Biomedical subjects

Andreas Inmann

Publications and source records attributed to Andreas Inmann.

3 recordsLinked to original sources

PubMed2004

Functional evaluation of natural sensory feedback incorporated in a hand grasp neuroprosthesis.

We investigated whether automatic control of a hand grasp neuroprosthesis by means of signals from natural sensors in the skin of the index finger can mimic the natural control of grasp force in an important task of daily living, namely eating. We designed a simulated eating task with the same ratio of rest and activity as was found on average in a video analysis of three meals consumed in a social environment. An instrumented fork measured grasp force as well as the force in the long axis and perpendicular to the long axis at the tip of the fork. The simulated eating task was performed by a tetraplegic volunteer using a hand grasp neuroprosthesis both with and without use of feedback from the natural sensors. Further, 10 able-bodied volunteers performed the task with the same (lateral) grasp as the tetraplegic volunteer to obtain measures for improving the control strategy of the hand grasp neuroprosthesis. We have shown that a hand grasp neuroprosthesis incorporating natural sensory feedback can to some extent mimic the natural application of grasp force on a fork during simulated eating. The mean grasp force during active phases was higher than the mean grasp force during inactive phases. The mean grasp force applied during a simulated eating task was reduced by using the system with sensory feedback compared to using the system without sensory feedback.

Activities of Daily Living↗

PubMed2004

Implementation of natural sensory feedback in a portable control system for a hand grasp neuroprosthesis.

This paper presents the design and implementation of the first generation of a portable system for a hand grasp neuroprosthesis that is controlled by means of signals from natural sensors in the skin of the index finger. To reduce development time and costs, we based our design on readily available, standardised modules such as a 486DX100 compatible CPU, a data acquisition board, a flash disk storage unit, and a high-efficiency DC/DC switch-mode power supply. Additionally, we designed and built a telemeter to supply an implanted muscle stimulator with power and control data. The signal from the natural sensors was recorded with a cuff electrode implanted around the palmar digital nerve innervating the radial aspect of the index finger. For amplification of the recorded nerve signal, we added an external low-noise nerve signal amplifier. For pre-processing of the recorded nerve signal, an optimised band-pass filter was used. A data-recording unit allowed storage and off-line analysis of the stimulator command and the recorded nerve signal. The portable system was used by a tetraplegic volunteer to test the feasibility of including natural sensors in a hand grasp neuroprosthesis for activities of daily living. The flexibility of the presented system allows rapid prototyping of experimental FES hand grasp systems intended for portable use.

Activities of Daily Living↗

PubMed2003

Biopotentials as command and feedback signals in functional electrical stimulation systems.

Today Functional Electrical Stimulation (FES) is available as a clinical tool in muscle activation used for picking up objects, for standing and walking, for controlling bladder emptying, and for breathing. Despite substantial progress in development and new knowledge, many challenges remain to be resolved to provide a more efficient functionality of FES systems. The most important task of these challenges is to improve control of the activated muscles through open loop or feedback systems. Command and feedback signals can be extracted from biopotentials recorded from muscles (Electromyogram, EMG), nerves (Electroneurogram, ENG), and the brain (Electroencephalogram (EEG) or individual cells). This paper reviews work in which EMG, ENG, and EEG signals in humans have been used as command and feedback signals in systems using electrical stimulation of motor nerves to restore movements after an injury to the Central Nervous System (CNS). It is concluded that the technology is ready to push for more substantial clinical FES investigations in applying muscle and nerve signals. Brain-computer interface systems hold great prospects, but require further development of faster and clinically more acceptable technologies.

Action Potentials↗