Toshiyo Tamura

Discrimination of walking patterns using wavelet-based fractal analysis

In this paper, we attempted to classify the acceleration signals for walking along a corridor and on stairs by using the wavelet-based fractal analysis method. In addition, the wavelet-based fractal analysis method was used to evaluate the gait of elderly subjects and patients with Parkinsons disease. The triaxial acceleration signals were measured close to the center of gravity of the body while the subject walked along a corridor and up and down stairs continuously. Signal measurements were recorded from 10 healthy young subjects and 11 elderly subjects. For comparison, two patients with Parkinsons disease participated in the level walking. The acceleration signal in each direction was decomposed to seven detailed signals at different wavelet scales by using the discrete wavelet transform. The variances of detailed signals at scales 7 to 1 were calculated. The fractal dimension of the acceleration signal was then estimated from the slope of the variance progression. The fractal dimensions were significantly different among the three types of walking for individual subjects (p < 0.01) and showed a high reproducibility. Our results suggest that the fractal dimensions are effective for classifying the walking types. Moreover, the fractal dimensions were significantly higher for the elderly subjects than for the young subjects (p < 0.01). For the patients with Parkinsons disease, the fractal dimensions tended to be higher than those of healthy subjects. These results suggest that the acceleration signals change into a more complex pattern with aging and with Parkinsons disease, and the fractal dimension can be used to evaluate the gait of elderly subjects and patients with Parkinsons disease.

ANALYSIS AND AUTOMATIC IDENTIFICATION OF SLEEP STAGES USING HIGHER ORDER SPECTRA

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.

Classification of waist-acceleration signals in a continuous walking record

We attempted to distinguish walking on level ground from walking on a stairway using waist acceleration signals. A triaxial accelerometer was fixed to the subjects waist and the three acceleration signals were recorded by a portable data logger at a sampling rate of 256 Hz. Twenty healthy male subjects were asked to walk through a corridor and up and down a stairway as a single sequence, without any instruction. The data were analyzed using discrete wavelet transform. Walking patterns were classified in two stages. In the first stage, the times when the walking pattern changed were detected using the low-frequency component of the anteroposterior acceleration (LF(A)) and of the vertical acceleration (LF(V)). In the second stage, the three types of walking patterns were classified by comparing powers of wavelet coefficients in the vertical direction (P(WCV)) and in the anteroposterior direction (RP(WCA)). Changes in walking patterns could be detected by using either LF(A) or LF(V). Walking down stairs could be distinguished from the other types of walking as it gave the largest value in P(WCV), and walking up stairs could be discriminated from level walking using RP(WCA). Level and stairway walking could be classified from continuous records of waist acceleration.

A Wearable Airbag to Prevent Fall Injuries

We have developed a wearable airbag that incorporates a fall-detection system that uses both acceleration and angular velocity signals to trigger inflation of the airbag. The fall-detection algorithm was devised using a thresholding technique with an accelerometer and gyro sensor. Sixteen subjects mimicked falls, and their acceleration waveforms were monitored. Then, we developed a fall-detection algorithm that could detect signals 300 ms before the fall. This signal was used as a trigger to inflate the airbag to a capacity of 2.4 L. Although the proposed system can help to prevent fall-related injuries, further development is needed to miniaturize the inflation system.

Quantitative evaluation of movement using the timed up-and-go test

In this study, the combined use of an accelerometer and rate gyrosensor to identify the activity phases of the timed up-and-go test (TUG-T) was proposed. Measurements during clinical rehabilitation of hemiplegic patients have been attempted using a triaxial accelerometer to measure the activity objectively, which allows a quantitative evaluation. A waist gyrosensor is useful for measuring the postural displacement with high accuracy. By using both the accelerometer and gyrosensor signals, it was possible to detect the activity phases, which were similar to those observed by the therapists. In addition, the walking activity was extracted from the TUG-T, and the RMS value and CV from the acceleration were calculated in every walking cycle. A qualitative difference between the subjects who could walk independently and those requiring supervision was revealed.

Development of real-time image sequence analysis for evaluating posture change and respiratory rate of a subject in bed

An image sequence analysis technique was developed to evaluate posture change and respiratory rate of a subject in bed without any physical contact. Although the image sequence analysis requires many calculations, the system can perform them in real time. The system consisted of a CCD video camera and a PC equipped with a high-speed image processor. To evaluate the system, we tested it on five subjects at a nursing home. The system evaluated 99.4% of the movements of subjects during the total monitoring time (about 61 hours). The waveform was flat when the subject was out of view of the video camera. The system has the possibility of evaluating not only posture changes and respiratory rate. but also sleeping patterns.

Classification of walking pattern using acceleration waveform in elderly people

We attempted to classify walking on level ground from walking on a stairway using a waist acceleration signal. A tri-axial accelerometer was fixed to the subjects waist and the three acceleration signals were recorded by a portable data logger at a sampling rate of 256 Hz. Eleven healthy, elderly subjects were asked to walk through a corridor and up and down a stairway as a single sequence, without any instruction. The data were analyzed using a discrete wavelet transform. Walking patterns were classified using two parameters; one was the ratio between the power of wavelet coefficients which were corresponded to locomotion and total power in the anteroposterior direction (RPA). The other was the ratio between root mean square of wavelet coefficients at the anteroposterior direction and that at the vertical direction (RAV). Walking up stairs could be distinguished by the smallest value in RPA from other walking patterns. Walking down stairs could be discriminated from level walking using RAV. It was possible to classify the walking pattern using acceleration signals in elderly people.

Monitoring behavior in the home using positioning sensors

To support our health, we must recognize our health condition and living patterns precisely. The aim of this study is to describe a way of monitoring human behavior during daily life to improve quality of life. Human behavior reflects the mental and physical health of the subject. In this study, an unconstrained indoor monitoring system was developed. Human behavior and activity were monitored by infrared positioning sensors and magnetic sensors. In addition, the use of furniture and doors was monitored by magnetic sensors. The system, which included several sensors, was installed in one apartment and monitoring of human behavior was carried out during nine weeks. The system operated without any trouble and the data showed that the daily cycle of an individual could be observed without that persons awareness.

E-Healthcare at an Experimental Welfare Techno House in Japan

An automated monitoring system for home health care has been designed for an experimental house in Japan called the Welfare Techno House (WTH). Automated electrocardiogram (ECG) measurements can be taken while in bed, in the bathtub, and on the toilet, without the subject’s awareness, and without using body surface electrodes. In order to evaluate this automated health monitoring system, overnight measurements were performed to monitor health status during the daily lives of both young and elderly subjects.

Fractal dynamics of body motion in patients with Parkinson's disease

In this paper, we assess the complexity (fractal measure) of body motion during walking in patients with Parkinsons disease. The body motion of 11 patients with Parkinsons disease and 10 healthy elderly subjects was recorded using a triaxial accelerometry technique. A triaxial accelerometer was attached to the lumbar region. An assessment of the complexity of body motion was made using a maximum-likelihood-estimator-based fractal analysis method. Our data suggest that the fractal measures of the body motion of patients with Parkinsons disease are higher than those of healthy elderly subjects. These results were statistically different in the X (anteroposterior), Y (lateral) and Z (vertical) directions of body motion between patients with Parkinsons disease and the healthy elderly subjects (p < 0.01 in X and Z directions and p < 0.05 in Y direction). The complexity (fractal measure) of body motion can be useful to assess and monitor the output from the motor system during walking in clinical practice.