Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume 'motor noise' leads to force variability and its 'signal dependence' (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by 'motor noise.' Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.

Complex contact manipulation tasks can be decomposed into sequences of motor primitives. Individual primitives often end with a distinct contact state, such as inserting a screwdriver tip into a screw head or loosening it through twisting. To achieve robust execution, the robot should be able to verify that the primitive's goal has been reached as well as disambiguate it from erroneous contact states. In this paper, we introduce and evaluate a framework to autonomously construct manipulation graphs from manipulation demonstrations. Our manipulation graphs include sequences of motor primitives for performing a manipulation task as well as corresponding contact state information. The sensory models for the contact states allow the robot to verify the goal of each motor primitive as well as detect erroneous contact changes. The proposed framework was experimentally evaluated on grasping, unscrewing, and insertion tasks on a Barrett arm and hand equipped with two BioTacs. The results of our experiments indicate that the learned manipulation graphs achieve more robust manipulation executions by confirming sensory goals as well as discovering and detecting novel failure modes.

In order to endow robots with human-like tactile sensory abilities, they must be provided with tactile sensors and intelligent algorithms to select and control useful exploratory movements and interpret data from all available sensors. Current robotic systems do not possess such sensors or algorithms. In this study we integrate multimodal tactile sensing (force, vibration and temperature) from the BioTac® with a Shadow Dexterous Hand and program the robot to make exploratory movements similar to those humans make when identifying objects by their compliance, texture, and thermal properties. Signal processing strategies were developed to provide measures of these perceptual properties. When identifying an object, exploratory movements are intelligently selected using a process we have previously developed called Bayesian exploration [1], whereby exploratory movements that provide the most disambiguation between likely candidates of objects are automatically selected. The exploration algorithm was augmented with reinforcement learning whereby its internal representations of objects evolved according to its cumulative experience with them. This allowed the algorithm to compensate for drift in the performance of the anthropomorphic robot hand and the ambient conditions of testing, improving accuracy while reducing the number of exploratory movements required to identify an object. The robot correctly identified 10 different objects on 99 out of 100 presentations.

Tactile sensations make grasping fragile objects a simple and unchallenging task for the human hand. Prosthetic hands lack these sensory capabilities and their users frequently struggle with such tasks. To address this problem, the benefits of compliant fingertips with contact-detection reflexes were assessed in one prosthesis user when grasping fragile objects (eggshells, foam packing peanuts, crackers, and soft clay). A commercially available myoelectric prosthetic hand was modified to include the compliant BioTac® tactile sensors (SynTouch, LLC), which have previously been demonstrated to be more sensitive to contact than the human fingertip. Upon sensing contact during hand closure on an object, the gain of the operator's EMG command signals to the prosthesis' motor was reduced to prevent excessive closing forces, a behavior similar to an inhibitory reflex. This allowed the prosthetic hand to quickly react to the presence of the object and permitted the operator to handle fragile objects with ease and without the usual dependency on visual feedback. The time required to grasp and move a set of fragile objects with this modified prosthesis was compared to the subject's usual prosthetic hand. The contact detection method demonstrated both utility and reliability through faster completion times and reduced variance in the times to complete these trials.

The nervous system is a one-trick pony, using general- purpose neurons with the same basic signal transduction, transmission and integration mechanisms to handle essentially all information processing needs in the body: sensation and perception, posture and movement, autonomic and visceral function, memory and learning. Over the past fifty years, scientists and engineers have developed many different interfaces between neurons and electronic instrumentation in order to study how individual subsystems work and to fix some of them when they malfunction (e.g. pacemakers, cochlear implants, deep brain stimulators, etc.). While the various interfaces and their applications may look different, they are all based on strikingly similar, fundamental principles of biophysics, electrochemistry and information theory, and enabled by similar microfabrication and microelectronic technologies. Neural control is gradually converging on principles of design and best practices that can and should give rise to engineering standards and interchangeable components for recurring functions such as bioelectric recording and stimulation, transmission of power and data, and physical packaging and user interfaces. As such general tools become available, the clinical applications will be limited only by our understanding of the underlying pathologies, which are often best studied by those same tools. This virtuous circle consists of accessible technology enabling basic science enabling clinical applications generating business success motivating yet more technology.

Patients with transhumeral amputations and C5/C6 quadriplegia may be able to use voluntary shoulder motion as command signals for powered prostheses and functional electrical stimulation, respectively. Spatiotemporal synergies exist between the shoulder and elbow joints for goal-oriented reaching movements as performed by able-bodied subjects. We are using a multi-layer perceptron neural network to discover and embody these synergies. Such a network could be used as a high-level controller that could predict the desired distal arm joint kinematics from the voluntary movements of the shoulder joint of an able-bodied subject. We evaluated this for a task that involved reaching to 16 targets in a horizontal plane. After reaching reasonable offline prediction accuracy for our neural networks, we then deployed the best network to make real-time predictions of the elbow angles and examined its performance on both inter- and intra-subject trials. Finally, we extended the model to utilize the five degrees-of-freedom at the shoulder to control the five degrees-of-freedom required for a prosthetic arm and hand to reach and grasp variously oriented objects in the extrapersonal workspace. Such a system, although very simple, was readily controllable for a reach and grasp task presented to the subject in a virtual reality environment.

C5/C6 tetraplegic patients and transhumeral amputees may be able to use voluntary shoulder motion as command signals for a functional electrical stimulation (FES) system or a transhumeral prosthesis. Such prostheses require, at the most basic level, the control of endpoint position in three dimensions, hand orientation, and grasp. Spatiotemporal synergies exist between the proximal and distal arm joints for goal-oriented reaching movements as performed by able-bodied subjects. To fit these synergies, we utilized three-layer artificial neural networks. These networks could be used as a means for obtaining user intent information during reaching movements. We conducted reaching experiments in which subjects reached to and grasped a handle in a three-dimensional gantry. In our previous work, the three rotational angles at the shoulder were used to predict elbow flexion/extension angle during reaches on a two-dimensional plane. In this paper, we extend this model to include the two translational movements at the shoulder as inputs and an additional output of forearm pronation/supination. Counterintuitively, as the complexity of the task and the complexity of the neural network architecture increased, the performance also improved.