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Background Neurosurgical interventions that require active patient feedback, such as deep brain stimulation surgery, create an opportunity to conduct cognitive or behavioral experiments during the acquisition of invasive neurophysiology. Optimal design and implementation of intraoperative behavioral experiments require consideration of stimulus presentation, time and surgical constraints. We describe the use of a modular, inexpensive system that implements a decision-making paradigm, designed to overcome challenges associated with the operative environment. New method We have created an auditory, two-alternative forced choice (2AFC) task for intraoperative use. Behavioral responses were acquired using an Arduino based single-hand held joystick controller equipped with a 3-axis accelerometer, and two button presses, capable of sampling at 2 kHz. We include designs for all task relevant code, 3D printed components, and Arduino pin-out diagram. Results We demonstrate feasibility both in and out of the operating room with behavioral results represented by three healthy control subjects and two Parkinson's disease subjects undergoing deep brain stimulator implantation. Psychometric assessment of performance indicated that the subjects could detect, interpret and respond accurately to the task stimuli using the joystick controller. We also demonstrate, using intraoperative neurophysiology recorded during the task, that the behavioral system described here allows us to examine neural correlates of human behavior. Comparison with existing methods For low cost and minimal effort, any clinical neural recording system can be adapted for intraoperative behavioral testing with our experimental setup. Conclusion Our system will enable clinicians and basic scientists to conduct intraoperative awake and behaving electrophysiologic studies in humans.

Maintaining a good state of health is most challenging and necessary for all. In this regard, carrying out regular exercise with proper moves is important. Improper exercise moves often results is a strain, injury or no progress towards the goal. It is necessary to have a trained professional for proper guidance. Smart systems are in place, to help people in tracking their exercising activity, in the form of wearable bands or mobile apps. In this paper, an exercise assist tool is proposed to distinguish proper and improper exercise moves. Accelerometers were attached to upper limbs for data collections and an Artificial Neural Network (ANN) was used to classify them based on the features extracted. The proposed system was tested on five different volunteers for three different exercise movements. It was observed that the network was able to identify which axis the movement goes wrong.