Determination of ankle muscle power in normal gait using an EMG-to-force processing approach.

The purpose of this study was to determine the contribution of individual ankle muscles to the net ankle power and to examine each muscle's role in propulsion or support of the body during normal, self-selected-speed walking. An EMG-to-force processing (EFP) model was developed which scaled muscle tendon unit force output to gait EMG, with that muscle's power output being the product of muscle force and contraction velocity. Net EFP power was determined by summing individual ankle muscle power. Net ankle power was also calculated for these subjects via inverse dynamics. Closeness of fit of the power curves of the two methods was used to validate the model. The curves were highly correlated (r(2)=.91), thus the model was deconstructed to analyze the power contribution and role of each ankle muscle during normal gait. Key findings were that the plantar flexors control tibial rotation in single support, and act to propel the entire limb into swing phase. The dorsiflexors provide positive power for swing phase foot clearance, negative power to control early stance phase foot placement, and a second positive power burst to actively advance the tibia in the transition from double to single support. Co-contraction of agonists and antagonists was limited to only a small percentage of the gait cycle.

An EMG-to-force processing approach for determining ankle muscle forces during normal human gait.

Muscle forces move our limbs. These forces must be estimated with indirect techniques, as direct measurements are neither generally possible nor practical. An electromyography (EMG)-to-force processing technique was developed. Ankle joint moments and, by extension, ankle muscle forces were calculated. The ankle moment obtained by inverse dynamics was calculated for ten normal adults during free speed gait. There was close correlation between inverse dynamics ankle moments and moments determined by the EMG-to-force processing approach. Muscle forces were determined. The gait peak Achilles tendon force occurred in late single limb support. Peak force observed (2.9 kN) closely matched values obtained where force transducers were used to obtain in vivo muscle forces (2.6 kN). The EMG-to-force processing model presented here appears to be a practical means to determine in vivo muscle forces.

Energy transfer mechanisms as a compensatory strategy in below knee amputee runners.

Below knee amputee runners exhibit abnormalities in the mechanical work characteristics of the lower extremity musculature during stance phase. The most significant abnormality is a marked reduction in the mechanical work done in the stance phase prosthetic limb. Energy transfer across the hip joint to the trunk during deceleration of the swing phase leg may be an important energy distribution mechanism to compensate for the reduced work done during prosthetic stance phase. Five unilateral below knee amputee runners wearing the SACH prosthetic foot and 5 normal subjects were studied. All subjects ran at a controlled velocity of 2.8 ms(-1) while kinematic and ground reaction force data were collected. Using a four segment linked segment model and an inverse dynamics approach joint moments, muscle power outputs, mechanical work values and energy transfers across the hip were calculated. The total amount of energy transferred during swing phase and the energy transferred out of the swing phase leg into the trunk were both significantly greater than normal. Energy transfer mechanisms are important in influencing the lower extremity energetics during swing phase. In addition, the 74 percent increase in energy transfer out of the intact swing phase limb combined with the temporal characteristics of this energy flow suggests that energy transfer may be an adaptive mechanism that allows energy redistribution to the trunk which may partially compensate for the reduced power output of the stance phase prosthetic limb.

AAEM minimonograph #16: instrumentation and measurement in electrodiagnostic medicine--Part I.

Technical and instrumentation factors play an important role in obtaining reliable information during electrodiagnostic studies. With contemporary electrodiagnostic equipment, neurophysiologic potentials are detected using a variety of electrodes and undergo differential amplification, filtering, conversion to digital form, and finally, analysis and display. Understanding the signal processing principles, limitations, and sources of errors that can occur during this multistep process can improve the technical quality of studies, minimize preventable errors, and improve clinical interpretation. Part I of this minimonograph reviews the basic principles of action potential generation and overviews electrodiagnostic instrumentation. The concept of waveform frequency content is related to the role of filters in suppressing noise while preserving waveform latency, amplitude, and morphology. The electrical characteristics of various surface and needle electrodes influence instrument design and the nature of the potentials recorded. This is especially important in understanding the differences in motor unit characteristics obtained from monopolar and concentric needle electrodes.

AAEM minimonograph #16: instrumentation and measurement in electrodiagnostic medicine--Part II.

A review of instrumentation and measurement in electrodiagnostic medicine is continued in this Part II which focuses on digital instrumentation principles, gain and sweep effects, noise, nerve stimulation, and conduction measurement limitations. With the adoption of microprocessor-based equipment, the neurophysiologic signal must undergo analog-to-digital conversion (ADC) before analysis and display on a video monitor. ADC resolution and sampling rates affect accuracy and measurement precision. Following waveform display, the visual assessment of latency and duration may be influenced by sweep and gain settings, often overlooked sources of error. Undesired signal or noise typically originates from power-line interference, electronic amplifier noise, background muscle activity, or nerve stimulation artifact. Noise often interferes with clinical studies but techniques exist to reduce noise to acceptable levels in virtually all situations. An awareness and understanding of these technical issues will lead to an appreciation of the limitations of electrodiagnostic testing and improve interpretation and clinical decision-making.

Acoustic myography as a control signal for an externally powered prosthesis.

Contracting skeletal muscle produces sounds that are easily recorded with a standard microphone. The recording of these sounds is known as acoustic myography, or AMG. As a control signal for an externally powered prosthesis, some advantages of AMG over surface EMG are: there is no need for direct skin contact; the AMG signal is unaffected by changes in skin impedence; AMG intensity is high enough to produce a 50 mV output from a standard microphone, requiring less amplification and electrical shielding; the AMG signal is qualitatively less sensitive to placement on the muscle than EMG. Disadvantages, such as the susceptibility of AMG to interference by extraneous environmental noise, are relatively easy to overcome. To demonstrate this, we have constructed a myoacoustically controlled prosthetic hand, whose tristate control via a single microphone (vs differential control) proves its feasibility in the more difficult case. The control circuitry for this device costs less than $50. The existing device utilizes a free-standing hand; a prosthetic shell which will allow comparison of AMG vs EMG control is currently being designed. The two patients who have tried it have learned to open and close the hand reliably after only three minutes of practice. Protocols are being established for functional assessment of AMG control.