Automation of motor dexterity assessment

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

Characterization of a raspberry Pi as the core for a low-cost multimodal EEG-fNIRS platform.

Poor understanding of brain recovery after injury, sparsity of evaluations and limited availability of healthcare services hinders the success of neurorehabilitation programs in rural communities. The availability of neuroimaging ca-pacities in remote communities can alleviate this scenario supporting neurorehabilitation programs in remote settings. This research aims at building a multimodal EEG-fNIRS neuroimaging platform deployable to rural communities to support neurorehabilitation efforts. A Raspberry Pi 4 is chosen as the CPU for the platform responsible for presenting the neurorehabilitation stimuli, acquiring, processing and storing concurrent neuroimaging records as well as the proper synchronization between the neuroimaging streams. We present here two experiments to assess the feasibility and characterization of the Raspberry Pi as the core for a multimodal EEG-fNIRS neuroimaging platform; one over controlled conditions using a combination of synthetic and real data, and another from a full test during resting state. CPU usage, RAM usage and operation temperature were measured during the tests with mean operational records below 40% for CPU cores, 13.6% for memory and 58.85 ° C for temperatures. Package loss was inexistent on synthetic data and negligible on experimental data. Current consumption can be satisfied with a 1000 mAh 5V battery. The Raspberry Pi 4 was able to cope with the required workload in conditions of operation similar to those needed to support a neurorehabilitation evaluation

Brain resuscitation in the drowning victim

Item does not contain fulltextDrowning is a leading cause of accidental death. Survivors may sustain severe neurologic morbidity. There is negligible research specific to brain injury in drowning making current clinical management non-specific to this disorder. This review represents an evidence-based consensus effort to provide recommendations for management and investigation of the drowning victim. Epidemiology, brain-oriented prehospital and intensive care, therapeutic hypothermia, neuroimaging/monitoring, biomarkers, and neuroresuscitative pharmacology are addressed. When cardiac arrest is present, chest compressions with rescue breathing are recommended due to the asphyxial insult. In the comatose patient with restoration of spontaneous circulation, hypoxemia and hyperoxemia should be avoided, hyperthermia treated, and induced hypothermia (32-34 degrees C) considered. Arterial hypotension/hypertension should be recognized and treated. Prevent hypoglycemia and treat hyperglycemia. Treat clinical seizures and consider treating non-convulsive status epilepticus. Serial neurologic examinations should be provided. Brain imaging and serial biomarker measurement may aid prognostication. Continuous electroencephalography and N20 somatosensory evoked potential monitoring may be considered. Serial biomarker measurement (e.g., neuron specific enolase) may aid prognostication. There is insufficient evidence to recommend use of any specific brain-oriented neuroresuscitative pharmacologic therapy other than that required to restore and maintain normal physiology. Following initial stabilization, victims should be transferred to centers with expertise in age-specific post-resuscitation neurocritical care. Care should be documented, reviewed, and quality improvement assessment performed. Preclinical research should focus on models of asphyxial cardiac arrest. Clinical research should focus on improved cardiopulmonary resuscitation, re-oxygenation/reperfusion strategies, therapeutic hypothermia, neuroprotection, neurorehabilitation, and consideration of drowning in advances made in treatment of other central nervous system disorders