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Gait disorders in neurological diseases are frequently associated with spasticity. Intramuscular injection of Botulinum Toxin Type A (BTX-A) can be used to treat spasticity. Providing optimal treatment with the highest possible benefit-risk ratio is a crucial consideration. This paper presents a novel approach for predicting knee and ankle kinematics after BTX-A treatment based on pre-treatment kinematics and treatment information. The proposed method is based on a Bidirectional Long Short-Term Memory (Bi-LSTM) deep learning architecture. Our study's objective is to investigate this approach's effectiveness in accurately predicting the kinematics of each phase of the gait cycle separately after BTX-A treatment. Two deep learning models are designed to incorporate categorical medical treatment data corresponding to the injected muscles: (1) within the hidden layers of the Bi-LSTM network, (2) through a gating mechanism. Since several muscles can be injected during the same session, the proposed architectures aim to model the interactions between the different treatment combinations. In this study, we conduct a comparative analysis of our prediction results with the current state of the art. The best results are obtained with the incorporation of the gating mechanism. The average prediction root mean squared error is 2.99° (R2 = 0.85) and 2.21° (R2 = 0.84) for the knee and the ankle kinematics, respectively. Our findings indicate that our approach outperforms the existing methods, yielding a significantly improved prediction accuracy.

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This study addresses the characterization of normal gait and pathological deviations induced by neurological diseases, considering knee angular kinematics in the sagittal plane. We propose an unsupervised approach based on Dynamic Time Warping (DTW) to identify different normal gait profiles (NGPs) corresponding to real cycles representing the overall behavior of healthy subjects, instead of considering an average reference, as done in the literature. The obtained NGPs are then used to measure the deviations of pathological gait cycles from normal gait with DTW. Hierarchical Clustering is applied to stratify deviations into clusters. Results show that three NGPs are necessary to finely characterize the heterogeneity of normal gait and accurately quantify pathological deviations. In particular, we automatically identify which lower limb is affected for Hemiplegic patients and characterize the severity of motor impairment for Paraplegic patients. Concerning Tetraplegic patients, different profiles appear in terms of impairment severity. These promising results are obtained by considering the raw description of gait signals. Indeed, we have shown that normalizing signals removes the temporal properties of signals, inducing a loss of dynamic information that is crucial for accurately measuring pathological deviations. Our methodology could be exploited to quantify the impact of therapies on gait rehabilitation.

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We propose a framework for optimizing personalized treatment outcomes for patients with neurological diseases. A typical consequence of such diseases is gait disorders, partially explained by command and muscle tone problems associated with spasticity. Intramuscular injection of botulinum toxin type A is a common treatment for spasticity. According to the patient's profile, offering the optimal treatment combined with the highest possible benefit-risk ratio is important. For the prediction of knee and ankle kinematics after botulinum toxin type A (BTX-A) treatment, we propose: (1) a regression strategy based on a multi-task architecture composed of LSTM models; (2) to introduce medical treatment data (MTD) for context modeling; and (3) a gating mechanism to model treatment interaction more efficiently. The proposed models were compared with and without metadata describing treatments and with serial models. Multi-task learning (MTL) achieved the lowest root-mean-squared error (RMSE) (5.60°) for traumatic brain injury (TBI) patients on knee trajectories and the lowest RMSE (3.77°) for cerebral palsy (CP) patients on ankle trajectories, with only a difference of 5.60° between actual and predicted. Overall, the best RMSE ranged from 5.24° to 6.24° for the MTL models. To the best of our knowledge, this is the first time that MTL has been used for post-treatment gait trajectory prediction. The MTL models outperformed the serial models, particularly when introducing treatment metadata. The gating mechanism is efficient in modeling treatment interaction and improving trajectory prediction.

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The pooling layer is at the heart of every convolutional neural network (CNN) contributing to the invariance of data variation. This paper proposes a pooling method based on Zeckendorf's number series. The maximum pooling layers are replaced with Z pooling layer, which capture texels from input images, convolution layers, etc. It is shown that Z pooling properties are better adapted to segmentation tasks than other pooling functions. The method was evaluated on a traditional image segmentation task and on a dense labeling task carried out with a series of deep learning architectures in which the usual maximum pooling layers were altered to use the proposed pooling mechanism. Not only does it arbitrarily increase the receptive field in a parameterless fashion but it can better tolerate rotations since the pooling layers are independent of the geometric arrangement or sizes of the image regions. Different combinations of pooling operations produce images capable of emphasizing low/high frequencies, extract ultrametric contours, etc.

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Movement-based video games can provide engaging practice for repetitive therapeutic gestures towards improving manual ability in youth with cerebral palsy (CP). However, home-based gesture calibration and classification is needed to personalize therapy and ensure an optimal challenge point. Nineteen youth with CP controlled a video game during a 4-week home-based intervention using therapeutic hand gestures detected via electromyography and inertial sensors. The in-game calibration and classification procedure selects the most discriminating, person-specific features using random forest classification. Then, a support vector machine is trained with this feature subset for in-game interaction. The procedure uses features intended to be sensitive to signs of CP and leverages directional statistics to characterize muscle activity around the forearm. Home-based calibration showed good agreement with video verified ground truths (0.86 ± 0.11, 95%CI = 0.93-0.97). Across participants, classifier performance (F1-score) for the primary therapeutic gesture was 0.90 ± 0.05 (95%CI = 0.87-0.92) and, for the secondary gesture, 0.82 ± 0.09 (95%CI = 0.77-0.86). Features sensitive to signs of CP were significant contributors to classification and correlated to wrist extension improvement and increased practice time. This study contributes insights for classifying gestures in people with CP and demonstrates a new gesture controller to facilitate home-based therapy gaming.

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Abnormally-synchronized, high-voltage spindles (HVSs) are associated with motor deficits in 6-hydroxydopamine-lesioned parkinsonian rats. The non-stationary, spike-and-wave HVSs (5-13 Hz) represent the cardinal parkinsonian state in the local field potentials (LFPs). Although deep brain stimulation (DBS) is an effective treatment for the Parkinson's disease, continuous stimulation results in cognitive and neuropsychiatric side effects. Therefore, an adaptive stimulator able to stimulate the brain only upon the occurrence of HVSs is demanded. This paper proposes an algorithm not only able to detect the HVSs with low latency but also friendly for hardware realization of an adaptive stimulator. The algorithm is based on autoregressive modeling at interval, whose parameters are learnt online by an adaptive Kalman filter. In the LFPs containing 1131 HVS episodes from different brain regions of four parkinsonian rats, the algorithm detects all HVSs with 100% sensitivity. The algorithm also achieves higher precision (96%) and lower latency (61 ms), while requiring less computation time than the continuous wavelet transform method. As the latency is much shorter than the mean duration of an HVS episode (4.3 s), the proposed algorithm is suitable for realization of a smart neuromodulator for mitigating HVSs effectively by closed-loop DBS.

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IMPORTANCE/BACKGROUND: Movement-controlled video games have potential to promote home-based practice of therapy activities. The success of therapy gaming interventions depends on the quality of the technology used and the presence of effective support structures. AIM: This study assesses the feasibility of a novel intervention that combines a co-created gaming technology integrating evidence-based biofeedback and solution-focused coaching (SFC) strategies to support therapy engagement and efficacy at home. METHODS: Following feasibility and single-case reporting standards (CONSORT and SCRIBE), this was a non-blind, randomized, multiple-baseline, AB, design. Nineteen (19) young people with cerebral palsy (8-18 years old) completed the 4-week home-based intervention in France and Canada. Participant motivations, personalized practice goals, and relevance of the intervention to daily activities were discussed in a Solution Focused Coaching-style conversation pre-, post-intervention and during weekly check-ins. Participants controlled a video game by completing therapeutic gestures (wrist extension, pinching) detected via electromyography and inertial sensors on the forearm (Myo Armband and custom software). Process feasibility success criteria for recruitment response, completion and adherence rates, and frequency of technical issues were established a priori. Scientific feasibility, effect size estimates and variance were determined for Body Function outcome measures: active wrist extension, grip strength and Box and Blocks Test; and for Activities and Participation measures: Assisting Hand Assessment (AHA), Canadian Occupational Performance Measure (COPM) and Self-Reported Experiences of Activity Settings (SEAS). RESULTS: Recruitment response (31%) and assessment completion (84%) rates were good and 74% of participants reached self-identified practice goals. As 17% of technical issues required external support to resolve, the intervention was graded as feasible with modifications. No adverse events were reported. Moderate effects were observed in Body Function measures (active wrist extension: SMD = 1.82, 95%CI = 0.85-2.78; Grip Strength: SMD = 0.63, 95%CI = 0.65-1.91; Box and Blocks: Hedge's g = 0.58, 95%CI = -0.11-1.27) and small-moderate effects in Activities and Participation measures (AHA: Hedge's g = 0.29, 95%CI = -0.39-0.97, COPM: r = 0.60, 95%CI = 0.13-0.82, SEAS: r = 0.24, 95%CI = -0.25-0.61). CONCLUSION: A definitive RCT to investigate the effectiveness of this novel intervention is warranted. Combining SFC-style coaching with high-quality biofeedback may positively engage youth in home rehabilitation to complement traditional therapy. TRIAL REGISTRATION: ClinicalTrials.gov, U.S. National Library of Medicine: NCT03677193.

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The goal of classifier combination can be briefly stated as combining the decisions of individual classifiers to obtain a better classifier. In this paper, we propose a method based on the combination of weak rank classifiers because rankings contain more information than unique choices for a many-class problem. The problem of combining the decisions of more than one classifier with raw outputs in the form of candidate class rankings is considered and formulated as a general discrete optimization problem with an objective function based on the distance between the data and the consensus decision. This formulation uses certain performance statistics about the joint behavior of the ensemble of classifiers. Assuming that each classifier produces a ranking list of classes, an initial approach leads to a binary linear programming problem with a simple and global optimum solution. The consensus function can be considered as a mapping from a set of individual rankings to a combined ranking, leading to the most relevant decision. We also propose an information measure that quantifies the degree of consensus between the classifiers to assess the strength of the combination rule that is used. It is easy to implement and does not require any training. The main conclusion is that the classification rate is strongly improved by combining rank classifiers globally. The proposed algorithm is tested on real cytology image data to detect cervical cancer.

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Purpose: The purpose of this study is to evaluate the quality of evidence of biofeedback interventions aimed at improving motor activities in people with Cerebral Palsy (CP). Second, to describe the relationship between intervention outcomes and biofeedback characteristics. Methods: Eight databases were searched for rehabilitation interventions that provided external feedback and addressed motor activities. Two reviewers independently assessed and extracted data. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework was used to evaluate quality of evidence for outcome measures related to two International Classification of Functioning, Disability and Health (ICF) chapters. Results: Fifty-seven studies were included. There were 53 measures related Activities and Participation and 39 measures related to Body Functions. Strength of evidence was "Positive, Very-Low" due to the high proportion of non-controlled studies and heterogeneity of measures. Overall, 79% of studies and 63% of measures showed improvement post-intervention. Counter to motor learning theory recommendations, most studies provided feedback consistently and concurrently throughout the intervention regardless of the individual's desire or progress. Conclusion: Heterogeneous interventions and poor study design limit the strength of biofeedback evidence. A thoughtful biofeedback paradigm and standardized outcome toolbox can strengthen the confidence in the effect of biofeedback interventions for improving motor rehabilitation for people with CP. Implications for Rehabilitation Biofeedback can improve motor outcomes for people with Cerebral Palsy. If given too frequently, biofeedback may prevent the client from learning autonomously. Use consistent and concurrent feedback to improve simple/specific motor activities. Use terminal feedback and client-directed feedback to improve more complex/general motor activities.

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In this work, postoperative lower limb kinematics are predicted with respect to preoperative kinematics, physical examination and surgery data. Data of 115 children with cerebral palsy that have undergone single-event multilevel surgery were considered. Preoperative data dimension was reduced utilizing principal component analysis. Then, multiple linear regressions with 80% confidence intervals were performed between postoperative kinematics and bilateral preoperative kinematics, 36 physical examination variables and combinations of 9 different surgical procedures. The mean prediction errors on test vary from 4° (pelvic obliquity and hip adduction) to 10° (hip rotation and foot progression), depending on the kinematic angle. The unilateral mean sizes of the confidence intervals vary from 5° to 15°. Frontal plane angles are predicted with the lowest errors, however the same performance is achieved when considering the postoperative average signals. Sagittal plane angles are better predicted than transverse plane angles, with statistical differences with respect to the average postoperative kinematics for both plane's angles except for ankle dorsiflexion. The mean prediction errors are smaller than the variability of gait parameters in cerebral palsy. The performance of the system is independent of the preoperative state severity of the patient. Even if the system is not yet accurate enough to define a surgery plan, it shows an unbiased estimation of the most likely outcome, which can be useful for both the clinician and the patient. More patients' data are necessary for improving the precision of the model in order to predict the kinematic outcome of a large number of possible surgeries and gait patterns.

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AIMS/HYPOTHESIS: Early diagnosis of diabetic polyneuropathy (DPN) is critical for a good prognosis. We aimed to identify different groups of patients, based on the various common clinical signs and symptoms of DPN, that represent a progressive worsening of the disease before the onset of plantar ulceration or amputation. We also sought to identify the most important DPN-related variables that can discriminate between groups, thus representing the most informative variables for early detection. METHODS: In 193 diabetic patients, we assessed 16 DPN-related signs, symptoms, and foot characteristics, based on the literature and the International Consensus on the Diabetic Foot. We used multiple correspondence analysis and the Kohonen algorithm to group the variables into micro and macro-classes and to identify clusters of patients that represent different DPN conditions. RESULTS: Four distinct groups were observed. One group showed no indication of DPN. The remaining groups were characterized by a progressive loss of the vibration perception, without a worsening of symptoms or tactile perception. The 2 intermediate groups presented different aspects of DPN: one showed mostly DPN symptoms and the other showed the incipient vibration impairment, callus and crack formation, and foot arch alteration. The fourth group showed more severe foot and DPN conditions, including ulceration and amputation, absence of vibration and tactile perception (irrespective of how many compromised foot areas), and worse foot deformities and callus and crack formation. CONCLUSION: Vibration perception was more informative than tactile sensitivity in discriminating early DPN onset because its impairment was evident in more groups. Symptoms and callus and cracks did not discriminate the severity status and should be interpreted in association with other clinical variables. Reconsideration of the current screening techniques is needed to clinically determine the early onset of neuropathy using tactile perception.

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Many of the multichannel extracellular recordings of neural activity consist of attempting to sort spikes on the basis of shared characteristics with some feature detection techniques. Then spikes can be sorted into distinct clusters. There are in general two main statistical issues: firstly, spike sorting can result in well-sorted units, but by with no means one can be sure that one is dealing with single units due to the number of neurons adjacent to the recording electrode. Secondly, the waveform dimensionality is reduced in a small subset of discriminating features. This shortening dimension effort was introduced as an aid to visualization and manual clustering, but also to reduce the computational complexity in automatic classification. We introduce a metric based on common neighbourhood to introduce sparsity in the dataset and separate data into more homogeneous subgroups. The approach is particularly well suited for clustering when the individual clusters are elongated (that is nonspherical). In addition it does need not to select the number of clusters, it is very efficient to visualize clusters in a dataset, it is robust to noise, it can handle imbalanced data, and it is fully automatic and deterministic.

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This study compares muscle fiber conduction velocities estimated using surface electromyography during isometric maximal voluntary contraction in different stages of diabetic neuropathy. Eighty-five adults were studied: 16 non-diabetic individuals and 69 diabetic patients classified into four neuropathy stages, defined by a fuzzy expert system: absent (n=26), mild (n=21), moderate (n=11) and severe (n=11). Average muscle fiber conduction velocities of gastrocnemius medialis, tibialis anterior, vastus lateralis and biceps femoris were assessed using linear array electrodes, and were compared by ANOVA. Conduction velocities were significantly decreased in the moderate neuropathy group for the vastus lateralis compared to other groups (from 18% to 21% decrease), and were also decreased in all diabetic groups for the tibialis anterior (from 15% to 20% from control group). Not only the distal anatomical localization of the muscle affects the conduction velocity, but also the proportion of muscle fiber type, where the tibialis anterior with greater type I fiber proportion is affected earlier while the vastus lateralis with greater type II fiber proportion is affected in later stages of the disease. Generally, the muscles of the lower limb have different responsiveness to the effects of diabetes mellitus and show a reduction in the conduction velocity as neuropathy progresses.

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