L. Donald Gilmore

Learn More
This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed(More)
Automatic tracking of movement disorders in patients with Parkinson's disease (PD) is dependent on the ability of machine learning algorithms to resolve the complex and unpredictable characteristics of wearable sensor data. The challenge reflects the variety of movement disorders that fluctuate throughout the day which can be confounded by voluntary(More)
The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and(More)
We investigated the influence of inter-electrode spacing on the degree of crosstalk contamination in surface electromyographic (sEMG) signals in the tibialis anterior (target muscle), generated by the triceps surae (crosstalk muscle), using bar and disk electrode arrays. The degree of crosstalk contamination was assessed for voluntary constant-force(More)
The authors previously reported speaker-dependent automatic speech recognition accuracy for isolated words using eleven surface-electromyographic (sEMG) sensors in fixed recording locations on the face and neck. The original array of sensors was chosen to ensure ample coverage of the muscle groups known to contribute to articulation during speech(More)
De Luca, Carlo J., Alexander Adam, Robert Wotiz, L. Donald Gilmore, and S. Hamid Nawab. Decomposition of surface EMG signals. J Neurophysiol 96: 1646–1657, 2006; doi:10.1152/jn.00009.2006. This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential(More)
This study compared the performance of surface electromyographic (sEMG) sensors for different detection conditions affecting the electro-mechanical stability between the sensor and its contact with the skin. These comparisons were made to gain a better understanding of how specific characteristics of sensor design and use may alter the ability of sEMG(More)
An electrode unit capable of detecting voluntarily elicited nerve signals for prolonged periods of time has been developed and tested. The electrode unit has been implanted around the distal ends of severed sciatic and peroneal nerves in rabbits. This electrode may prove suitable for implantation in human amputees for the purpose of recording neural control(More)
Parallel isolated word corpora were collected from healthy speakers and individuals with speech impairment due to stroke or cerebral palsy. Surface electromyographic (sEMG) signals were collected for both vocalized and mouthed speech production modes. Pioneering work on disordered speech recognition using the acoustic signal, the sEMG signals, and their(More)
Six recording electrode units were implanted around the severed sciatic nerves of rabbits immediately after an axotomy was performed. Voluntary and involuntary motor neuroelectric signals (including individual action potentials) were recorded from the surface of the severed nerve for as long as 142 days after implantation, the average duration being 64(More)