Abstract: A system for performing spinal cord stimulation on a subject is disclosed. The system comprises one or more recording arrays, at least one stimulation array, and a processor. The recording arrays, which may be minimally invasively inserted to a target recording site of the brain, include recording electrodes having a diameter of less than about 1 mm and having a spacing of less than about 1 mm therebetween. The stimulation array, which may be minimally invasively inserted to a target stimulation site within the nervous tissue, includes stimulation electrodes. The processor is configured to receive more recorded signals from the recording arrays at the target recording site, determine an electrophysiological state of the brain based on the recorded signals, and deliver electrical stimulation to the target stimulation site through the stimulation array based on the determined electrophysiological state in order to affect the electrophysiological state.

Abstract: A medical device system that can include a medical device configured to sense data associated with the subject or receive control input, an external device communicably coupled to the medical device, a storage medium communicably coupled to the external device, and one or more communications interfaces between the medical device, the external device, and the storage medium or components thereof, wherein the one or more communications interfaces comprise an encryption protocol.

Abstract: A neural device system that can include a neural device configured to sense data associated with the subject or receive control input, an external device communicably coupled to the neural device, a storage medium communicable coupled to the external device, and one or more communications interfaces between the neural device, the external device, and the storage medium or components thereof, wherein the one or more communications interfaces comprise an encryption protocol.

Abstract: A systems and methods for calibrating a neural device using transfer learning techniques. The methods can include aggregating calibration data across a user population to define a global dataset, identifying similar data segments across the global dataset to define a task-independent training dataset, training a feature extraction model based on the task-independent training dataset to define a trained, task-independent feature extraction model, receiving the calibration data from a user calibrating the neural device, and calibrating a user-specific feature extraction model using the trained, task-independent feature extraction model and the calibration data.

Abstract: In an example method, a computing device obtains sensor data generated by one or more accelerometers and one or more gyroscopes over a time period, including an acceleration signal indicative of an acceleration measured by the one or more accelerometers over a time period, and an orientation signal indicative of an orientation measured by the one or more gyroscopes over the time period. The one or more accelerometers and the one or more gyroscopes are physically coupled to a user walking along a surface. The computing device identifies one or more portions of the sensor data based on one or more criteria, and determines characteristics regarding a gait of the user based on the one or more portions of the sensor data, including a walking speed of the user and an asymmetry of the gait of the user.

Abstract: The present disclosure relates to a system and method of detecting activity by a wheelchair user. In one aspect, a method comprises collecting motion data of a user device located on an appendage of the user; detecting, by a processor circuit, that one or more activities by the wheelchair user occurred based on the motion data; calculating, by a processor circuit, an energy expenditure by the user based the one or more activities by the wheelchair user occurred; and outputting, by a processor circuit, the energy expenditure estimation.

Abstract: The present disclosure relates to a system and method of detecting activity by a wheelchair user. In one aspect, a method comprises collecting motion data of a user device located on an appendage of the user; detecting, by a processor circuit, that one or more activities by the wheelchair user occurred based on the motion data; calculating, by a processor circuit, an energy expenditure by the user based the one or more activities by the wheelchair user occurred; and outputting, by a processor circuit, the energy expenditure estimation.

Abstract: Embodiments are disclosed for passive tracking of dyskinesia and tremor symptoms using a wearable computer. In an embodiment, a method comprises: obtaining, by one or more motion sensors of a computer attached to a user's limb, motion data; extracting, by one or more processors of the computer, one or more features from the motion data that are potentially indicative of dyskinesia or tremor; determining, by one or more processors of the computer and based on the one or more extracted features, the likelihood of dyskinesia or tremor; generating, by the one or more processors, data indicating the likelihood of dyskinesia or tremor; and outputting, by the one or more processors, the data through an output device of the computer.

Abstract: The present disclosure relates to a system and method of detecting activity by a wheelchair user. In one aspect, a method comprises collecting motion data of a user device located on an appendage of the user; detecting, by a processor circuit, that one or more activities by the wheelchair user occurred based on the motion data; calculating, by a processor circuit, an energy expenditure by the user based the one or more activities by the wheelchair user occurred; and outputting, by a processor circuit, the energy expenditure estimation.