An accurate description of muscular activity plays an important role in the clinical diagnosis and rehabilitation research. The electromyography (EMG) is the most used technique to make accurate descriptions of muscular activity. The EMG is associated with the electrical changes generated by the activity of the motor neurons. Typically, to decode the muscular activation during different movements, a large number of individual motor neurons are monitored simultaneously, producing large amounts of data to be transferred and processed by the computing devices. In this paper, we follow an alternative approach that can be deployed locally on the sensor side. We propose a neuromorphic implementation of a spiking neural network (SNN) to extract spatio-temporal information of EMG signals locally and classify hand gestures with very low power consumption. We present experimental results on the input data stream using a mixed-signal analog/digital neuromorphic processor. We performed a thorough investigation on the performance of the SNN implemented on the chip, by: first, calculating PCA on the activity of the silicon neurons at the input and the hidden layers to show how the network helps in separating the samples of different classes; second, performing classification of the data using state-of-theart SVM and logistic regression methods and a hardware-friendly spike-based read-out. The traditional algorithm achieved a classification rate of 84% and 81%, respectively, and the spiking learning method achieved 74%. The power consumption of the SNN is 0.05 mW, showing the potential of this approach for ultra-low power processing.

We examined vibrotactile stimulation as a form of supplemental limb state feedback to enhance planning and ongoing control of goal-directed movements. Subjects wore a two-dimensional vibrotactile display on their nondominant arm while performing horizontal planar reaching with the dominant arm. The vibrotactile display provided feedback of hand position such that small hand displacements were more easily discriminable using vibrotactile feedback than with intrinsic proprioceptive feedback. When subjects relied solely on proprioception to capture visuospatial targets, performance was degraded by proprioceptive drift and an expansion of task space. By contrast, reach accuracy was enhanced immediately when subjects were provided vibrotactile feedback and further improved over 2 days of training. Improvements reflected resolution of proprioceptive drift, which occurred only when vibrotactile feedback was active, demonstrating that benefits of vibrotactile feedback are due, in part to its integration into the ongoing control of movement. A partial resolution of task space expansion persisted even when vibrotactile feedback was inactive, demonstrating that training with vibrotactile feedback also induced changes in movement planning. However, the benefits of vibrotactile feedback come at a cognitive cost. All subjects adopted a stereotyped strategy wherein they attempted to capture targets by moving first along one axis of the vibrotactile display and then the other. For most subjects, this inefficient approach did not resolve over two bouts of training performed on separate days, suggesting that additional training is needed to integrate vibrotactile feedback into the planning and online control of goal-directed reaching in a way that promotes smooth and efficient movement. NEW & NOTEWORTHY A two-dimensional vibrotactile display provided state (not error) feedback to enhance control of a moving limb. Subjects learned to use state feedback to perform blind reaches with accuracy and precision exceeding that attained using intrinsic proprioception alone. Feedback utilization incurred substantial cognitive cost: subjects moved first along one axis of the vibrotactile display, then the other. This stereotyped control strategy must be overcome if vibrotactile limb state feedback is to promote naturalistic limb movements.