants: Cohort study conducted from July 2015 to September 2016 at the Imperial College Healthcare National Health Service Trust, England. One hundred two surgical residents (postgraduate year 1 and greater) were invited to participate, of which 33 agreed to partake. Exposures: Participants performed a laparoscopic suturing task under 2 conditions: self-paced (SP; without time-per-knot restrictions), and time pressure (TP; 2-minute per knot time restriction). Main Outcomes and Measures: A composite deterioration score was computed based on between-condition differences in task performance metrics (task progression score [arbitrary units], error score [millimeters], leak volume [milliliters], and knot tensile strength [newtons]). Based on the composite score, quartiles were computed reflecting performance stability (quartile 1 [Q1]) and decline (quartile 4 [Q4]). Changes in PFC oxygenated hemoglobin concentration (HbO2) measured at 24 different locations using functional near-infrared spectroscopy were compared between Q1 and Q4. Secondary outcomes included subjective workload (Surgical Task Load Index) and heart rate. Results: Of the 33 participants, the median age was 33 years, the range was 29 to 56 years, and 27 were men (82%). The Q1 residents demonstrated task-induced increases in HbO2 across the bilateral ventrolateral PFC (VLPFC) and right dorsolateral P
Wu K, Farzam P, Sheriff F, et al., 2019, Monitoring cerebral blood flow and critical closing pressure in stroke patients, 29th International Symposium on Cerebral Blood Flow, Metabolism and Function / 14th International Conference on Quantification of Brain Function with PET (BRAIN and BRAIN Pet), Publisher: SAGE PUBLICATIONS INC, Pages: 258-258, ISSN: 0271-678X
Modi H, Singh H, Fiorentino F, et al., 2019, Neural signatures of resident resilience, JAMA Surgery, ISSN: 2168-6254
Importance: Intraoperative stressors may compound cognitive load, prompting performance decline and threatening patient safety. However, not all surgeons cope equally well with stress, and the disparity between performance stability and decline under high cognitive demand may be characterized by differences in activation within brain areas associated with attention and concentration such as the prefrontal cortex (PFC).Objective: To compare PFC activation between surgeons demonstrating stable performance under temporal stress with those exhibiting stress-related performance decline. The a priori hypothesis being that under temporal demand sustained prefrontal “activation(s)” reflect performance stability, whereas performance decline is manifest as “deactivation(s)”.Design: Cohort study conducted from July 2015 to September 2016. Setting: Single center (Imperial College Healthcare NHS Trust, United Kingdom). Participants: 102 surgical residents (PGY1 and above) were invited to participate, of which 33 agreed to partake (median age [range]: 33 [29-56] years, 27 [82%] males).Exposure: Subjects performed a laparoscopic suturing task under two conditions: ‘self-paced’ (SP; without time per knot restrictions), and ‘time pressure’ (TP; two-minute per knot time restriction). Main Outcomes and Measures: A composite deterioration score was computed based on between-condition differences in task performance metrics [(task progression score (au), error score (mm), leak volume (ml) and knot tensile strength (N)]. Based on the composite score, quartiles were computed reflecting performance stability (Q1) and decline (Q4). Changes in PFC oxygenated haemoglobin concentration (HbO2) measured at 24 different locations using functional near-infrared spectroscopy were compared between Q1 and Q4. Secondary outcomes included subjective workload (Surgical Task Load Index) and heart rate. Results: Q1 residents demonstrated task-induced incr
Joel Rivas J, Orihuela-Espina F, Enrique Sucar L, 2019, Recognition of affective states in virtual rehabilitation using late fusion with semi-naive Bayesian classifier, 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Publisher: Association for Computing Machinery, Pages: 308-313, ISSN: 2153-1633
Virtual rehabilitation platforms may tailor the rehabilitation tasks to the patients' needs if they could recognize the patient's affective state. Affective states recognition systems can enhance their performance if they receive data coming from different sensors of human behaviour. In this work, we propose a late Fusion using Semi-Naive Bayesian classifier (FSNB) as a multimodal affective states recognition system to infer four states: tiredness, anxiety, pain, and motivation, from observable metrics of fingers pressure, hand movements, and facial expressions of post-stroke patients. Data streams were recorded from 5 post-stroke patients while they attended virtual rehabilitation therapies along 10 sessions over 4 weeks, manifesting the aforementioned states spontaneously. Recognition rates of the FSNB classifier were over 90% (with a standard deviation of around ± 0.06) of AUC for the four states. These results represent contributions for enhancing the development of affective states recognition systems in virtual rehabilitation.
Alejandro Hernandez-Contreras D, Peregrina-Barreto H, De Jesus Rangel-Magdaleno J, et al., 2019, Statistical approximation of plantar temperature distribution on diabetic subjects based on beta mixture model, IEEE Access, Vol: 7, Pages: 28383-28391, ISSN: 2169-3536
A change in plantar temperature distribution can be an indicator of tissue damage, inflammation, or peripheral vascular abnormalities associated with diabetic foot. Despite the efforts to detect these abnormalities through infrared thermography, there are still several problems to be addressed, especially to detect abnormalities on each foot separately. In this paper, a characterization of the plantar temperature distribution based on a probabilistic approach is proposed. The objective is to detect temperature variations on each foot eluding contralateral comparison. A beta mixture model with four components approximates the plantar temperature distributions of diabetic and non-diabetic subjects. Each component represents an area of the plantar region: toes; metatarsal heads; arch; and heel. The approximation was applied to 60 temperature distributions of non-diabetic subjects and 220 of diabetic subjects. The results suggest that it is possible to characterize distribution in terms of the mean of its beta components.
Lami M, Singh H, Dilley JH, et al., 2018, Gaze patterns hold key to unlocking successful search strategies and increasing polyp detection rate in colonoscopy, Endoscopy, Vol: 50, Pages: 701-707, ISSN: 1438-8812
BACKGROUND: The adenoma detection rate (ADR) is an important quality indicator in colonoscopy. The aim of this study was to evaluate the changes in visual gaze patterns (VGPs) with increasing polyp detection rate (PDR), a surrogate marker of ADR. METHODS: 18 endoscopists participated in the study. VGPs were measured using eye-tracking technology during the withdrawal phase of colonoscopy. VGPs were characterized using two analyses - screen and anatomy. Eye-tracking parameters were used to characterize performance, which was further substantiated using hidden Markov model (HMM) analysis. RESULTS: Subjects with higher PDRs spent more time viewing the outer ring of the 3 × 3 grid for both analyses (screen-based: r = 0.56, P = 0.02; anatomy: r = 0.62, P < 0.01). Fixation distribution to the "bottom U" of the screen in screen-based analysis was positively correlated with PDR (r = 0.62, P = 0.01). HMM demarcated the VGPs into three PDR groups. CONCLUSION: This study defined distinct VGPs that are associated with expert behavior. These data may allow introduction of visual gaze training within structured training programs, and have implications for adoption in higher-level assessment.
Montero-Hernandez S, Orihuela-Espina F, Enrique Sucar L, et al., 2018, Estimating functional connectivity symmetry between oxy- and deoxy-haemoglobin: implications for fNIRS connectivity analysis, Algorithms, Vol: 11, ISSN: 1999-4893
Functional Near InfraRed Spectroscopy (fNIRS) connectivity analysis is often performed using the measured oxy-haemoglobin (HbO2) signal, while the deoxy-haemoglobin (HHb) is largely ignored. The in-common information of the connectivity networks of both HbO2 and HHb is not regularly reported, or worse, assumed to be similar. Here we describe a methodology that allows the estimation of the symmetry between the functional connectivity (FC) networks of HbO2 and HHb and propose a differential symmetry index (DSI) indicative of the in-common physiological information. Our hypothesis is that the symmetry between FC networks associated with HbO2 and HHb is above what should be expected from random networks. FC analysis was done in fNIRS data collected from six freely-moving healthy volunteers over 16 locations on the prefrontal cortex during a real-world task in an out-of-the-lab environment. In addition, systemic data including breathing rate (BR) and heart rate (HR) were also synchronously collected and used within the FC analysis. FC networks for HbO2 and HHb were established independently using a Bayesian networks analysis. The DSI between both haemoglobin (Hb) networks with and without systemic influence was calculated. The relationship between the symmetry of HbO2 and HHb networks, including the segregational and integrational characteristics of the networks (modularity and global efficiency respectively) were further described. Consideration of systemic information increases the path lengths of the connectivity networks by 3%. Sparse networks exhibited higher asymmetry than dense networks. Importantly, our experimental connectivity networks symmetry between HbO2 and HHb departs from random (t-test: t(509) = 26.39, p < 0.0001). The DSI distribution suggests a threshold of 0.2 to decide whether both HbO2 and HHb FC networks ought to be studied. For sparse FC networks, analysis of both haemoglobin species is strongly recommended. Our DSI can provide a quantifiable g
Herrera-Vega J, Orihuela-Espina F, Ibarguengoytia PH, et al., 2018, A local multiscale probabilistic graphical model for data validation and reconstruction, and its application in industry, Engineering Applications of Artificial Intelligence, Vol: 70, Pages: 1-15, ISSN: 0952-1976
The detection and subsequent reconstruction of incongruent data in time series by means of observation of statistically related information is a recurrent issue in data validation. Unlike outliers, incongruent observations are not necessarily confined to the extremes of the data distribution. Instead, these rogue observations are unlikely values in the light of statistically related information. This paper proposes a multiresolution Bayesian network model for the detection of rogue values and posterior reconstruction of the erroneous sample for non-stationary time-series. Our method builds local Bayesian Network models that best fit to segments of data in order to achieve a finer discretization and hence improve data reconstruction. Our local multiscale approach is compared against its single-scale global predecessor (assumed as our gold standard) in the predictive power and of this, both error detection capabilities and error reconstruction capabilities are assessed. This parameterization and verification of the model are evaluated over three synthetic data source topologies. The virtues of the algorithm are then further tested in real data from the steel industry where the aforementioned problem characteristics are met but for which the ground truth is unknown. The proposed local multiscale approach was found to dealt better with increasing complexities in data topologies.
Orihuela-Espina F, Sucar LE, 2018, Adaptation and customization in virtual rehabilitation, Virtual and Augmented Reality: Concepts, Methodologies, Tools, and Applications, Pages: 826-849, ISBN: 9781522554691
Background. Adaptation and customization are two related but distinct concepts that are central to virtual rehabilitation if this motor therapy modality is to succeed in alleviating the demand for expert supervision. These two elements of the therapy are required to exploit the flexibility of virtual environments to enhance motor training and boost therapy outcome. Aim. The chapter provides a non-systematic overview of the state of the art regarding the evolving manipulation of virtual rehabilitation environments to optimize therapy outcome manifested through customization and adaptation mechanisms. Methods. Both concepts will be defined, aspects guiding their implementation reviewed, and available literature suggesting different solutions discussed. We present "Gesture Therapy", a platform realizing our contributions to the field and we present results of the adaptation techniques integrated into it. Less explored additional dimensions such as liability and privacy issues affecting their implementation are briefly discussed. Results. Solutions to implement decision-making on how to manipulate the environment are varied. They range from predefined system configurations to sophisticated artificial intelligence (AI) models. Challenge maintenance and feedback personalization is the most common driving force for their incorporation to virtual rehabilitation platforms. Conclusions. Customization and adaptation are the main mechanisms responsible for the full exploitation of the potential of virtual rehabilitation environments, and the potential benefits are worth pursuing. Despite encouraging evidence of the many solutions proposed thus far in literature, none has yet proven to substantially alter the therapy outcome. In consequence, research is still on going to equip virtual rehabilitation solutions with efficacious tailoring elements.
Soto-Perez de Celis E, Abraham Baez-Bagattela J, Lira-Huerta E, et al., 2018, Sensor-based mobile system for the promotion and real-time monitoring of physical activity, Salud Pública de Mexico, Vol: 60, Pages: 119-120, ISSN: 0036-3634
Joel Rivas J, Palafox L, Hernandez-Franco J, et al., 2018, Automatic Recognition of Pain, Anxiety, Engagement and Tiredness for Virtual Rehabilitation from Stroke: A Marginalization Approach, 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), Publisher: IEEE, Pages: 159-164, ISSN: 2156-8103
Virtual rehabilitation taps affective computing to personalize therapy. States of anxiety, pain and engagement (affective) and tiredness (physical or psychological) were studied to be inferable from metrics of 3D hand location-proxy of hand movement- and fingers' pressure relevant for upper limb motor recovery. Features from the data streams characterized the motor dynamics of 2 stroke patients attending 10 sessions of motor virtual rehabilitation. Experts tagged states manifestations from videos. We aid classification contributing with a marginalization mechanism whereby absent input is reconstructed. With the hand movement information absent, marginalization statistically outperformed a base model where such input is ignored. Marginalized classification performance was (Area below ROC curve: μ ± σ) 0.880 ± 0.173 and 0.738 ± 0.177 for each patient. Marginalization aid classification sustaining performance under input failure or permitting different sensing settings.
Orihuela-Espina F, Leff DR, James DRC, et al., 2018, Imperial College near infrared spectroscopy neuroimaging analysis framework., Neurophotonics, Vol: 5, ISSN: 2329-423X
This paper describes the Imperial College near infrared spectroscopy neuroimaging analysis (ICNNA) software tool for functional near infrared spectroscopy neuroimaging data. ICNNA is a MATLAB-based object-oriented framework encompassing an application programming interface and a graphical user interface. ICNNA incorporates reconstruction based on the modified Beer-Lambert law and basic processing and data validation capabilities. Emphasis is placed on the full experiment rather than individual neuroimages as the central element of analysis. The software offers three types of analyses including classical statistical methods based on comparison of changes in relative concentrations of hemoglobin between the task and baseline periods, graph theory-based metrics of connectivity and, distinctively, an analysis approach based on manifold embedding. This paper presents the different capabilities of ICNNA in its current version.
Herrera-Vega J, Montero-Hernandez S, Tachtsidis I, et al., 2017, Modelling and validation of diffuse reflectance of the adult human head for fNIRS: scalp sub-layers definition, 13th International Conference on Medical Information Processing and Analysis, Publisher: Society of Photo-Optical Instrumentation Engineers (SPIE), ISSN: 0277-786X
Accurate estimation of brain haemodynamics parameters such as cerebral blood flow and volume as well as oxygen consumption i.e. metabolic rate of oxygen, with funcional near infrared spectroscopy (fNIRS) requires precise characterization of light propagation through head tissues. An anatomically realistic forward model of the human adult head with unprecedented detailed specification of the 5 scalp sublayers to account for blood irrigation in the connective tissue layer is introduced. The full model consists of 9 layers, accounts for optical properties ranging from 750nm to 950nm and has a voxel size of 0.5mm. The whole model is validated comparing the predicted remitted spectra, using Monte Carlo simulations of radiation propagation with 108 photons, against continuous wave (CW) broadband fNIRS experimental data. As the true oxy- and deoxy-hemoglobin concentrations during acquisition are unknown, a genetic algorithm searched for the vector of parameters that generates a modelled spectrum that optimally fits the experimental spectrum. Differences between experimental and model predicted spectra was quantified using the Root mean square error (RMSE). RMSE was 0.071 ± 0.004, 0.108 ± 0.018 and 0.235±0.015 at 1, 2 and 3cm interoptode distance respectively. The parameter vector of absolute concentrations of haemoglobin species in scalp and cortex retrieved with the genetic algorithm was within histologically plausible ranges. The new model capability to estimate the contribution of the scalp blood flow shall permit incorporating this information to the regularization of the inverse problem for a cleaner reconstruction of brain hemodynamics.
Heyer P, Castrejon LR, Orihuela-Espina F, et al., 2017, Automation of motor dexterity assessment, International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE, Pages: 521-526, ISSN: 1945-7898
Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and with loose restrictions on sensing technologies is presented. The system consists of two main elements: 1) A data representation that abstracts the low level information obtained from a variety of sensors, into a highly separable low dimensionality encoding employing t-distributed Stochastic Neighbourhood Embedding, and, 2) central to this communication, a multi-label classifier that boosts classification rates by exploiting the fact that the classes corresponding to the individual exercises are naturally organized as a network. Depending on the targeted therapeutic movement class labels i.e. exercises scores, are highly correlated-patients who perform well in one, tends to perform well in related exercises-; and critically no node can be used as proxy of others - an exercise does not encode the information of other exercises. Over data from a cohort of 20 patients, the novel classifier outperforms classical Naive Bayes, random forest and variants of support vector machines (ANOVA: p <; 0.001). The novel multi-label classification strategy fulfills an automatic system for motor dexterity assessment, with implications for lessening therapist's workloads, reducing healthcare costs and providing support for home-based virtual rehabilitation and telerehabilitation alternatives.
Herrera-Vega J, Treviño-Palacios CG, Orihuela-Espina F, 2017, Neuroimaging with functional near infrared spectroscopy: From formation to interpretation, Infrared Physics and Technology, Vol: 85, Pages: 225-237, ISSN: 1350-4495
Functional Near Infrared Spectroscopy (fNIRS) is gaining momentum as a functional neuroimaging modality to investigate the cerebral hemodynamics subsequent to neural metabolism. As other neuroimaging modalities, it is neuroscience's tool to understand brain systems functions at behaviour and cognitive levels. To extract useful knowledge from functional neuroimages it is critical to understand the series of transformations applied during the process of the information retrieval and how they bound the interpretation. This process starts with the irradiation of the head tissues with infrared light to obtain the raw neuroimage and proceeds with computational and statistical analysis revealing hidden associations between pixels intensities and neural activity encoded to end up with the explanation of some particular aspect regarding brain function.To comprehend the overall process involved in fNIRS there is extensive literature addressing each individual step separately. This paper overviews the complete transformation sequence through image formation, reconstruction and analysis to provide an insight of the final functional interpretation.
Modi HN, SIngh H, Orihuela-Espina F, et al., 2017, Temporal stress in the operating room: brain engagement promotes "coping" and disengagement prompts "choking", Annals of Surgery, Vol: 267, Pages: 683-691, ISSN: 1528-1140
Objective:To investigate the impact of time pressure (TP) on prefrontalactivation and technical performance in surgical residents during a laparo-scopic suturing task.Background:Neural mechanisms enabling surgeons to maintain perform-ance and cope with operative stressors are unclear. The prefrontal cortex(PFC) is implicated due to its role in attention, concentration, and perform-ance monitoring.Methods:A total of 33 residents [Postgraduate Year (PGY)1 – 2¼15,PGY3– 4¼8, and PGY5¼10] performed a laparoscopic suturing taskunder ‘‘self-paced’’ (SP) and ‘‘TP’’ conditions (TP¼maximum 2 minutes perknot). Subjective workload was quantified using the Surgical Task LoadIndex. PFC activation was inferred using optical neuroimaging. Technicalskill was assessed using progression scores (au), error scores (mm), leakvolumes (mL), and knot tensile strengths (N).Results:TP led to greater perceived workload amongst all residents (meanSurgical Task Load Index score SD: PGY1 – 2: SP¼160.3 24.8 vs TP¼202.1 45.4,P<0.001; PGY3 – 4: SP¼123.0 52.0 vs TP¼172.5 43.1,P<0.01; PGY5: SP¼105.8 55.3 vs TP¼159.1 63.1,P<0.05).Amongst PGY1– 2 and PGY3– 4, deterioration in task progression, errorscores and knot tensile strength (P<0.05), and diminished PFC activationwas observed under TP. In PGY5, TP resulted in inferior task progression anderror scores (P<0.05), but preservation of knot tensile strength. Furthermore,PGY5 exhibited less attenuation of PFC activation under TP, and greateractivation than either PGY1 – 2 or PGY3 – 4 under both experimental con-ditions (P<0.05).Conclusions:Senior residents cope better with temporal demands and exhibitgreater technical performance stability under pressure, possibly due to
Leff DR, Yongue G, Vlaev I, et al., 2017, "Contemplating the next maneuver": functional neuroimaging reveals intraoperative decision-making strategies, Annals of Surgery, Vol: 265, Pages: 320-330, ISSN: 1528-1140
OBJECTIVE: To investigate differences in the quality, confidence, and consistency of intraoperative surgical decision making (DM) and using functional neuroimaging expose decision systems that operators use. SUMMARY BACKGROUND DATA: Novices are hypothesized to use conscious analysis (effortful DM) leading to activation across the dorsolateral prefrontal cortex, whereas experts are expected to use unconscious automation (habitual DM) in which decisions are recognition-primed and prefrontal cortex independent. METHODS: A total of 22 subjects (10 medical student novices, 7 residents, and 5 attendings) reviewed simulated laparoscopic cholecystectomy videos, determined the next safest operative maneuver upon video termination (10 s), and reported decision confidence. Video paradigms either declared ("primed") or withheld ("unprimed") the next operative maneuver. Simultaneously, changes in cortical oxygenated hemoglobin and deoxygenated hemoglobin inferring prefrontal activation were recorded using Optical Topography. Decision confidence, consistency (primed vs unprimed), and quality (script concordance) were assessed. RESULTS: Attendings and residents were significantly more certain (P < 0.001), and decision quality was superior (script concordance: attendings = 90%, residents = 78.3%, and novices = 53.3%). Decision consistency was significantly superior in experts (P < 0.001) and residents (P < 0.05) than novices (P = 0.183). During unprimed DM, novices showed significant activation of the dorsolateral prefrontal cortex, whereas this activation pattern was not observed among residents and attendings. During primed DM, significant activation was not observed in any group. CONCLUSIONS: Expert DM is characterized by improved quality, consistency, and confidence. The findings imply attendings use a habitual decision system, whereas novices use an effortful approach under uncertainty. In the presence of operative cues (primes), novices disengage
Hernandez-Contreras D, Peregrina-Barreto H, Rangel-Magdaleno J, et al., 2017, Measuring Changes in the Plantar Temperature Distribution in Diabetic Patients, IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Publisher: IEEE, Pages: 272-277
Morales-Vargas E, Reyes-García CA, Peregrina-Barreto H, et al., 2017, Facial expression recognition with fuzzy explainable models, Pages: 51-54
The performance of current algorithms of facial expressions recognition are still insufficient for certain applications such as facial rehabilitation. We aim at alleviating some current limitations of these algorithms by exploiting explainable fuzzy models over sequences of frontal face images. In this work, facial expressions are characterized in terms of action units. Fuzzy models maintain a semantic relation between the facial muscle appearance and the fuzzily associated facial expression. First, heuristic guided affine transformations align facial landmarks of the neutral and target expression. Second, features are extracted describing face movements in terms of changes in orientation (angle and magnitude) of distinctive facial areas. Third, the full featured representation is embedded into a compact one by means of pooling. Finally, a Sugeno-type adaptive neuro fuzzy inference system is used for each action unit to generate a description of the movements in the face that identifies the facial expression present in an image sequence. The proposed model discriminates facial expressions with mean accuracy of 89.04±0.91% with a maximum accuracy of 91.41±28%. Further, distinctly to current solutions the model can also describe why is reaching such decision. The current solution brings application in facial rehabilitation a step closer.
Heyer P, Orihuela-Espina F, Castrejon LR, et al., 2017, Sensor Abstracted Extremity Representation for Automatic Fugl-Meyer Assessment, International 360 Degree Summit on Applications for Future Internet (AFI360), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 152-163, ISSN: 1867-8211
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