Withdrawal reflexes in the upper limb adapt to arm posture and stimulus location.

INTRODUCTION: Withdrawal reflexes in the leg adapt in a context-appropriate manner to remove the limb from noxious stimuli, but the extent to which withdrawal reflexes adapt in the arm remains unknown. METHODS: We examined the adaptability of withdrawal reflexes in response to nociceptive stimuli applied in different arm postures and to different digits. Reflexes were elicited at rest, and kinetic and electromyographic responses were recorded under isometric conditions, thereby allowing motorneuron pool excitability to be controlled. RESULTS: Endpoint force changed from a posterior-lateral direction in a flexed posture to predominantly a posterior direction in a more extended posture [change in force angle (mean ± standard deviation) 35.6 ± 5.0°], and the force direction changed similarly with digit I stimulation compared with digit V (change = 22.9 ± 2.9°). CONCLUSIONS: The withdrawal reflex in the human upper limb adapts in a functionally relevant manner when elicited at rest.

Assessing upper extremity motor function in practice of virtual activities of daily living.

A study was conducted to investigate the criterion validity of measures of upper extremity (UE) motor function derived during practice of virtual activities of daily living (ADLs). Fourteen hemiparetic stroke patients employed a Virtual Occupational Therapy Assistant (VOTA), consisting of a high-fidelity virtual world and a Kinect™ sensor, in four sessions of approximately one hour in duration. An unscented Kalman Filter-based human motion tracking algorithm estimated UE joint kinematics in real-time during performance of virtual ADL activities, enabling both animation of the user's avatar and automated generation of metrics related to speed and smoothness of motion. These metrics, aggregated over discrete sub-task elements during performance of virtual ADLs, were compared to scores from an established assessment of UE motor performance, the Wolf Motor Function Test (WMFT). Spearman's rank correlation analysis indicates a moderate correlation between VOTA-derived metrics and the time-based WMFT assessments, supporting the criterion validity of VOTA measures as a means of tracking patient progress during an UE rehabilitation program that includes practice of virtual ADLs.

Optimal input selection for neural machine interfaces predicting multiple non-explicit outputs.

This study implemented a novel algorithm that optimally selects inputs for neural machine interface (NMI) devices intended to control multiple outputs and evaluated its performance on systems lacking explicit output. NMIs often incorporate signals from multiple physiological sources and provide predictions for multidimensional control, leading to multiple-input multiple-output systems. Further, NMIs often are used with subjects who have motor disabilities and thus lack explicit motor outputs. Our algorithm was tested on simulated multiple-input multiple-output systems and on electromyogram and kinematic data collected from healthy subjects performing arm reaches. Effects of output noise in simulated systems indicated that the algorithm could be useful for systems with poor estimates of the output states, as is true for systems lacking explicit motor output. To test efficacy on physiological data, selection was performed using inputs from one subject and outputs from a different subject. Selection was effective for these cases, again indicating that this algorithm will be useful for predictions where there is no motor output, as often is the case for disabled subjects. Further, prediction results generalized for different movement types not used for estimation. These results demonstrate the efficacy of this algorithm for the development of neural machine interfaces.