Recently, rehabilitation systems for stroke patients have been developed using engineering technology. Because the abnormal movements caused by stroke differ greatly among patients, the rehabilitation effects using these novel systems may not be consistent or stable. Therefore, objective and quantitative evaluation of the motor function before rehabilitation is important for every patient. This study aimed to develop an easy method for clinical use, which detects abnormal gait movements using an inertial sensor. In the motor measurements using the inertial senor, there was a difference between the anatomical coordinate system constructed from the sagittal and frontal planes, and the coordinate system calculated using the inertial sensor for use to express movement, , which affected the measured results. Therefore, in this study, we developed a novel method to detect gait abnormality using vector loci that display thigh movements, which does not require calibration of the coordinate system. Using the proposed indexes, the gait movements of 26 healthy subjects and 7 hemiplegic subjects were analyzed. The results using the proposed indexes demonstrated the feasibility of detecting abnormal movements of hemiplegic subjects, indicating the usefulness of the proposed indexes.
Recently, magnetic particle imaging (MPI) has been proposed as a new medical imaging technology for diagnosing cancer and cardiovascular diseases. When magnetic nanoparticles (MNP) are injected into a living body, the MNP circulate in the blood vessels and accumulate in a cancer cell. In MPI, the MNP distribution is reconstructed by detecting the magnetization signals generated by changing the magnetization state of the MNP using an externally applied alternating magnetic field. However, in this signal detection method, because both the magnetization signals and the alternating magnetic field are simultaneously detected, it is necessary to separate them. Consequently, the same component with the same frequency as the alternating magnetic field is removed, and this component constitutes the majority of the magnetization signals. Hence, the signals of the MNP after separation become small and the signal-to-noise ratio (SNR) deteriorates. Therefore, we propose a magnetization signal detection method based on vibration of the MNP, without using the alternating magnetic field. In this method, because it is not necessary to separate the component with the same frequency as the alternating magnetic field from the magnetization signals, larger signals can be detected compared to that obtained by the conventional method, and the SNR can be improved. In this study, in order to confirm the efficacy of the proposed method, the SNRs of the conventional and the proposed methods were evaluated experimentally. Based on the results obtained, we confirmed that the SNR of the proposed method improved by 11.3dB compared to the conventional method.
We developed a system in which an electrode matrix sheet is placed on the bedding to detect electrically urination of bedridden elderly patients without installation of special sensors in disposable diapers. The sensing device is an electrode matrix sheet consisting of 165 square copper electrodes arranged in 11 rows vertically and 15 horizontally. The system consists of an AC power supply, the electrode matrix sheet, and a resistor of 100 kΩ (for output detection) connected in series. The system can be represented as an AC circuit with a resistor and a capacitor connected in series. The output was the voltage at the resistor. It was possible to depict how simulated urine spread in the disposable diaper as the change in capacitive reactance in the electrode matrix sheet. The output was inversely proportional to the number of electrodes in the area with absorption of the simulated urine. It was also possible to estimate the amount of simulated urine regardless of the direction of spread on the sensing device. We investigated the relationship between the amount of simulated urine and the output voltage of the system using a three-dimensional buttock model. The experimental results showed that detection of urine from outside the disposable diaper was possible if the amount at the first urination was 200mL or more. The results of this experiment, showed that the output tended to increase logarithmically as the amount of simulated urine increased within the range of 200-1,000mL.
The direction of postural sway in response to neck dorsal muscle stimulation can be influenced by change in gaze direction. Likewise, this postural sway is affected by change in direction of auditory stimulation. The degree of influence differs from one subject to another, and the reason for this individual difference remains largely unknown. This study analyzed whether the direction of postural sway induced by gaze change and that induced by auditory stimulation are related to each other, with the aim to obtain insight into the brain region involved in these phenomena. Twenty-three subjects participated in the following two sets of measurements. First, we measured the directions of postural sway when the subjects changed their gaze after they turned their head to either the left or right. Next, we measured the directions of postural sway during auditory stimulation from the left or right. Then we calculated the correlation coefficient between the directions of sway measured in these experiments. The correlation coefficients were significant on the right side. These results imply that a common mechanism, in which the two types of sensory information are integrated, is involved in these phenomena.
Prevention of loss of muscle functions is important for the elderly because loss of muscle functions is accompanied by decreases in quantity and quality of exercise, resulting in lowered capability in activities of daily living and progression of frailty. In general, muscle strength is an index of physical capacity. However, current methods of measuring muscle strength of the lower limb are limited because they require large-scale measuring equipment. Therefore, easy measurement of muscle strength using home-based method is desired. Some research has suggested easy muscle force evaluation methods by correlating muscle strength and walking velocity based on easy methods of muscle strength evaluation such as 10-m walking time and timed up and go test. However, these walking tests are not natural movements in daily life. In addition, these tests require a well trained assessor. The assessor has to provide reproducible and objective results. Considering all of the above-mentioned issues and needs, we propose a highly accurate method for estimating muscle strength in the lower limb measured during walking while wearing sensors in the ankle. From the input data of standard walking waveform obtained from a walking subject, the muscle strength was estimated using multiple linear regression analysis and convolutional neural network (CNN). The mean absolute errors in multiple linear regression analysis and CNN were 17.3% and 9.27%, respectively. Therefore, we confirm that the CNN model provides highly accurate estimation of muscle strength in the lower limb.