In this study, we aimed to realize a simplified method of gait evaluation using a wearable sensor system intended to be used in motor function rehabilitation or daily exercise for healthcare. This paper focused on gait event analysis by using a single gyroscope attached on the instep. Characteristic points such as a turning value or a zero-crossing of the angular velocity waveform measured with the gyroscope were compared with the actual time points of gait events (heel off, toe off, heel contact and foot flat) detected by aluminum electrode sets for normal speed and slow speed walking of three healthy subjects. By using detection rules that could be applied to the offline processing, more than 90% of the gait events were detected within the difference in detection time of 50ms. For the normal speed walking, more than 90% of the gait events were detected within the difference of 42ms by detection rules both in offline and real time processing. These results showed that the characteristic points of the angular velocity waveform corresponded well to the four gait events, allowing to be detected by a single sensor.
The ultrasonic echo method is a simple and useful way of observing cross sections of extremities. However, due to the narrow view through the probe and its incapability to take images behind bones, combining multiple images is necessary in order to capture cross-sectional images of the whole extremity. Therefore, tissue deformation caused by pressing the probe when taking pictures is a problem. In this study, we investigated the conditions under which high-quality images of extremities can be taken, after avoiding the deformation of tissues by using a water bath together with the ultrasonic echo device. As a result, we found that use of a water bath having similar acoustical impedance to that of biological body and keeping the water at about 40 degrees while shooting enabled to obtain high-quality images.
Muscle forces can be predicted by an optimization method. Researchers use various parameters for this method. This means that the optimization method has not been established yet. The present paper examines how these parameters affect correlation coefficients between predicted muscle forces and electromyogram (EMG) to search a better combination of these parameters. The parameters investigated were (1) two moment-arms, (2) three kinds of denominators of objective function (methods to normalize predicted muscle forces), (3) five kinds of physical cross-sectional areas (PCSA) and (4) three kinds of muscle length and muscle fiber length. Three male subjects walked three times. The model we used had three joints and nine muscles of the lower leg in sagittal plane. Electrodes were placed on gluteus maximus, semitendinosus, rectus femoris, vastus medialis, gastrocnemius, soleus and tibial anterior muscle. Results showed that, in the best combination of the investigated parameters, the correlation coefficients between predicted muscle forces and EMG activities at six muscles except rectus femoris were 0.79 or more. In most cases, the coefficients at rectus femoris were 0.2 or less. Otherwise, the coefficients at rectus femoris were high (r=0.47 or 0.55), and the coefficients at hamstring and vasti muscles were lower.