Here an inertial sensor-based monitoring system for measuring and analyzing upper limb movements is presented. The final goal is the integration of this motion-tracking device within a portable rehabilitation system for brain injury patients. A set of four inertial sensors mounted on a special garment worn by the patient provides the quaternions representing the patient upper limb’s orientation in space. A kinematic model is built to estimate 3D upper limb motion for accurate therapeutic evaluation. The human upper limb is represented as a kinematic chain of rigid bodies with three joints and six degrees of freedom. Validation of the system has been performed by co-registration of movements with a commercial optoelectronic tracking system. Successful results are shown that exhibit a high correlation among signals provided by both devices and obtained at the Institut Guttmann Neurorehabilitation Hospital.

This paper presents the design, development and first evaluation of an algorithm, named Intelligent Therapy Assistant (ITA), which automatically selects, configures and schedules rehabilitation tasks for patients with cognitive impairments after an episode of Acquired Brain Injury. The ITA is integrated in “Guttmann, Neuro Personal Trainer” (GNPT), a cognitive tele-rehabilitation platform that provides neuropsychological services.

Electrical enabling motor control (eEmc) through transcutaneous spinal cord stimulation offers promise in improving hand function. However, it is still unknown which stimulus intensity or which muscle force level could be better for this improvement. Nine healthy individuals received the following interventions: (i) eEmc intensities at 80%, 90% and 110% of abductor pollicis brevis motor threshold combined with hand training consisting in 100% handgrip strength; (ii) hand training consisting in 100% and 50% of maximal handgrip strength combined with 90% eEmc intensity. The evaluations included box and blocks test (BBT), maximal voluntary contraction (MVC), F wave persistency, F/M ratio, spinal and cortical motor evoked potentials (MEP), recruitment curves of spinal MEP and cortical MEP and short-interval intracortical inhibition. The results showed that: (i) 90% eEmc intensity increased BBT, MVC, F wave persistency, F/M ratio and cortical MEP recruitment curve; 110% eEmc intensity increased BBT, F wave persistency and cortical MEP and recruitment curve of cortical MEP; (ii) 100% handgrip strength training significantly modulated MVC, F wave persistency, F/M wave and cortical MEP recruitment curve in comparison to 50% handgrip strength. In conclusion, eEmc intensity and muscle strength during training both influence the results for neuromodulation at the cervical level.

Background: Traumatic brain injury (TBI) is a leading cause of disability worldwide. TBI is a highly heterogeneous disease, which makes it complex for effective therapeutic interventions. Cluster analysis has been extensively applied in previous research studies to identify homogeneous subgroups based on performance in neuropsychological baseline tests. Nevertheless, most analyzed samples are rarely larger than a size of 100, and different cluster analysis approaches and cluster validity indices have been scarcely compared or applied in web-based rehabilitation treatments. Objective: The aims of our study were as follows: (1) to apply state-of-the-art cluster validity indices to different cluster strategies: hierarchical, partitional, and model-based, (2) to apply combined strategies of dimensionality reduction by using principal component analysis and random forests and perform stability assessment of the final profiles, (3) to characterize the identified profiles by using demographic and clinically relevant variables, and (4) to study the external validity of the obtained clusters by considering 3 relevant aspects of TBI rehabilitation: Glasgow Coma Scale, functional independence measure, and execution of web-based cognitive tasks. Methods: This study was performed from August 2008 to July 2019. Different cluster strategies were executed with Mclust, factoextra, and cluster R packages. For combined strategies, we used the FactoMineR and random forest R packages. Stability analysis was performed with the fpc R package. Between-group comparisons for external validation were performed using 2-tailed t test, chi-square test, or Mann-Whitney U test, as appropriate. Results: We analyzed 574 adult patients with TBI (mostly severe) who were undergoing web-based rehabilitation. We identified and characterized 3 clusters with strong internal validation: (1) moderate attentional impairment and moderate dysexecutive syndrome with mild memory impairment and normal spatiotemporal perception, with almost 66% (111/170) of the patients being highly educated (P<.05); (2) severe dysexecutive syndrome with severe attentional and memory impairments and normal spatiotemporal perception, with 49.2% (153/311) of the patients being highly educated (P<.05); (3) very severe cognitive impairment, with 45.2% (42/93) of the patients being highly educated (P<.05). We externally validated them with severity of injury (P=.006) and functional independence assessments: cognitive (P<.001), motor (P<.001), and total (P<.001). We mapped 151,763 web-based cognitive rehabilitation tasks during the whole period to the 3 obtained clusters (P<.001) and confirmed the identified patterns. Stability analysis indicated that clusters 1 and 2 were respectively rated as 0.60 and 0.75; therefore, they were measuring a pattern and cluster 3 was rated as highly stable. Conclusions: Cluster analysis in web-based cognitive rehabilitation treatments enables the identification and characterization of strong response patterns to neuropsychological tests, external validation of the obtained clusters, tailoring of cognitive web-based tasks executed in the web platform to the identified profiles, thereby providing clinicians a tool for treatment personalization, and the extension of a similar approach to other medical conditions.

The goal of this study was to investigate whether real-time external urethral sphincter (EUS) EMG-controlled dorsal genital nerve (DGN) stimulation can suppress undesired detrusor bladder contractions in patients with both neurogenic detrusor overactivity (NDO) and detrusor sphincter dyssynergia (DSD). Detrusor pressure (Pdet) and EUS EMG were recorded in 12 neurogenic patients who underwent two filling cystometries. The first one was without stimulation and was intended to confirm the NDO and DSD and to set the EMG detection threshold. The second one was with real-time EMG-controlled stimulation of DGNs. Two detection methods were analyzed to detect bladder contractions. The first method was a Kurtosis-scaled root mean square (RMS) detector and was used on-line. The second was a simple RMS detector and was used off-line. Of 12 patients included, 10 patients showed both NDO and DSD. In nine of these ten patients relevant EMG concomitant to detrusor activity was detected and stimulation could suppress at least one detrusor contraction. The second filling compared to the first one showed an increase of 84% in bladder capacity (p = 0.002) and a decrease of 106% in Pdet (p = 0.002). Nine false-positive detections occurred during the ten fillings with electrical stimulation. The mean increases of both time and Pdet between stimulation and bladder contraction onsets for method 1 were 1.8 s and 4 cmH(2)O and for method 2 were 0.9 s and 2 cmH(2)O, respectively. This study shows that EUS EMG can be used in real time to detect the onset of a bladder contraction. In combination with DGN stimulation has been shown to be feasible to suppress undesired bladder contractions and in turn to increase bladder capacity in subjects with both NDO and DSD.